diff --git a/AGENTS.md b/AGENTS.md index 108414ac..7fb11a80 100644 --- a/AGENTS.md +++ b/AGENTS.md @@ -62,10 +62,50 @@ src/ config.rs — YAML configuration loading (serde), nested tool configs io.rs — Shared I/O utilities (gzip-transparent file reading) gtf.rs — GTF annotation file parser (with configurable attribute extraction) + common/ + mod.rs — Re-exports the shared modules + bam_flags.rs — BAM flag constants and aux-tag helpers + bam_stat.rs — bam_stat.py reimplementation, result types + bam_stat_accum.rs — Read-level counter accumulator feeding bam_stat and samtools + cpp_rng.rs — C++ RNG FFI shim for preseq bootstrap reproducibility + preseq.rs — preseq lc_extrap library complexity extrapolation + samtools/ + mod.rs — Re-exports the samtools writers + stats.rs — samtools stats full output (SN + all histogram sections) + flagstat.rs — samtools flagstat-compatible output + idxstats.rs — samtools idxstats-compatible output + dna/ + mod.rs — Re-exports the DNA submodules + depth.rs — Per-contig depth accumulator (delta array, CIGAR walk, + mate-overlap correction, prefix sum) + gc_bias.rs — Picard CollectGcBiasMetrics reimplementation + hs_metrics.rs — Picard CollectHsMetrics reimplementation (targeted mode) + insert_size.rs — Picard CollectInsertSizeMetrics reimplementation + intervals.rs — BED interval parsing and merging for targeted mode + qualimap.rs — Qualimap bamqc accumulation (windows, coverage, composition) + qualimap_output.rs — genome_results.txt, the raw data tables and the HTML report + wgs_metrics.rs — Picard CollectWgsMetrics reimplementation + mosdepth/ + mod.rs — Per-contig summarisation feeding the mosdepth outputs + output.rs — The six mosdepth-compatible writers (bgzf for the BED outputs) + protein/ + mod.rs + coding/ + mod.rs — Coding-region QC from an alignment and an annotation + regions.rs — Coding, UTR, intronic and intergenic interval sets + output.rs — Base assignment and the CollectRnaSeqMetrics writer + sequence/ + mod.rs — FASTA parsing into per-sequence records + stats.rs — Length statistics reproducing seqkit stats -a + defects.rs — Internal stops, non-standard residues, duplicates + output.rs — The seqkit-compatible table and the RustQC report + spectra/ — behind the `proteomics` cargo feature + mod.rs — mzML reading via mzdata + metrics.rs — Per-run and per-level spectrum metrics + output.rs — The run report rna/ - mod.rs — Re-exports all submodules (dupradar, featurecounts, rseqc, bam_flags, cpp_rng, preseq, qualimap) - bam_flags.rs — BAM flag constants - cpp_rng.rs — C++ RNG FFI shim for preseq bootstrap reproducibility + mod.rs — Re-exports the RNA submodules (dupradar, featurecounts, rseqc, qualimap) + and re-exports the shared ones from `common` for compatibility dupradar/ mod.rs — Re-exports counting, dupmatrix, fitting, plots counting.rs — BAM read counting engine (largest module) @@ -75,7 +115,6 @@ src/ featurecounts/ mod.rs — Re-exports output output.rs — featureCounts-format output & biotype counting - preseq.rs — preseq lc_extrap library complexity extrapolation qualimap/ mod.rs — Re-exports all Qualimap modules accumulator.rs — Gene body coverage accumulation logic @@ -88,9 +127,6 @@ src/ mod.rs — Re-exports all RSeQC modules + common helpers accumulators.rs — Shared RSeQC accumulator infrastructure (read dispatch) common.rs — Shared junction/intron extraction, from_genes builders - bam_stat.rs — bam_stat.py reimplementation - flagstat.rs — samtools flagstat-compatible output - idxstats.rs — samtools idxstats-compatible output infer_experiment.rs — infer_experiment.py reimplementation inner_distance.rs — inner_distance.py reimplementation junction_annotation.rs — junction_annotation.py reimplementation @@ -98,7 +134,6 @@ src/ plots.rs — RSeQC plot generation (duplication, junctions, etc.) read_distribution.rs — read_distribution.py reimplementation read_duplication.rs — read_duplication.py reimplementation - stats.rs — samtools stats full output (SN + all histogram sections) tin.rs — TIN (Transcript Integrity Number) analysis tests/ integration_test.rs — 12 integration tests vs R dupRadar reference output @@ -107,13 +142,23 @@ tests/ create_test_data.R — R script to regenerate test data + references ``` -Nested module structure — top-level modules (`cli`, `config`, `io`, `gtf`, `rna`) declared -in `main.rs`, no `lib.rs`. The `rna` module contains sub-modules for each tool group. -Inter-module access uses `crate::` paths (e.g., `use crate::rna::dupradar::counting::GeneCounts;`). +Nested module structure. The library crate root is `src/lib.rs`, which declares +`common`, `config`, `cpu`, `gtf`, `io`, `rna` and `summary`; the binary +(`src/main.rs`) additionally declares `cli`, `citations` and `ui`. +Inter-module access uses `crate::` paths (e.g., `use crate::common::bam_stat_accum::BamStatAccum;`). +Assay-agnostic analyses belong in `common`; put new code under `rna` only if it +needs a gene annotation or a library strand protocol. -The CLI uses a single subcommand: +The CLI has two subcommands: - `rustqc rna ... --gtf [OPTIONS]` +- `rustqc dna ... [OPTIONS]` + +The `dna` subcommand needs no annotation. It runs depth of coverage +(mosdepth-compatible), the samtools-compatible outputs and preseq in one pass, +with one worker per contig. Shared flags keep their `rna` names, short forms +and `RUSTQC_*` environment variables, with one deliberate exception: +`-Q/--mapq` defaults to 0 for `dna`, matching mosdepth, rather than 30. A GTF gene annotation file (`--gtf`) is required. This runs all analyses: dupRadar duplicate rate analysis, featureCounts-compatible gene counting, diff --git a/CHANGELOG.md b/CHANGELOG.md index 2b67cce4..fd51c72b 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,5 +1,37 @@ # RustQC Changelog +## Unreleased + +### Features + +- New `rustqc dna` subcommand for DNA (WGS) quality control: depth of coverage + with mosdepth-compatible outputs, samtools-compatible stats, flagstat and + idxstats, and preseq library complexity, all in a single pass over the + alignment with one worker per contig, plus Picard-compatible + CollectWgsMetrics, CollectInsertSizeMetrics and CollectGcBiasMetrics. + Passing `--targets` switches on targeted mode and Picard-compatible + CollectHsMetrics. Qualimap-compatible `bamqc` output rounds it out, with + `genome_results.txt`, the raw data tables and an HTML summary. Validated + against mosdepth 0.3.14, samtools 1.24, Picard 3.4.0 and Qualimap 2.3. + +- New `rustqc protein sequence` mode: protein FASTA quality control, with + length statistics reproducing `seqkit stats -a` exactly, plus amino acid + composition and defect detection (internal stop codons, non-standard + residues, duplicate sequences and identifiers) that seqkit does not report. +- New `rustqc protein coding` mode: where reads fall relative to coding + sequence, with base assignment matching Picard `CollectRnaSeqMetrics`. +- New `rustqc protein spectra` mode: mass spectrometry run QC from mzML, with + per-level spectrum and peak counts, total ion current, retention time range + and precursor charge distribution. Behind the `proteomics` cargo feature, + which is on by default; `--no-default-features` drops it along with mzdata. + +### Changed + +- Internal: assay-agnostic analyses (BAM flag helpers, read-level statistics, + the samtools stats/flagstat/idxstats writers, preseq) moved from `rna` to a + new `common` module. The old `rustqc::rna::...` paths still resolve through + re-exports, so this is not a breaking change for library users. + ## [Version 0.2.1](https://github.com/seqeralabs/RustQC/releases/tag/v0.2.1) - 2026-04-09 ### Bug fixes diff --git a/Cargo.lock b/Cargo.lock index da20215e..33348d8e 100644 --- a/Cargo.lock +++ b/Cargo.lock @@ -88,6 +88,16 @@ version = "1.5.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "c08606f8c3cbf4ce6ec8e28fb0014a2c086708fe954eaa885384a6165172e7e8" +[[package]] +name = "base64-simd" +version = "0.8.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "339abbe78e73178762e23bea9dfd08e697eb3f3301cd4be981c0f78ba5859195" +dependencies = [ + "outref", + "vsimd", +] + [[package]] name = "bindgen" version = "0.69.5" @@ -133,6 +143,15 @@ version = "2.11.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "843867be96c8daad0d758b57df9392b6d8d271134fce549de6ce169ff98a92af" +[[package]] +name = "block-buffer" +version = "0.12.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "d2f6c7dbe95a6ed67ad9f18e57daf93a2f034c524b99fd2b76d18fdfeb6660aa" +dependencies = [ + "hybrid-array", +] + [[package]] name = "bumpalo" version = "3.20.2" @@ -303,6 +322,12 @@ dependencies = [ "windows-sys 0.59.0", ] +[[package]] +name = "const-oid" +version = "0.10.2" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "a6ef517f0926dd24a1582492c791b6a4818a4d94e789a334894aa15b0d12f55c" + [[package]] name = "core-foundation" version = "0.9.4" @@ -398,6 +423,15 @@ version = "0.8.21" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "d0a5c400df2834b80a4c3327b3aad3a4c4cd4de0629063962b03235697506a28" +[[package]] +name = "crypto-common" +version = "0.2.2" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "ce6e4c961d6cd6c9a86db418387425e8bdeaf05b3c8bc1411e6dca4c252f1453" +dependencies = [ + "hybrid-array", +] + [[package]] name = "curl-sys" version = "0.4.87+curl-8.19.0" @@ -441,6 +475,17 @@ dependencies = [ "syn", ] +[[package]] +name = "digest" +version = "0.11.3" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "f1dd6dbb5841937940781866fa1281a1ff7bd3bf827091440879f9994983d5c2" +dependencies = [ + "block-buffer", + "const-oid", + "crypto-common", +] + [[package]] name = "dirs" version = "6.0.0" @@ -506,6 +551,15 @@ version = "1.0.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "34aa73646ffb006b8f5147f3dc182bd4bcb190227ce861fc4a4844bf8e3cb2c0" +[[package]] +name = "encoding_rs" +version = "0.8.35" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "75030f3c4f45dafd7586dd6780965a8c7e8e285a5ecb86713e63a79c5b2766f3" +dependencies = [ + "cfg-if", +] + [[package]] name = "env_filter" version = "1.0.1" @@ -727,6 +781,12 @@ version = "0.5.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "2304e00983f87ffb38b55b444b5e3b60a884b5d30c0fca7d82fe33449bbe55ea" +[[package]] +name = "hex" +version = "0.4.3" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "7f24254aa9a54b5c858eaee2f5bccdb46aaf0e486a595ed5fd8f86ba55232a70" + [[package]] name = "hts-sys" version = "2.2.0" @@ -744,6 +804,15 @@ dependencies = [ "openssl-sys", ] +[[package]] +name = "hybrid-array" +version = "0.4.14" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "707114b52a152fa7bdb290cd7cd5912d9467273b6d74e21b8d81aca1f8533f6b" +dependencies = [ + "typenum", +] + [[package]] name = "iana-time-zone" version = "0.1.65" @@ -855,6 +924,12 @@ version = "2.3.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "3d3067d79b975e8844ca9eb072e16b31c3c1c36928edf9c6789548c524d0d954" +[[package]] +name = "identity-hash" +version = "0.1.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "dfdd7caa900436d8f13b2346fe10257e0c05c1f1f9e351f4f5d57c03bd5f45da" + [[package]] name = "idna" version = "1.1.0" @@ -1099,6 +1174,102 @@ dependencies = [ "simd-adler32", ] +[[package]] +name = "mzdata" +version = "0.66.5" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "1f5f8f0c09288b90b6885d08b51833f8955a4ae89181ad81b40a4e328de2c92d" +dependencies = [ + "base64-simd", + "bitflags 2.11.0", + "bytemuck", + "chrono", + "encoding_rs", + "flate2", + "hex", + "identity-hash", + "indexmap", + "log", + "memchr", + "mzdata-bindata", + "mzdata-meta", + "mzdata-param", + "mzdata-spectrum", + "mzpeaks", + "num-traits", + "quick-xml", + "regex", + "sha1", + "thiserror 2.0.18", +] + +[[package]] +name = "mzdata-bindata" +version = "0.66.5" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "a3a072f523e2f147a0b3b27186f5a4a08c83e439080a77172308349e3c6f8a13" +dependencies = [ + "base64-simd", + "bytemuck", + "flate2", + "log", + "mzdata-param", + "mzpeaks", + "num-traits", + "thiserror 2.0.18", +] + +[[package]] +name = "mzdata-meta" +version = "0.66.5" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "d1acb7f6c3da9decd189eb41fa3350c0b1082c3a7cc48200fc5d14592f6bd9cb" +dependencies = [ + "bitflags 2.11.0", + "chrono", + "log", + "mzdata-bindata", + "mzdata-param", + "num-traits", + "regex", + "thiserror 2.0.18", +] + +[[package]] +name = "mzdata-param" +version = "0.66.5" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "2acf302af0eac175f5a09a1457f40300a78f36274548656e3dab54a211c21c0c" +dependencies = [ + "log", + "thiserror 2.0.18", +] + +[[package]] +name = "mzdata-spectrum" +version = "0.66.5" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "c069006a6810bb906c0a9e5f33927761fbc493057084f361381cff0143442a7f" +dependencies = [ + "identity-hash", + "log", + "mzdata-bindata", + "mzdata-meta", + "mzdata-param", + "mzpeaks", + "num-traits", + "thiserror 2.0.18", +] + +[[package]] +name = "mzpeaks" +version = "1.0.9" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "543be9eac70437bfc915b3339e6ae4f23dc034922f13eb2535dcc19e7e9e9481" +dependencies = [ + "num-traits", +] + [[package]] name = "newtype_derive" version = "0.1.6" @@ -1173,6 +1344,12 @@ version = "0.2.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "04744f49eae99ab78e0d5c0b603ab218f515ea8cfe5a456d7629ad883a3b6e7d" +[[package]] +name = "outref" +version = "0.5.2" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "1a80800c0488c3a21695ea981a54918fbb37abf04f4d0720c453632255e2ff0e" + [[package]] name = "pathfinder_geometry" version = "0.5.1" @@ -1321,6 +1498,16 @@ version = "1.2.3" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "a1d01941d82fa2ab50be1e79e6714289dd7cde78eba4c074bc5a4374f650dfe0" +[[package]] +name = "quick-xml" +version = "0.41.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "e660451e55124f798a69a5af3f49ccfbefbd41910eefd25caf2393e1f3473ec1" +dependencies = [ + "memchr", + "serde", +] + [[package]] name = "quote" version = "1.0.45" @@ -1491,6 +1678,7 @@ dependencies = [ "indexmap", "indicatif", "log", + "mzdata", "number_prefix", "plotters", "plotters-backend", @@ -1593,6 +1781,17 @@ dependencies = [ "unsafe-libyaml", ] +[[package]] +name = "sha1" +version = "0.11.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "aacc4cc499359472b4abe1bf11d0b12e688af9a805fa5e3016f9a386dc2d0214" +dependencies = [ + "cfg-if", + "cpufeatures", + "digest", +] + [[package]] name = "shlex" version = "1.3.0" @@ -1714,6 +1913,12 @@ version = "0.20.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "17f77d76d837a7830fe1d4f12b7b4ba4192c1888001c7164257e4bc6d21d96b4" +[[package]] +name = "typenum" +version = "1.20.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "b6f5e870be6c3b371b77fe0ee0bafb859fa4964b4404c27de1d380043c4dda20" + [[package]] name = "unicode-ident" version = "1.0.24" @@ -1768,6 +1973,12 @@ version = "0.2.15" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "accd4ea62f7bb7a82fe23066fb0957d48ef677f6eeb8215f372f52e48bb32426" +[[package]] +name = "vsimd" +version = "0.8.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "5c3082ca00d5a5ef149bb8b555a72ae84c9c59f7250f013ac822ac2e49b19c64" + [[package]] name = "walkdir" version = "2.5.0" diff --git a/Cargo.toml b/Cargo.toml index ebf02f0e..1e1ba138 100644 --- a/Cargo.toml +++ b/Cargo.toml @@ -21,7 +21,17 @@ path = "src/lib.rs" name = "rustqc" path = "src/main.rs" +[features] +default = ["proteomics"] +# Mass spectrometry support for `rustqc protein spectra`. Enabled by default; +# disable with `--no-default-features` to drop mzdata and its transitive +# packages, which is worth doing when only the sequencing pipelines are needed. +proteomics = ["dep:mzdata"] + [dependencies] +# mzML reading for the protein spectra mode (optional, see the `proteomics` feature) +mzdata = { version = "0.66", default-features = false, features = ["mzml"], optional = true } + # CLI argument parsing clap = { version = "4", features = ["derive", "env"] } diff --git a/docs/astro.config.mjs b/docs/astro.config.mjs index 4e1c2f42..ca7e6f08 100644 --- a/docs/astro.config.mjs +++ b/docs/astro.config.mjs @@ -72,6 +72,15 @@ export default defineConfig({ { label: "Samtools", slug: "rna/samtools" }, ], }, + { + label: "DNA", + items: [ + { label: "Overview", slug: "dna/overview" }, + { label: "mosdepth", slug: "dna/mosdepth" }, + { label: "Picard metrics", slug: "dna/picard" }, + { label: "Qualimap bamqc", slug: "dna/qualimap" }, + ], + }, { label: "About", items: [ diff --git a/docs/src/content/docs/dna/mosdepth.mdx b/docs/src/content/docs/dna/mosdepth.mdx new file mode 100644 index 00000000..0c4e12fd --- /dev/null +++ b/docs/src/content/docs/dna/mosdepth.mdx @@ -0,0 +1,69 @@ +--- +title: mosdepth +description: Depth of coverage outputs compatible with mosdepth, and the semantics RustQC reproduces. +--- + +import { Aside } from "@astrojs/starlight/components"; + + + +RustQC reproduces mosdepth's outputs exactly. On the project's test alignment +every one of the six files is identical to mosdepth 0.3.14's, including the +1094-line global distribution and the 721-interval per-base BED. + +## Files + +| File | Written when | +| --- | --- | +| `{sample}.mosdepth.summary.txt` | always | +| `{sample}.mosdepth.global.dist.txt` | always | +| `{sample}.per-base.bed.gz` (+ `.csi`) | unless `--skip-per-base` | +| `{sample}.regions.bed.gz` (+ `.csi`) | with `--window-size` | +| `{sample}.mosdepth.region.dist.txt` | with `--window-size` | +| `{sample}.thresholds.bed.gz` (+ `.csi`) | with `--window-size` and thresholds | + +Compressed outputs are bgzf with a CSI companion index, so `tabix` can seek +into them just as it can into mosdepth's own. + +## What counts towards depth + +RustQC applies mosdepth's default filters: + +- records carrying any of `UNMAP`, `SECONDARY`, `QCFAIL` or `DUP` are skipped, + which is mosdepth's `-F 1796`; +- records below `--mapq` are skipped, defaulting to 0; +- `M`, `=` and `X` cover the reference; `D` and `N` advance without covering; + `I`, `S`, `H` and `P` do not advance at all; +- **a base covered by both mates of one pair counts once.** + + + +## Reading the distribution files + +`{sample}.mosdepth.global.dist.txt` holds `chrom`, `depth` and `proportion` +rows in descending depth order, where the proportion is the fraction of that +contig's bases at depth **at or above** the given value, ending at depth 0 with +`1.00`. + +Which depths get a row is worth knowing, because it is not simply "every depth +seen": + +- depths 0 through 300 always get a row, whether or not any base sits at that + exact depth; +- above 300, only depths that actually occur; +- the maximum observed depth gets a row when it falls inside that dense range, + and none when it does not. + +The region distribution follows the same rules but is computed over windows and +their **rounded mean** depth, not over individual bases. diff --git a/docs/src/content/docs/dna/overview.mdx b/docs/src/content/docs/dna/overview.mdx new file mode 100644 index 00000000..31799902 --- /dev/null +++ b/docs/src/content/docs/dna/overview.mdx @@ -0,0 +1,99 @@ +--- +title: DNA QC Overview +description: What the rustqc dna subcommand runs, what it writes, and how it differs from the RNA pipeline. +--- + +import { Aside, FileTree } from "@astrojs/starlight/components"; + +`rustqc dna` runs a DNA (whole-genome or targeted) quality control pipeline in +a single pass over each alignment file. Unlike [`rustqc rna`](/rna/dupradar/), +it needs no gene annotation. + +```bash +rustqc dna sample.bam --reference genome.fasta --outdir results/ +``` + +## What it runs + +| Upstream tool | What RustQC produces | +| --- | --- | +| [mosdepth](/dna/mosdepth/) | depth of coverage, per base, per window and per region | +| [Picard `CollectWgsMetrics`](/dna/picard/) | genome-wide coverage metrics with the exclusion breakdown | +| [Picard `CollectInsertSizeMetrics`](/dna/picard/) | insert size distribution per pair orientation | +| [Picard `CollectGcBiasMetrics`](/dna/picard/) | coverage bias against reference GC content | +| [Picard `CollectHsMetrics`](/dna/picard/) | targeted enrichment metrics, when `--targets` is given | +| [Samtools](/rna/samtools/) | `stats`, `flagstat` and `idxstats` | +| [Preseq](/rna/preseq/) | library complexity extrapolation | + +Every one of them is fed from the same record stream, so the alignment is read +once no matter how many are enabled. + +## Requirements + +The input must be **duplicate-marked, not duplicate-removed**. Duplicate rate +is a headline metric here, and several exclusion fractions are defined against +it. RustQC refuses input with no duplicate flags at all unless you pass +`--skip-dup-check`. + +A reference FASTA is needed for three things: reading CRAM, sizing +`GENOME_TERRITORY` for `CollectWgsMetrics`, and binning reference windows for +`CollectGcBiasMetrics`. Without one, those two analyses are skipped with a +warning and everything else still runs. + + + +## Output tree + + +- results/ + - mosdepth/ + - sample.mosdepth.summary.txt + - sample.mosdepth.global.dist.txt + - sample.mosdepth.region.dist.txt + - sample.per-base.bed.gz + - sample.per-base.bed.gz.csi + - sample.regions.bed.gz + - sample.thresholds.bed.gz + - picard/ + - wgs_metrics/ + - insert_size/ + - gc_bias/ + - hs_metrics/ + - samtools/ + - preseq/ + - rustqc_summary.json + - CITATIONS.md + + +Pass `--flat-output` to write everything directly into the output directory +instead. + +## Targeted mode + +Passing `--targets targets.bed` switches the run into targeted mode and adds +[`CollectHsMetrics`](/dna/picard/#collecthsmetrics). `--baits` defaults to the +same intervals; give it separately when the capture baits differ from the +regions you want reported. + +Intervals are merged on load. Overlapping targets would otherwise inflate the +reported territory and count the same base twice. + +## Memory + +The depth engine holds one array of four bytes per base for each contig being +processed, so the largest contig sets the cost per worker: roughly 1 GB for +GRCh38 chr1. `--max-depth-workers` bounds how many are live at once, defaulting +to a 4 GB budget divided by the largest contig. Raise it if you have the memory +and want more parallelism; lower it on a shared machine. + +## JSON summary + +`--json-summary` writes a machine-readable summary carrying genome length, +covered bases, mean, median and maximum coverage, the percentage of the +reference at or above each requested threshold, and the duplicate rate. The +coverage thresholds are a list rather than a map so that the order you asked +for survives. diff --git a/docs/src/content/docs/dna/picard.mdx b/docs/src/content/docs/dna/picard.mdx new file mode 100644 index 00000000..9cef0c28 --- /dev/null +++ b/docs/src/content/docs/dna/picard.mdx @@ -0,0 +1,118 @@ +--- +title: Picard metrics +description: CollectWgsMetrics, CollectInsertSizeMetrics, CollectGcBiasMetrics and CollectHsMetrics, and exactly which columns RustQC reproduces. +--- + +import { Aside } from "@astrojs/starlight/components"; + + + +RustQC reproduces four Picard collectors, validated against Picard 3.4.0. Three +match byte for byte; the fourth matches on every column that does not require a +Monte Carlo simulation. + +## CollectWgsMetrics + +Written to `picard/wgs_metrics/{sample}.wgs_metrics.txt`. Needs `--reference`, +because `GENOME_TERRITORY` counts the reference's non-N bases and cannot be +taken from the alignment header. + +Every column and all 251 histogram lines match Picard, except +`HET_SNP_SENSITIVITY` and `HET_SNP_Q`, which come from Picard's Monte Carlo +`TheoreticalSensitivity` and are written as `?`. + +### The exclusion breakdown + +This is what separates Picard's coverage from a plain depth count, and reading +it is the point of the tool. Unmapped, secondary and supplementary records +never enter the calculation. Every other record's reference-consuming bases +form the denominator of all the `PCT_EXC_*` columns. Exclusions then apply in a +fixed order: + +1. `PCT_EXC_DUPE`, the whole read, when duplicate-flagged; +2. `PCT_EXC_MAPQ`, the whole read, below the mapping quality floor; +3. `PCT_EXC_UNPAIRED`, the whole read, when unpaired; +4. `PCT_EXC_BASEQ`, per base, below the base quality floor; +5. `PCT_EXC_OVERLAP`, per base, where the mate already covered it; +6. `PCT_EXC_CAPPED`, per base, for depth beyond `--coverage-cap`. + +What survives is the high quality coverage the histogram reports. The figures +reconcile: on the test data, 670989 aligned bases less 201120 duplicate, 4933 +low quality, 217866 overlapping and 105814 capped leaves the 141256 the +histogram holds. + +`SD_COVERAGE` is the sample standard deviation over **every** base of the +territory, uncovered ones included, which is why it can dwarf the mean on a +targeted library. + +## CollectInsertSizeMetrics + +Written to `picard/insert_size/{sample}.insert_size_metrics.txt`. Matches +Picard byte for byte, metrics row and histogram. + +A pair counts when it is paired, neither secondary, supplementary, duplicate +nor unmapped, has a mapped mate, and carries a positive `TLEN`, which is what +counts each pair once. Proper-pair is deliberately not required. + +`MEAN_INSERT_SIZE` and `STANDARD_DEVIATION` are computed over the histogram +trimmed to `--deviations` median absolute deviations either side of the median, +while `MIN` and `MAX` are over the untrimmed set, so a single far outlier moves +the reported maximum but not the mean. + +## CollectGcBiasMetrics + +Written to `picard/gc_bias/{sample}.gc_bias.detail_metrics.txt` and +`.summary_metrics.txt`. Needs `--reference`. Both files match Picard byte for +byte. + +`NORMALIZED_COVERAGE` is the read density in a GC bin relative to the +genome-wide density, so 1.0 means a bin is covered exactly in proportion to how +much of the reference sits at that GC content. `AT_DROPOUT` and `GC_DROPOUT` +accumulate, over the bins where reads are under-represented, how many +percentage points of the reference are being missed. + + + +## CollectHsMetrics + +Written to `picard/hs_metrics/{sample}.hs_metrics.txt`, and only when +`--targets` is given. + +All 58 computable columns match Picard, including `HS_LIBRARY_SIZE`, which +solves the Lander-Waterman equation the same way Picard's estimator does. + +Seven columns are not computed and are written the way Picard writes its own +uncomputable values: + +| Column | Why | +| --- | --- | +| `HET_SNP_SENSITIVITY`, `HET_SNP_Q` | Monte Carlo theoretical sensitivity | +| `HS_PENALTY_10X` … `HS_PENALTY_100X` | derived from the same simulation | +| `FOLD_80_BASE_PENALTY` | derived from the same simulation | +| `AT_DROPOUT`, `GC_DROPOUT` | per-target GC binning, not implemented | + + + +## Reproducibility of the reference outputs + +The fixtures RustQC is validated against are regenerated by +`tests/create_dna_test_data.sh`, which pins the tool versions and forces the +JVM locale to English. A French default locale makes Picard write `3,531312` +where an English one writes `3.531312`, which would make the reference outputs +depend on the machine that produced them. diff --git a/docs/src/content/docs/dna/qualimap.mdx b/docs/src/content/docs/dna/qualimap.mdx new file mode 100644 index 00000000..5d350b15 --- /dev/null +++ b/docs/src/content/docs/dna/qualimap.mdx @@ -0,0 +1,69 @@ +--- +title: Qualimap bamqc +description: The bamqc outputs RustQC produces, how Qualimap's coverage differs from every other tool here, and which figures are not reproduced. +--- + +import { Aside } from "@astrojs/starlight/components"; + + + +RustQC writes `qualimap/genome_results.txt`, the +`raw_data_qualimapReport/` tables and an HTML summary. + + + +## Two figures that surprise people + +**Mean mapping quality reads about 2.4, not about 60.** It is the mean of the +per-window means, and a window with no reads contributes zero. On a targeted or +low-coverage library, most windows are empty, so the figure is closer to the +fraction of the genome covered than to the quality of the alignments. The +per-position histogram, which only counts covered positions, is the one to read +for that. + +**Base composition is reported in reference orientation.** Reverse-strand reads +are reverse-complemented before counting, so the A and T columns are not the +counts of A and T in the sequencer's output. + +## What matches Qualimap and what does not + +`genome_results.txt` matches on every line but four, and three of the raw +tables match byte for byte. The residuals, each with its cause: + +| Figure | Difference | +| --- | --- | +| `mean mapping quality` | fourth decimal; 393 of 397 windows match exactly | +| `std coverageData` | fourth decimal, same cause | +| `homopolymer indels` | differs outright, see below | +| coverage histogram and what derives from it | about five reference positions of 40001 sit one deeper | +| `genome_fraction_coverage` | last two digits of the double, Qualimap accumulates per window | +| `insert_size_histogram` | one extra row: Qualimap trims the largest insert from the plotted table while still counting it in the statistics | + +Qualimap classifies an indel as a homopolymer indel against a reference context +RustQC does not reconstruct. It reports two polyC indels on the test data, and +no rule derived from the read alone produces them, because the deleted bases +are not in the read. RustQC uses a run-of-four rule instead, so this one figure +will differ. + +Qualimap's GC content distribution and duplication rate histogram are not +written at all. The first is computed over a 679-read subsample whose selection +rule is not documented; the second uses a definition that does not match a +read-start-position count. Emitting tables under those names with different +numbers would be worse than leaving them out. + +## The HTML report + +RustQC writes its own summary page rather than a copy of Qualimap's, which +ships a bundle of images, CSS and JavaScript. It carries the same numbers as +`genome_results.txt`; the raw tables remain the machine-readable source. diff --git a/docs/src/content/docs/usage/cli-reference.mdx b/docs/src/content/docs/usage/cli-reference.mdx index deca6b40..3c638844 100644 --- a/docs/src/content/docs/usage/cli-reference.mdx +++ b/docs/src/content/docs/usage/cli-reference.mdx @@ -246,6 +246,70 @@ Preseq runs by default and can be skipped entirely with `--skip-preseq`. --- +## `dna` + +DNA quality control: depth of coverage, Picard metrics, samtools-compatible +outputs and library complexity, in a single pass. Needs no annotation. See the +[DNA overview](/dna/overview/) for what each output contains. + +### Synopsis + +```bash +rustqc dna ... [OPTIONS] +``` + +### Shared options + +`-o/--outdir`, `--sample-name`, `--flat-output`, `-c/--config`, +`-j/--json-summary`, `-t/--threads`, `-p/--paired`, `-q/--quiet`, +`-v/--verbose`, `--skip-dup-check`, `--skip-preseq` and the `--preseq-*` family +all behave exactly as they do for `rna`, with the same short flags and the same +`RUSTQC_*` environment variables. + + + +### DNA-specific options + +| Option | Default | Description | +| ------------------------------- | ---------------------- | -------------------------------------------------------------------- | +| `-r, --reference ` | none | Required for CRAM, `CollectWgsMetrics` and `CollectGcBiasMetrics` | +| `--targets ` | none | Switches on targeted mode and `CollectHsMetrics` | +| `--baits ` | same as `--targets` | Capture bait intervals, when they differ from the targets | +| `--depth-thresholds ` | `1,5,10,15,20,30,50` | Coverage thresholds to report | +| `--window-size ` | none | Fixed-width windows for per-window depth, mosdepth's `--by` | +| `--coverage-cap ` | `250` | Picard `COVERAGE_CAP` | +| `--min-base-quality ` | `20` | Picard `MINIMUM_BASE_QUALITY` | +| `--skip-per-base` | off | Suppress `per-base.bed.gz`, by far the largest output | +| `--skip-gc-bias` | off | Skip `CollectGcBiasMetrics` | +| `--max-depth-workers ` | derived from RAM | Cap on concurrently live per-contig depth arrays | + +### Examples + +```bash +# Whole genome, with the reference so every metric is available +rustqc dna sample.bam --reference genome.fasta --outdir results/ + +# Per-window depth and a custom threshold set +rustqc dna sample.bam -r genome.fasta --window-size 500 --depth-thresholds 1,10,30 + +# Targeted (exome or panel) mode +rustqc dna sample.bam -r genome.fasta --targets exome.bed --outdir results/ + +# Large genome on a shared machine: bound the depth memory explicitly +rustqc dna sample.bam -r genome.fasta --threads 16 --max-depth-workers 2 +``` + + + +--- + ## Exit codes | Code | Meaning | diff --git a/src/citations.rs b/src/citations.rs index 316ed4d1..0fb4c376 100644 --- a/src/citations.rs +++ b/src/citations.rs @@ -3,7 +3,7 @@ //! Writes a Markdown file alongside results documenting which upstream tools //! RustQC replicated in this run, their validated versions, and citation info. -use crate::config::RnaConfig; +use crate::config::{DnaConfig, RnaConfig}; use anyhow::{Context, Result}; use std::io::Write; use std::path::Path; @@ -48,6 +48,14 @@ const PRESEQ: Citation = Citation { doi: "10.1038/nmeth.2375", }; +const MOSDEPTH: Citation = Citation { + heading: "mosdepth (v0.3.14)", + description: "RustQC reimplements the depth of coverage analysis of mosdepth.", + reference: "Pedersen BS, Quinlan AR. Mosdepth: quick coverage calculation for genomes and exomes. *Bioinformatics*. 2018;34(5):867-868.", + url: "https://github.com/brentp/mosdepth", + doi: "10.1093/bioinformatics/btx699", +}; + const SAMTOOLS: Citation = Citation { heading: "Samtools (v1.22.1)", description: "RustQC produces Samtools-compatible flagstat, idxstats, and stats output.", @@ -56,6 +64,19 @@ const SAMTOOLS: Citation = Citation { doi: "10.1093/gigascience/giab008", }; +/// Same tool as [`SAMTOOLS`], different validated version. +/// +/// The `rna` pipeline's outputs were checked against samtools 1.22.1 and the +/// `dna` pipeline's against 1.24, so each cites the version it was actually +/// compared with rather than both claiming the newer one. +const SAMTOOLS_DNA: Citation = Citation { + heading: "Samtools (v1.24)", + description: "RustQC produces Samtools-compatible flagstat, idxstats, and stats output.", + reference: "Danecek P, Bonfield JK, Liddle J, et al. Twelve years of Samtools and BCFtools. *GigaScience*. 2021;10(2):giab008.", + url: "http://www.htslib.org/", + doi: "10.1093/gigascience/giab008", +}; + const QUALIMAP: Citation = Citation { heading: "Qualimap (v2.3)", description: "RustQC produces gene body coverage output compatible with Qualimap rnaseq.", @@ -79,21 +100,7 @@ pub fn write_citations(path: &Path, config: &RnaConfig, version: &str, commit: & .with_context(|| format!("Failed to create citations file: {}", path.display()))?; let mut w = std::io::BufWriter::new(file); - writeln!(w, "# RustQC Citations\n")?; - writeln!( - w, - "This file was generated by [RustQC](https://github.com/seqeralabs/RustQC) v{version} ({commit})." - )?; - writeln!( - w, - "It documents the upstream tools whose behaviour this run replicated." - )?; - writeln!( - w, - "Please cite both RustQC and the relevant upstream tools listed below.\n" - )?; - writeln!(w, "## RustQC (v{version})\n")?; - writeln!(w, "- Repository: \n")?; + write_header(&mut w, version, commit)?; if config.any_dupradar_output() { write_citation(&mut w, &DUPRADAR)?; @@ -118,6 +125,60 @@ pub fn write_citations(path: &Path, config: &RnaConfig, version: &str, commit: & Ok(()) } +/// Write `CITATIONS.md` for a `dna` run. +/// +/// Shares the header and the per-tool blocks with [`write_citations`]; only +/// the set of tools differs, because the two pipelines replicate different +/// upstream programs. +pub fn write_dna_citations( + path: &Path, + config: &DnaConfig, + version: &str, + commit: &str, +) -> Result<()> { + let file = std::fs::File::create(path) + .with_context(|| format!("Failed to create citations file: {}", path.display()))?; + let mut w = std::io::BufWriter::new(file); + + write_header(&mut w, version, commit)?; + + if config.mosdepth.enabled { + write_citation(&mut w, &MOSDEPTH)?; + } + if config.samtools.enabled { + write_citation(&mut w, &SAMTOOLS_DNA)?; + } + if config.qualimap.enabled { + write_citation(&mut w, &QUALIMAP)?; + } + if config.preseq.enabled { + write_citation(&mut w, &PRESEQ)?; + } + + w.flush()?; + Ok(()) +} + +/// Shared preamble of both citation files. +fn write_header(w: &mut W, version: &str, commit: &str) -> Result<()> { + writeln!(w, "# RustQC Citations\n")?; + writeln!( + w, + "This file was generated by [RustQC](https://github.com/seqeralabs/RustQC) v{version} ({commit})." + )?; + writeln!( + w, + "It documents the upstream tools whose behaviour this run replicated." + )?; + writeln!( + w, + "Please cite both RustQC and the relevant upstream tools listed below.\n" + )?; + writeln!(w, "## RustQC (v{version})\n")?; + writeln!(w, "- Repository: \n")?; + Ok(()) +} + #[cfg(test)] mod tests { use super::*; diff --git a/src/cli.rs b/src/cli.rs index 6e6459e8..0a5fcb8a 100644 --- a/src/cli.rs +++ b/src/cli.rs @@ -32,9 +32,634 @@ pub enum Commands { /// analyses in one pass. Requires a GTF annotation and duplicate-marked /// (not removed) alignments. Rna(RnaArgs), + + /// DNA QC — single-pass analysis of BAM/SAM/CRAM files. + /// + /// Runs depth of coverage, samtools stats and library complexity + /// estimation in one pass. Needs no gene annotation. Pass `--targets` + /// to switch to targeted (exome or panel) mode. + Dna(DnaArgs), + + /// Protein QC — sequence, coding-region and mass spectrometry analyses. + /// + /// Three modes taking different inputs entirely, so the mode is chosen + /// explicitly rather than inferred from which flags were given. + Protein(ProteinArgs), +} + +/// Arguments for the `protein` subcommand. +#[derive(Parser, Debug)] +pub struct ProteinArgs { + /// Which protein analysis to run. + #[command(subcommand)] + pub mode: ProteinMode, +} + +/// The `protein` subcommand's modes. +#[derive(Subcommand, Debug)] +pub enum ProteinMode { + /// Protein FASTA QC: length statistics, composition and defects. + Sequence(ProteinSequenceArgs), + + /// Coding-region QC from an alignment and an annotation. + Coding(ProteinCodingArgs), + + /// Mass spectrometry run QC from mzML. + /// + /// Only offered when built with the `proteomics` feature, which is on by + /// default. Without it the mode is absent from the help rather than + /// present and failing. + #[cfg(feature = "proteomics")] + Spectra(ProteinSpectraArgs), +} + +/// Arguments for `protein coding`. +#[derive(Parser, Debug)] +#[command( + next_line_help = false, + term_width = 120, + help_template = "\ +{about-with-newline} +{usage-heading} {usage} + +{all-args}" +)] +pub struct ProteinCodingArgs { + /// Alignment file(s) + #[arg(value_name = "INPUT", num_args = 1.., required = true, help_heading = "Input / Output")] + pub input: Vec, + + /// GTF gene annotation (plain or .gz) + #[arg( + short, + long, + value_name = "GTF", + env = "RUSTQC_GTF", + help_heading = "Input / Output" + )] + pub gtf: String, + + /// Output directory [default: .] + #[arg( + short, + long, + default_value = ".", + hide_default_value = true, + env = "RUSTQC_OUTDIR", + help_heading = "Input / Output" + )] + pub outdir: String, + + /// Override sample name for output filenames (default: derived from filename) + #[arg( + long, + value_name = "NAME", + env = "RUSTQC_SAMPLE_NAME", + help_heading = "Input / Output" + )] + pub sample_name: Option, + + /// Write outputs to a flat directory (no subdirs) + #[arg( + long, + default_value_t = false, + env = "RUSTQC_FLAT_OUTPUT", + help_heading = "Input / Output" + )] + pub flat_output: bool, + + /// Suppress output except warnings/errors + #[arg( + short = 'q', + long, + conflicts_with = "verbose", + env = "RUSTQC_QUIET", + help_heading = "General" + )] + pub quiet: bool, + + /// Show additional detail + #[arg( + short = 'v', + long, + conflicts_with = "quiet", + env = "RUSTQC_VERBOSE", + help_heading = "General" + )] + pub verbose: bool, +} + +/// Arguments for `protein spectra`. +#[cfg(feature = "proteomics")] +#[derive(Parser, Debug)] +#[command( + next_line_help = false, + term_width = 120, + help_template = "\ +{about-with-newline} +{usage-heading} {usage} + +{all-args}" +)] +pub struct ProteinSpectraArgs { + /// mzML file(s), plain or .gz + #[arg(value_name = "MZML", num_args = 1.., required = true, help_heading = "Input / Output")] + pub input: Vec, + + /// Output directory [default: .] + #[arg( + short, + long, + default_value = ".", + hide_default_value = true, + env = "RUSTQC_OUTDIR", + help_heading = "Input / Output" + )] + pub outdir: String, + + /// Override sample name for output filenames (default: derived from filename) + #[arg( + long, + value_name = "NAME", + env = "RUSTQC_SAMPLE_NAME", + help_heading = "Input / Output" + )] + pub sample_name: Option, + + /// Write outputs to a flat directory (no subdirs) + #[arg( + long, + default_value_t = false, + env = "RUSTQC_FLAT_OUTPUT", + help_heading = "Input / Output" + )] + pub flat_output: bool, + + /// YAML configuration file (see also: RUSTQC_CONFIG env var) + #[arg(short, long, value_name = "CONFIG", help_heading = "Input / Output")] + pub config: Option, + + /// JSON summary path (use "-" for stdout) + #[arg(short = 'j', long = "json-summary", value_name = "PATH", num_args = 0..=1, default_missing_value = "", env = "RUSTQC_JSON_SUMMARY", help_heading = "Input / Output")] + pub json_summary: Option, + + /// Suppress output except warnings/errors + #[arg( + short = 'q', + long, + conflicts_with = "verbose", + env = "RUSTQC_QUIET", + help_heading = "General" + )] + pub quiet: bool, + + /// Show additional detail + #[arg( + short = 'v', + long, + conflicts_with = "quiet", + env = "RUSTQC_VERBOSE", + help_heading = "General" + )] + pub verbose: bool, +} + +/// Arguments for `protein sequence`. +#[derive(Parser, Debug)] +#[command( + next_line_help = false, + term_width = 120, + help_template = "\ +{about-with-newline} +{usage-heading} {usage} + +{all-args}" +)] +pub struct ProteinSequenceArgs { + /// Protein FASTA file(s), plain or .gz + #[arg(value_name = "FASTA", num_args = 1.., required = true, help_heading = "Input / Output")] + pub input: Vec, + + /// Output directory [default: .] + #[arg( + short, + long, + default_value = ".", + hide_default_value = true, + env = "RUSTQC_OUTDIR", + help_heading = "Input / Output" + )] + pub outdir: String, + + /// Override sample name for output filenames (default: derived from filename) + #[arg( + long, + value_name = "NAME", + env = "RUSTQC_SAMPLE_NAME", + help_heading = "Input / Output" + )] + pub sample_name: Option, + + /// Write outputs to a flat directory (no subdirs) + #[arg( + long, + default_value_t = false, + env = "RUSTQC_FLAT_OUTPUT", + help_heading = "Input / Output" + )] + pub flat_output: bool, + + /// YAML configuration file (see also: RUSTQC_CONFIG env var) + #[arg(short, long, value_name = "CONFIG", help_heading = "Input / Output")] + pub config: Option, + + /// JSON summary path (use "-" for stdout) + #[arg(short = 'j', long = "json-summary", value_name = "PATH", num_args = 0..=1, default_missing_value = "", env = "RUSTQC_JSON_SUMMARY", help_heading = "Input / Output")] + pub json_summary: Option, + + /// Ignore sequences shorter than this + #[arg( + long = "min-length", + value_name = "N", + default_value_t = 0, + hide_default_value = true, + env = "RUSTQC_MIN_LENGTH", + help_heading = "Tool parameters" + )] + pub min_length: usize, + + /// Report a missing terminal stop codon as a defect + #[arg( + long = "expect-stop", + default_value_t = false, + env = "RUSTQC_EXPECT_STOP", + help_heading = "Tool parameters" + )] + pub expect_stop: bool, + + /// Suppress output except warnings/errors + #[arg( + short = 'q', + long, + conflicts_with = "verbose", + env = "RUSTQC_QUIET", + help_heading = "General" + )] + pub quiet: bool, + + /// Show additional detail + #[arg( + short = 'v', + long, + conflicts_with = "quiet", + env = "RUSTQC_VERBOSE", + help_heading = "General" + )] + pub verbose: bool, +} + +/// Arguments for the `rna` subcommand. +#[derive(Parser, Debug)] +#[command( + next_line_help = false, + term_width = 120, + help_template = "\ +{about-with-newline} +{usage-heading} {usage} + +{all-args}" +)] +pub struct RnaArgs { + // ── Input / Output ────────────────────────────────────────────────── + /// Duplicate-marked alignment file(s) + #[arg(value_name = "INPUT", num_args = 1.., required = true, help_heading = "Input / Output")] + pub input: Vec, + + /// GTF gene annotation (plain or .gz) + #[arg( + short, + long, + value_name = "GTF", + env = "RUSTQC_GTF", + help_heading = "Input / Output" + )] + pub gtf: String, + + /// Reference FASTA (required for CRAM) + #[arg( + short, + long, + value_name = "FASTA", + env = "RUSTQC_REFERENCE", + help_heading = "Input / Output" + )] + pub reference: Option, + + /// Output directory [default: .] + #[arg( + short, + long, + default_value = ".", + hide_default_value = true, + env = "RUSTQC_OUTDIR", + help_heading = "Input / Output" + )] + pub outdir: String, + + /// Override sample name for output filenames (default: derived from BAM filename) + #[arg( + long, + value_name = "NAME", + env = "RUSTQC_SAMPLE_NAME", + help_heading = "Input / Output" + )] + pub sample_name: Option, + + /// Write outputs to a flat directory (no subdirs) + #[arg( + long, + default_value_t = false, + env = "RUSTQC_FLAT_OUTPUT", + help_heading = "Input / Output" + )] + pub flat_output: bool, + + /// YAML configuration file (see also: RUSTQC_CONFIG env var) + #[arg(short, long, value_name = "CONFIG", help_heading = "Input / Output")] + pub config: Option, + + /// JSON summary path (use "-" for stdout) + #[arg(short = 'j', long = "json-summary", value_name = "PATH", num_args = 0..=1, default_missing_value = "", env = "RUSTQC_JSON_SUMMARY", help_heading = "Input / Output")] + pub json_summary: Option, + + // ── Library ───────────────────────────────────────────────────────── + /// Strandedness: unstranded, forward, reverse + #[arg( + short, + long, + value_enum, + env = "RUSTQC_STRANDED", + help_heading = "Library" + )] + pub stranded: Option, + + /// Paired-end reads + #[arg(short, long, env = "RUSTQC_PAIRED", help_heading = "Library")] + pub paired: bool, + + // ── General ───────────────────────────────────────────────────────── + /// Number of threads [default: 1] + #[arg( + short, + long, + default_value_t = 1, + hide_default_value = true, + env = "RUSTQC_THREADS", + help_heading = "General" + )] + pub threads: usize, + + /// MAPQ cutoff for quality filtering [default: 30] + #[arg( + short = 'Q', + long = "mapq", + default_value_t = 30, + hide_default_value = true, + env = "RUSTQC_MAPQ", + help_heading = "General" + )] + pub mapq_cut: u8, + + /// GTF attribute for biotype grouping + #[arg( + long, + value_name = "ATTR", + env = "RUSTQC_BIOTYPE_ATTRIBUTE", + help_heading = "General" + )] + pub biotype_attribute: Option, + + /// Skip duplicate-marking check + #[arg( + long, + default_value_t = false, + env = "RUSTQC_SKIP_DUP_CHECK", + help_heading = "General" + )] + pub skip_dup_check: bool, + + /// Suppress output except warnings/errors + #[arg( + short = 'q', + long, + conflicts_with = "verbose", + env = "RUSTQC_QUIET", + help_heading = "General" + )] + pub quiet: bool, + + /// Show additional detail + #[arg( + short = 'v', + long, + conflicts_with = "quiet", + env = "RUSTQC_VERBOSE", + help_heading = "General" + )] + pub verbose: bool, + + // ── Tool parameters ───────────────────────────────────────────────── + /// infer_experiment: sample size [default: 200000] + #[arg( + long = "infer-experiment-sample-size", + value_name = "N", + env = "RUSTQC_INFER_EXPERIMENT_SAMPLE_SIZE", + help_heading = "Tool parameters" + )] + pub infer_experiment_sample_size: Option, + + /// junction_annotation: min intron size [default: 50] + #[arg( + long = "min-intron", + value_name = "N", + env = "RUSTQC_MIN_INTRON", + help_heading = "Tool parameters" + )] + pub min_intron: Option, + + /// junction_saturation: random seed for reproducible results + #[arg( + long = "junction-saturation-seed", + value_name = "N", + env = "RUSTQC_JUNCTION_SATURATION_SEED", + help_heading = "Tool parameters" + )] + pub junction_saturation_seed: Option, + + /// junction_saturation: min coverage [default: 1] + #[arg( + long = "junction-saturation-min-coverage", + value_name = "N", + env = "RUSTQC_JUNCTION_SATURATION_MIN_COVERAGE", + help_heading = "Tool parameters" + )] + pub junction_saturation_min_coverage: Option, + + /// junction_saturation: start % [default: 5] + #[arg( + long = "junction-saturation-percentile-floor", + value_name = "N", + env = "RUSTQC_JUNCTION_SATURATION_PERCENTILE_FLOOR", + help_heading = "Tool parameters" + )] + pub junction_saturation_percentile_floor: Option, + + /// junction_saturation: end % [default: 100] + #[arg( + long = "junction-saturation-percentile-ceiling", + value_name = "N", + env = "RUSTQC_JUNCTION_SATURATION_PERCENTILE_CEILING", + help_heading = "Tool parameters" + )] + pub junction_saturation_percentile_ceiling: Option, + + /// junction_saturation: step % [default: 5] + #[arg( + long = "junction-saturation-percentile-step", + value_name = "N", + env = "RUSTQC_JUNCTION_SATURATION_PERCENTILE_STEP", + help_heading = "Tool parameters" + )] + pub junction_saturation_percentile_step: Option, + + /// inner_distance: sample size [default: 1000000] + #[arg( + long = "inner-distance-sample-size", + value_name = "N", + env = "RUSTQC_INNER_DISTANCE_SAMPLE_SIZE", + help_heading = "Tool parameters" + )] + pub inner_distance_sample_size: Option, + + /// inner_distance: lower bound [default: -250] + #[arg( + long = "inner-distance-lower-bound", + value_name = "N", + allow_hyphen_values = true, + env = "RUSTQC_INNER_DISTANCE_LOWER_BOUND", + help_heading = "Tool parameters" + )] + pub inner_distance_lower_bound: Option, + + /// inner_distance: upper bound [default: 250] + #[arg( + long = "inner-distance-upper-bound", + value_name = "N", + allow_hyphen_values = true, + env = "RUSTQC_INNER_DISTANCE_UPPER_BOUND", + help_heading = "Tool parameters" + )] + pub inner_distance_upper_bound: Option, + + /// inner_distance: bin width [default: 5] + #[arg( + long = "inner-distance-step", + value_name = "N", + allow_hyphen_values = true, + env = "RUSTQC_INNER_DISTANCE_STEP", + help_heading = "Tool parameters" + )] + pub inner_distance_step: Option, + + /// TIN: random seed for reproducible results + #[arg( + long = "tin-seed", + value_name = "N", + env = "RUSTQC_TIN_SEED", + help_heading = "Tool parameters" + )] + pub tin_seed: Option, + + /// Skip TIN analysis + #[arg( + long, + default_value_t = false, + env = "RUSTQC_SKIP_TIN", + help_heading = "Tool parameters" + )] + pub skip_tin: bool, + + /// Skip read duplication analysis + #[arg( + long, + default_value_t = false, + env = "RUSTQC_SKIP_READ_DUPLICATION", + help_heading = "Tool parameters" + )] + pub skip_read_duplication: bool, + + /// Skip preseq library complexity analysis + #[arg( + long, + default_value_t = false, + env = "RUSTQC_SKIP_PRESEQ", + help_heading = "Tool parameters" + )] + pub skip_preseq: bool, + + /// preseq: random seed for bootstrap CIs + #[arg( + long = "preseq-seed", + value_name = "N", + env = "RUSTQC_PRESEQ_SEED", + help_heading = "Tool parameters" + )] + pub preseq_seed: Option, + + /// preseq: max extrapolation depth + #[arg( + long = "preseq-max-extrap", + value_name = "N", + env = "RUSTQC_PRESEQ_MAX_EXTRAP", + help_heading = "Tool parameters" + )] + pub preseq_max_extrap: Option, + + /// preseq: step size between points + #[arg( + long = "preseq-step-size", + value_name = "N", + env = "RUSTQC_PRESEQ_STEP_SIZE", + help_heading = "Tool parameters" + )] + pub preseq_step_size: Option, + + /// preseq: bootstrap replicates for CIs + #[arg( + long = "preseq-n-bootstraps", + value_name = "N", + env = "RUSTQC_PRESEQ_N_BOOTSTRAPS", + help_heading = "Tool parameters" + )] + pub preseq_n_bootstraps: Option, + + /// preseq: max segment length for PE merging + #[arg( + long = "preseq-seg-len", + value_name = "N", + env = "RUSTQC_PRESEQ_SEG_LEN", + help_heading = "Tool parameters" + )] + pub preseq_seg_len: Option, } -/// Arguments for the `rna` subcommand. +/// Arguments for the `dna` subcommand. +/// +/// Shared options keep the same long name, short flag and `RUSTQC_*` +/// environment variable as their `rna` counterparts, so wrapper scripts and +/// muscle memory carry over between the two pipelines. The differences are +/// deliberate: there is no `--gtf` and no `--stranded`, and `--mapq` defaults +/// to 0 rather than 30 because that is mosdepth's default. #[derive(Parser, Debug)] #[command( next_line_help = false, @@ -45,31 +670,40 @@ pub enum Commands { {all-args}" )] -pub struct RnaArgs { +pub struct DnaArgs { // ── Input / Output ────────────────────────────────────────────────── /// Duplicate-marked alignment file(s) #[arg(value_name = "INPUT", num_args = 1.., required = true, help_heading = "Input / Output")] pub input: Vec, - /// GTF gene annotation (plain or .gz) + /// Reference FASTA (required for CRAM and for GC bias) #[arg( short, long, - value_name = "GTF", - env = "RUSTQC_GTF", + value_name = "FASTA", + env = "RUSTQC_REFERENCE", help_heading = "Input / Output" )] - pub gtf: String, + pub reference: Option, - /// Reference FASTA (required for CRAM) + /// Target intervals BED; switches on targeted (exome or panel) mode #[arg( - short, long, - value_name = "FASTA", - env = "RUSTQC_REFERENCE", + value_name = "BED", + env = "RUSTQC_TARGETS", help_heading = "Input / Output" )] - pub reference: Option, + pub targets: Option, + + /// Capture bait intervals BED [default: same as --targets] + #[arg( + long, + value_name = "BED", + env = "RUSTQC_BAITS", + requires = "targets", + help_heading = "Input / Output" + )] + pub baits: Option, /// Output directory [default: .] #[arg( @@ -109,16 +743,6 @@ pub struct RnaArgs { pub json_summary: Option, // ── Library ───────────────────────────────────────────────────────── - /// Strandedness: unstranded, forward, reverse - #[arg( - short, - long, - value_enum, - env = "RUSTQC_STRANDED", - help_heading = "Library" - )] - pub stranded: Option, - /// Paired-end reads #[arg(short, long, env = "RUSTQC_PAIRED", help_heading = "Library")] pub paired: bool, @@ -135,26 +759,17 @@ pub struct RnaArgs { )] pub threads: usize, - /// MAPQ cutoff for quality filtering [default: 30] + /// MAPQ cutoff; reads below it are ignored [default: 0] #[arg( short = 'Q', long = "mapq", - default_value_t = 30, + default_value_t = 0, hide_default_value = true, env = "RUSTQC_MAPQ", help_heading = "General" )] pub mapq_cut: u8, - /// GTF attribute for biotype grouping - #[arg( - long, - value_name = "ATTR", - env = "RUSTQC_BIOTYPE_ATTRIBUTE", - help_heading = "General" - )] - pub biotype_attribute: Option, - /// Skip duplicate-marking check #[arg( long, @@ -185,134 +800,75 @@ pub struct RnaArgs { pub verbose: bool, // ── Tool parameters ───────────────────────────────────────────────── - /// infer_experiment: sample size [default: 200000] - #[arg( - long = "infer-experiment-sample-size", - value_name = "N", - env = "RUSTQC_INFER_EXPERIMENT_SAMPLE_SIZE", - help_heading = "Tool parameters" - )] - pub infer_experiment_sample_size: Option, - - /// junction_annotation: min intron size [default: 50] - #[arg( - long = "min-intron", - value_name = "N", - env = "RUSTQC_MIN_INTRON", - help_heading = "Tool parameters" - )] - pub min_intron: Option, - - /// junction_saturation: random seed for reproducible results - #[arg( - long = "junction-saturation-seed", - value_name = "N", - env = "RUSTQC_JUNCTION_SATURATION_SEED", - help_heading = "Tool parameters" - )] - pub junction_saturation_seed: Option, - - /// junction_saturation: min coverage [default: 1] - #[arg( - long = "junction-saturation-min-coverage", - value_name = "N", - env = "RUSTQC_JUNCTION_SATURATION_MIN_COVERAGE", - help_heading = "Tool parameters" - )] - pub junction_saturation_min_coverage: Option, - - /// junction_saturation: start % [default: 5] - #[arg( - long = "junction-saturation-percentile-floor", - value_name = "N", - env = "RUSTQC_JUNCTION_SATURATION_PERCENTILE_FLOOR", - help_heading = "Tool parameters" - )] - pub junction_saturation_percentile_floor: Option, - - /// junction_saturation: end % [default: 100] - #[arg( - long = "junction-saturation-percentile-ceiling", - value_name = "N", - env = "RUSTQC_JUNCTION_SATURATION_PERCENTILE_CEILING", - help_heading = "Tool parameters" - )] - pub junction_saturation_percentile_ceiling: Option, - - /// junction_saturation: step % [default: 5] - #[arg( - long = "junction-saturation-percentile-step", - value_name = "N", - env = "RUSTQC_JUNCTION_SATURATION_PERCENTILE_STEP", - help_heading = "Tool parameters" - )] - pub junction_saturation_percentile_step: Option, - - /// inner_distance: sample size [default: 1000000] + /// Coverage thresholds to report [default: 1,5,10,15,20,30,50] #[arg( - long = "inner-distance-sample-size", - value_name = "N", - env = "RUSTQC_INNER_DISTANCE_SAMPLE_SIZE", + long = "depth-thresholds", + value_name = "N,...", + value_delimiter = ',', + default_values_t = vec![1u32, 5, 10, 15, 20, 30, 50], + hide_default_value = true, + env = "RUSTQC_DEPTH_THRESHOLDS", help_heading = "Tool parameters" )] - pub inner_distance_sample_size: Option, + pub depth_thresholds: Vec, - /// inner_distance: lower bound [default: -250] + /// Fixed-width window size for per-window depth #[arg( - long = "inner-distance-lower-bound", + long = "window-size", value_name = "N", - allow_hyphen_values = true, - env = "RUSTQC_INNER_DISTANCE_LOWER_BOUND", + env = "RUSTQC_WINDOW_SIZE", help_heading = "Tool parameters" )] - pub inner_distance_lower_bound: Option, + pub window_size: Option, - /// inner_distance: upper bound [default: 250] + /// Picard COVERAGE_CAP [default: 250] #[arg( - long = "inner-distance-upper-bound", + long = "coverage-cap", value_name = "N", - allow_hyphen_values = true, - env = "RUSTQC_INNER_DISTANCE_UPPER_BOUND", + default_value_t = 250, + hide_default_value = true, + env = "RUSTQC_COVERAGE_CAP", help_heading = "Tool parameters" )] - pub inner_distance_upper_bound: Option, + pub coverage_cap: u32, - /// inner_distance: bin width [default: 5] + /// Picard MINIMUM_BASE_QUALITY [default: 20] #[arg( - long = "inner-distance-step", + long = "min-base-quality", value_name = "N", - allow_hyphen_values = true, - env = "RUSTQC_INNER_DISTANCE_STEP", + default_value_t = 20, + hide_default_value = true, + env = "RUSTQC_MIN_BASE_QUALITY", help_heading = "Tool parameters" )] - pub inner_distance_step: Option, + pub min_base_quality: u8, - /// TIN: random seed for reproducible results + /// Skip the per-base depth output, by far the largest file #[arg( - long = "tin-seed", - value_name = "N", - env = "RUSTQC_TIN_SEED", + long, + default_value_t = false, + env = "RUSTQC_SKIP_PER_BASE", help_heading = "Tool parameters" )] - pub tin_seed: Option, + pub skip_per_base: bool, - /// Skip TIN analysis + /// Skip GC bias metrics #[arg( long, default_value_t = false, - env = "RUSTQC_SKIP_TIN", + env = "RUSTQC_SKIP_GC_BIAS", help_heading = "Tool parameters" )] - pub skip_tin: bool, + pub skip_gc_bias: bool, - /// Skip read duplication analysis + /// Cap on concurrently live per-contig depth arrays [default: derived from RAM] #[arg( - long, - default_value_t = false, - env = "RUSTQC_SKIP_READ_DUPLICATION", + long = "max-depth-workers", + value_name = "N", + env = "RUSTQC_MAX_DEPTH_WORKERS", help_heading = "Tool parameters" )] - pub skip_read_duplication: bool, + pub max_depth_workers: Option, /// Skip preseq library complexity analysis #[arg( @@ -413,7 +969,6 @@ mod tests { assert_eq!(args.min_intron, None); assert_eq!(args.inner_distance_step, None); } - #[allow(unreachable_patterns)] _ => panic!("Expected Rna subcommand"), } } @@ -435,7 +990,6 @@ mod tests { assert_eq!(args.input, vec!["a.bam", "b.bam", "c.bam"]); assert_eq!(args.gtf, "genes.gtf"); } - #[allow(unreachable_patterns)] _ => panic!("Expected Rna subcommand"), } } @@ -470,7 +1024,6 @@ mod tests { assert_eq!(args.reference, Some("genome.fa".to_string())); assert_eq!(args.mapq_cut, 20); } - #[allow(unreachable_patterns)] _ => panic!("Expected Rna subcommand"), } } @@ -512,7 +1065,6 @@ mod tests { assert_eq!(args.inner_distance_upper_bound, Some(500)); assert_eq!(args.inner_distance_step, Some(10)); } - #[allow(unreachable_patterns)] _ => panic!("Expected Rna subcommand"), } } @@ -542,7 +1094,6 @@ mod tests { assert_eq!(args.preseq_n_bootstraps, Some(200)); assert_eq!(args.preseq_seg_len, Some(100_000_000)); } - #[allow(unreachable_patterns)] _ => panic!("Expected Rna subcommand"), } } @@ -568,7 +1119,6 @@ mod tests { assert_eq!(args.tin_seed, Some(2)); assert_eq!(args.junction_saturation_seed, Some(3)); } - #[allow(unreachable_patterns)] _ => panic!("Expected Rna subcommand"), } } @@ -587,8 +1137,173 @@ mod tests { Commands::Rna(args) => { assert!(args.skip_preseq); } - #[allow(unreachable_patterns)] _ => panic!("Expected Rna subcommand"), } } + + #[test] + fn test_dna_default_args() { + let cli = Cli::parse_from(["rustqc", "dna", "test.bam"]); + match cli.command { + Commands::Dna(args) => { + assert_eq!(args.input, vec!["test.bam"]); + assert_eq!(args.outdir, "."); + assert_eq!(args.threads, 1); + assert_eq!(args.mapq_cut, 0); + assert_eq!(args.coverage_cap, 250); + assert_eq!(args.min_base_quality, 20); + assert_eq!(args.depth_thresholds, vec![1, 5, 10, 15, 20, 30, 50]); + assert_eq!(args.window_size, None); + assert!(args.targets.is_none()); + assert!(args.baits.is_none()); + assert!(!args.skip_per_base); + assert!(!args.skip_gc_bias); + } + _ => panic!("Expected Dna subcommand"), + } + } + + #[test] + fn test_dna_no_gtf_required() { + assert!(Cli::try_parse_from(["rustqc", "dna", "test.bam"]).is_ok()); + } + + #[test] + fn test_dna_targeted_args() { + let cli = Cli::parse_from([ + "rustqc", + "dna", + "a.bam", + "b.bam", + "--targets", + "t.bed", + "--baits", + "b.bed", + "--depth-thresholds", + "1,10,100", + "--window-size", + "500", + "--reference", + "genome.fa", + "-Q", + "20", + "--threads", + "4", + ]); + match cli.command { + Commands::Dna(args) => { + assert_eq!(args.input, vec!["a.bam", "b.bam"]); + assert_eq!(args.targets, Some("t.bed".to_string())); + assert_eq!(args.baits, Some("b.bed".to_string())); + assert_eq!(args.depth_thresholds, vec![1, 10, 100]); + assert_eq!(args.window_size, Some(500)); + assert_eq!(args.reference, Some("genome.fa".to_string())); + assert_eq!(args.mapq_cut, 20); + assert_eq!(args.threads, 4); + } + _ => panic!("Expected Dna subcommand"), + } + } + + #[test] + fn test_dna_baits_without_targets_is_rejected() { + let result = Cli::try_parse_from(["rustqc", "dna", "test.bam", "--baits", "b.bed"]); + assert!( + result.is_err(), + "--baits without --targets must be rejected" + ); + } + + #[test] + fn test_protein_sequence_default_args() { + let cli = Cli::parse_from(["rustqc", "protein", "sequence", "proteome.fa"]); + match cli.command { + Commands::Protein(args) => match args.mode { + ProteinMode::Sequence(args) => { + assert_eq!(args.input, vec!["proteome.fa"]); + assert_eq!(args.outdir, "."); + assert_eq!(args.min_length, 0); + assert!(!args.expect_stop); + } + #[allow(unreachable_patterns)] + _ => panic!("Expected the sequence mode"), + }, + _ => panic!("Expected Protein subcommand"), + } + } + + #[test] + fn test_protein_requires_a_mode() { + let result = Cli::try_parse_from(["rustqc", "protein", "proteome.fa"]); + assert!( + result.is_err(), + "the mode is explicit, so a bare file argument must be rejected" + ); + } + + #[test] + fn test_protein_sequence_multiple_inputs_and_flags() { + let cli = Cli::parse_from([ + "rustqc", + "protein", + "sequence", + "a.fa", + "b.fa.gz", + "--min-length", + "50", + "--expect-stop", + "--outdir", + "/tmp/out", + ]); + match cli.command { + Commands::Protein(args) => match args.mode { + ProteinMode::Sequence(args) => { + assert_eq!(args.input, vec!["a.fa", "b.fa.gz"]); + assert_eq!(args.min_length, 50); + assert!(args.expect_stop); + assert_eq!(args.outdir, "/tmp/out"); + } + #[allow(unreachable_patterns)] + _ => panic!("Expected the sequence mode"), + }, + _ => panic!("Expected Protein subcommand"), + } + } + + #[cfg(feature = "proteomics")] + #[test] + fn test_protein_spectra_args() { + let cli = Cli::parse_from([ + "rustqc", "protein", "spectra", "run.mzML", "--outdir", "/tmp/ms", + ]); + match cli.command { + Commands::Protein(args) => match args.mode { + ProteinMode::Spectra(args) => { + assert_eq!(args.input, vec!["run.mzML"]); + assert_eq!(args.outdir, "/tmp/ms"); + } + _ => panic!("Expected the spectra mode"), + }, + _ => panic!("Expected Protein subcommand"), + } + } + + #[test] + fn test_protein_coding_requires_an_annotation() { + assert!( + Cli::try_parse_from(["rustqc", "protein", "coding", "a.bam"]).is_err(), + "--gtf is required" + ); + let cli = Cli::parse_from(["rustqc", "protein", "coding", "a.bam", "--gtf", "g.gtf"]); + match cli.command { + Commands::Protein(args) => match args.mode { + ProteinMode::Coding(args) => { + assert_eq!(args.input, vec!["a.bam"]); + assert_eq!(args.gtf, "g.gtf"); + } + _ => panic!("Expected the coding mode"), + }, + _ => panic!("Expected Protein subcommand"), + } + } } diff --git a/src/rna/bam_flags.rs b/src/common/bam_flags.rs similarity index 100% rename from src/rna/bam_flags.rs rename to src/common/bam_flags.rs diff --git a/src/rna/rseqc/bam_stat.rs b/src/common/bam_stat.rs similarity index 100% rename from src/rna/rseqc/bam_stat.rs rename to src/common/bam_stat.rs diff --git a/src/common/bam_stat_accum.rs b/src/common/bam_stat_accum.rs new file mode 100644 index 00000000..f4f08408 --- /dev/null +++ b/src/common/bam_stat_accum.rs @@ -0,0 +1,1313 @@ +//! Read-level alignment statistics accumulator. +//! +//! [`BamStatAccum`] gathers, in a single pass over the records, every counter +//! consumed by RSeQC `bam_stat` and by the samtools-compatible `stats`, +//! `flagstat` and `idxstats` writers. It needs no annotation and no library +//! protocol, so both the `rna` and `dna` pipelines drive the same struct: each +//! parallel worker owns one, and they are merged before conversion. + +use std::collections::HashMap; + +use rust_htslib::bam; + +use crate::common::bam_flags::*; +use crate::common::bam_stat::{BamStatResult, GcDepthBin}; + +/// Default GC-depth bin size in base pairs (matches upstream samtools default). +const GCD_BIN_SIZE: u64 = 20_000; + +// =================================================================== +// Merge helpers for Vec<[u64; N]> per-cycle arrays +// =================================================================== + +/// Merge two `Vec<[u64; N]>` arrays element-wise, extending target if shorter. +fn merge_vec_arrays(target: &mut Vec<[u64; N]>, source: Vec<[u64; N]>) { + if source.len() > target.len() { + target.resize(source.len(), [0u64; N]); + } + for (i, arr) in source.into_iter().enumerate() { + for j in 0..N { + target[i][j] += arr[j]; + } + } +} + +/// bam_stat accumulator — simple flag/MAPQ counting. +/// +/// Also collects the additional counters needed for samtools-compatible +/// flagstat, idxstats, and stats output. +#[derive(Debug)] +pub struct BamStatAccum { + // --- RSeQC bam_stat fields (original) --- + /// Total BAM records seen (primary + secondary + supplementary + unmapped). + pub total_records: u64, + /// Records with QC-fail flag (0x200). + pub qc_failed: u64, + /// Records with duplicate flag (0x400). + pub duplicates: u64, + /// Secondary alignment records (0x100). RSeQC calls these "non-primary". + pub non_primary: u64, + /// Unmapped reads (0x4). + pub unmapped: u64, + /// Mapped reads with MAPQ < cutoff. + pub non_unique: u64, + /// Mapped reads with MAPQ >= cutoff (uniquely mapped). + pub unique: u64, + /// Among unique reads: read1 in a pair. + pub read_1: u64, + /// Among unique reads: read2 in a pair. + pub read_2: u64, + /// Among unique reads: forward strand. + pub forward: u64, + /// Among unique reads: reverse strand. + pub reverse: u64, + /// Among unique reads: has splice junction (CIGAR N). + pub splice: u64, + /// Among unique reads: no splice junctions. + pub non_splice: u64, + /// Among unique reads: in proper pairs (0x2). + pub proper_pairs: u64, + /// Among proper-paired unique reads: mates on different chromosomes. + pub proper_pair_diff_chrom: u64, + + // --- samtools flagstat additional fields --- + /// Secondary alignments (0x100) — counted independently of QC/dup. + pub secondary: u64, + /// Supplementary alignments (0x800) — counted independently of QC/dup. + pub supplementary: u64, + /// All mapped records (not 0x4), regardless of QC/dup. + pub mapped: u64, + /// Paired reads (0x1), regardless of QC/dup. + pub paired_flagstat: u64, + /// Read1 in pair (0x40), regardless of QC/dup — for flagstat. + pub read1_flagstat: u64, + /// Read2 in pair (0x80), regardless of QC/dup — for flagstat. + pub read2_flagstat: u64, + /// First fragments for samtools stats: primary reads that are not "last fragments". + pub first_fragments: u64, + /// Last fragments for samtools stats: primary reads with 0x80 flag. + pub last_fragments: u64, + /// Properly paired reads (0x1 + 0x2), regardless of QC/dup. + pub properly_paired: u64, + /// Both mates mapped (paired + both !unmapped). + pub both_mapped: u64, + /// Singletons (paired, this mapped, mate unmapped). + pub singletons: u64, + /// Paired, both mapped, different reference. + pub mate_diff_chr: u64, + /// Paired, both mapped, different reference, MAPQ >= 5. + pub mate_diff_chr_mapq5: u64, + + // --- samtools idxstats additional fields --- + /// Per-reference (tid) mapped and unmapped counts. + pub chrom_counts: HashMap, + /// Unmapped reads with no reference (tid < 0). + pub unplaced_unmapped: u64, + + // --- samtools stats SN additional fields --- + /// Sum of query sequence lengths for all primary reads (non-secondary, non-supplementary). + pub total_len: u64, + /// Sum of first fragment (read1 or unpaired) sequence lengths. + pub total_first_fragment_len: u64, + /// Sum of last fragment (read2) sequence lengths. + pub total_last_fragment_len: u64, + /// Sum of query lengths for mapped primary reads. + pub bases_mapped: u64, + /// Sum of M/=/X CIGAR operations for mapped primary reads. + pub bases_mapped_cigar: u64, + /// Sum of query lengths for duplicate-flagged primary reads. + pub bases_duplicated: u64, + /// Maximum query sequence length (among primary reads). + pub max_len: u64, + /// Maximum first-fragment sequence length. + pub max_first_fragment_len: u64, + /// Maximum last-fragment sequence length. + pub max_last_fragment_len: u64, + /// Sum of average per-read base qualities (for average-of-averages). + pub quality_sum: f64, + /// Number of reads contributing to quality_sum (primary, non-QC-fail). + pub quality_count: u64, + /// Sum of NM tag values across mapped primary reads. + pub mismatches: u64, + /// Insert size with orientation: abs_tlen → [total, inward, outward, other]. + /// Only one mate per pair contributes (upstream mate), capped at 8000. + pub is_hist: HashMap, + /// Inward-oriented pairs (FR). + pub inward_pairs: u64, + /// Outward-oriented pairs (RF). + pub outward_pairs: u64, + /// Other orientation pairs (FF, RR). + pub other_orientation: u64, + /// Total primary reads (non-secondary, non-supplementary). + pub primary_count: u64, + /// Primary mapped reads count (non-secondary, non-supplementary, !unmapped). + pub primary_mapped: u64, + /// Primary duplicate reads. + pub primary_duplicates: u64, + /// Primary mapped reads with MAPQ = 0 (matching upstream samtools stats). + pub reads_mq0: u64, + /// Primary non-QC-fail mapped paired reads where mate is also mapped. + pub reads_mapped_and_paired: u64, + + // --- samtools stats histogram/distribution fields --- + /// Read length histogram (all primary reads): length → count. + pub rl_hist: HashMap, + /// First fragment read length histogram: length → count. + pub frl_hist: HashMap, + /// Last fragment read length histogram: length → count. + pub lrl_hist: HashMap, + /// MAPQ histogram: primary, mapped, !qcfail, !dup (quality 0-255). + pub mapq_hist: [u64; 256], + /// Per-cycle quality for first fragments (primary, mapped, !qcfail, !dup). + /// Outer: cycle index. Inner: quality value → count (64 buckets covers Q0-Q63). + pub ffq: Vec<[u64; 64]>, + /// Per-cycle quality for last fragments. + pub lfq: Vec<[u64; 64]>, + /// GC content step-function for first fragments, 200 bins (matching samtools ngc=200). + /// Each bin i stores the number of reads with gc_count * 199 / seq_len <= i. + pub gcf: [u64; 200], + /// GC content step-function for last fragments, 200 bins. + pub gcl: [u64; 200], + /// Per-cycle base composition for first fragments (primary, mapped, !qcfail, !dup). + /// [A, C, G, T, N, Other] per cycle. + pub fbc: Vec<[u64; 6]>, + /// Per-cycle base composition for last fragments. + pub lbc: Vec<[u64; 6]>, + /// Per-cycle base composition (read-oriented) for first fragments. + /// Reverse strand reads contribute in reversed cycle order. + pub fbc_ro: Vec<[u64; 6]>, + /// Per-cycle base composition (read-oriented) for last fragments. + pub lbc_ro: Vec<[u64; 6]>, + /// Per-cycle base composition (reverse-complemented for reverse-strand reads, + /// combined first+last fragments). Used for GCT output. [A, C, G, T] only. + pub gcc_rc: Vec<[u64; 4]>, + /// Total base counters for first fragments: [A, C, G, T, N]. + pub ftc: [u64; 5], + /// Total base counters for last fragments: [A, C, G, T, N]. + pub ltc: [u64; 5], + /// Indel distribution by size: length → [insertions, deletions]. + pub id_hist: HashMap, + /// Indels per cycle: cycle → [ins_fwd, ins_rev, del_fwd, del_rev]. + pub ic: Vec<[u64; 4]>, + /// CRC32 checksum sums: [names, sequences, qualities]. + /// Each is the wrapping u32 sum of per-read CRC32 values. + pub chk: [u32; 3], + /// Coverage distribution: depth → number of reference positions at that depth. + /// Populated from a round buffer pileup during sorted BAM processing. + pub cov_hist: HashMap, + /// Circular buffer for coverage pileup, matching upstream samtools design. + /// `cov_buf[cov_buf_idx]` corresponds to reference position `cov_buf_pos`. + /// The buffer grows dynamically to accommodate `max_read_length * 5`. + cov_buf: Vec, + /// Index into `cov_buf` corresponding to `cov_buf_pos`. + cov_buf_idx: usize, + /// Reference position of the element at `cov_buf[cov_buf_idx]`. + cov_buf_pos: i64, + /// Current chromosome tid for round buffer tracking. + cov_buf_tid: i32, + + // --- GC-depth (GCD section) fields --- + /// Accumulated GC-depth bins (one per `GCD_BIN_SIZE`-bp genomic window). + gcd_bins: Vec, + /// Start position of the current GCD bin. + gcd_pos: i64, + /// Chromosome tid of the current GCD bin. + gcd_tid: i32, +} + +impl Default for BamStatAccum { + fn default() -> Self { + Self { + total_records: 0, + qc_failed: 0, + duplicates: 0, + non_primary: 0, + unmapped: 0, + non_unique: 0, + unique: 0, + read_1: 0, + read_2: 0, + forward: 0, + reverse: 0, + splice: 0, + non_splice: 0, + proper_pairs: 0, + proper_pair_diff_chrom: 0, + secondary: 0, + supplementary: 0, + mapped: 0, + paired_flagstat: 0, + read1_flagstat: 0, + read2_flagstat: 0, + first_fragments: 0, + last_fragments: 0, + properly_paired: 0, + both_mapped: 0, + singletons: 0, + mate_diff_chr: 0, + mate_diff_chr_mapq5: 0, + chrom_counts: HashMap::new(), + unplaced_unmapped: 0, + total_len: 0, + total_first_fragment_len: 0, + total_last_fragment_len: 0, + bases_mapped: 0, + bases_mapped_cigar: 0, + bases_duplicated: 0, + max_len: 0, + max_first_fragment_len: 0, + max_last_fragment_len: 0, + quality_sum: 0.0, + quality_count: 0, + mismatches: 0, + is_hist: HashMap::new(), + inward_pairs: 0, + outward_pairs: 0, + other_orientation: 0, + primary_count: 0, + primary_mapped: 0, + primary_duplicates: 0, + reads_mq0: 0, + reads_mapped_and_paired: 0, + rl_hist: HashMap::new(), + frl_hist: HashMap::new(), + lrl_hist: HashMap::new(), + mapq_hist: [0u64; 256], + ffq: Vec::new(), + lfq: Vec::new(), + gcf: [0u64; 200], + gcl: [0u64; 200], + fbc: Vec::new(), + lbc: Vec::new(), + fbc_ro: Vec::new(), + lbc_ro: Vec::new(), + gcc_rc: Vec::new(), + ftc: [0u64; 5], + ltc: [0u64; 5], + id_hist: HashMap::new(), + ic: Vec::new(), + chk: [0u32; 3], + cov_hist: HashMap::new(), + cov_buf: vec![0u32; 1500], // matches upstream samtools: nbases * 5 = 300 * 5 + cov_buf_idx: 0, + cov_buf_pos: 0, + cov_buf_tid: -1, + gcd_bins: Vec::new(), + gcd_pos: -1, + gcd_tid: -1, + } + } +} + +impl BamStatAccum { + /// Process a single BAM record. Called for EVERY record (before counting filters). + /// + /// Collects counters for: + /// - RSeQC bam_stat (original cascade with early returns) + /// - samtools flagstat (counts all records independently) + /// - samtools idxstats (per-reference mapped/unmapped counts) + /// - samtools stats SN section (sequence lengths, quality, insert size, etc.) + pub fn process_read(&mut self, record: &bam::Record, mapq_cut: u8) { + let flags = record.flags(); + self.total_records += 1; + + let is_secondary = flags & BAM_FSECONDARY != 0; + let is_supplementary = flags & BAM_FSUPPLEMENTARY != 0; + let is_unmapped = flags & BAM_FUNMAP != 0; + let is_paired = flags & BAM_FPAIRED != 0; + let is_dup = flags & BAM_FDUP != 0; + let is_qcfail = flags & BAM_FQCFAIL != 0; + let is_primary = !is_secondary && !is_supplementary; + let is_mapped = !is_unmapped; + let tid = record.tid(); + let mapq = record.mapq(); + + // ================================================================= + // samtools flagstat counters (count ALL records, no early returns) + // ================================================================= + if is_secondary { + self.secondary += 1; + } + if is_supplementary { + self.supplementary += 1; + } + if is_mapped { + self.mapped += 1; + } + // samtools stats: "1st fragments" / "last fragments" count primary reads only + // For paired reads: read2 flag -> last, everything else -> 1st + // For SE reads (no PAIRED flag): all counted as 1st fragments + if is_primary { + if flags & BAM_FREAD2 != 0 { + self.last_fragments += 1; + } else { + self.first_fragments += 1; + } + } + // samtools flagstat: paired-read metrics count PRIMARY reads only + // (secondary/supplementary are excluded from paired/read1/read2/properly-paired counts) + if is_paired && is_primary { + self.paired_flagstat += 1; + if flags & BAM_FREAD1 != 0 { + self.read1_flagstat += 1; + } + if flags & BAM_FREAD2 != 0 { + self.read2_flagstat += 1; + } + if flags & BAM_FPROPER_PAIR != 0 { + self.properly_paired += 1; + } + let mate_unmapped = flags & BAM_FMUNMAP != 0; + if is_mapped && !mate_unmapped { + self.both_mapped += 1; + if tid != record.mtid() { + self.mate_diff_chr += 1; + if mapq >= 5 { + self.mate_diff_chr_mapq5 += 1; + } + } + } + if is_mapped && mate_unmapped { + self.singletons += 1; + } + } + + // ================================================================= + // samtools idxstats counters (per-reference) + // ================================================================= + if is_unmapped { + if tid >= 0 { + // Unmapped read placed on a reference (has tid) + self.chrom_counts.entry(tid).or_insert((0, 0)).1 += 1; + } else { + self.unplaced_unmapped += 1; + } + } else if tid >= 0 { + // Mapped read + self.chrom_counts.entry(tid).or_insert((0, 0)).0 += 1; + } + + // ================================================================= + // CHK checksums: computed on ALL reads (including secondary and + // supplementary). Matches samtools stats.c update_checksum() which + // is called before the secondary-read early return. + // ================================================================= + { + let qname = record.qname(); + let name_crc = crc32fast::hash(qname); + self.chk[0] = self.chk[0].wrapping_add(name_crc); + + let seq_len = record.seq_len(); + if seq_len > 0 { + // SAFETY: We access the raw BAM record data to compute CRC32 + // checksums matching samtools' approach. The pointer arithmetic + // replicates htslib's bam_get_seq() macro: + // data + l_qname + (n_cigar << 2) + // The seq_len > 0 guard above ensures sequence data exists. + // The slice length seq_len.div_ceil(2) matches the BAM spec's + // 4-bit encoded sequence format: (seq_len+1)/2 bytes. + let seq_bytes = unsafe { + let inner = record.inner(); + let data = inner.data; + let seq_offset = + inner.core.l_qname as isize + ((inner.core.n_cigar as isize) << 2); + let seq_nbytes = seq_len.div_ceil(2); + std::slice::from_raw_parts(data.offset(seq_offset), seq_nbytes) + }; + let seq_crc = crc32fast::hash(seq_bytes); + self.chk[1] = self.chk[1].wrapping_add(seq_crc); + + let qual = record.qual(); + let qual_crc = crc32fast::hash(qual); + self.chk[2] = self.chk[2].wrapping_add(qual_crc); + } + } + + // Track gc_count from the primary-read per-cycle loop so the GCD + // section below can reuse it without re-scanning the sequence. + let mut primary_gc_count: u64 = 0; + + // ================================================================= + // samtools stats SN counters (primary reads only) + // ================================================================= + if is_primary { + self.primary_count += 1; + let seq_len = record.seq_len() as u64; + let mate_unmapped = flags & BAM_FMUNMAP != 0; + + self.total_len += seq_len; + let is_last_fragment = is_paired && flags & BAM_FREAD2 != 0; + if is_last_fragment { + self.total_last_fragment_len += seq_len; + if seq_len > self.max_last_fragment_len { + self.max_last_fragment_len = seq_len; + } + } else { + self.total_first_fragment_len += seq_len; + if seq_len > self.max_first_fragment_len { + self.max_first_fragment_len = seq_len; + } + } + if seq_len > self.max_len { + self.max_len = seq_len; + } + + // RL/FRL/LRL: read length histograms (all primary reads) + *self.rl_hist.entry(seq_len).or_insert(0) += 1; + if is_last_fragment { + *self.lrl_hist.entry(seq_len).or_insert(0) += 1; + } else { + *self.frl_hist.entry(seq_len).or_insert(0) += 1; + } + + if is_dup { + self.primary_duplicates += 1; + self.bases_duplicated += seq_len; + } + // "reads mapped and paired" for samtools stats: primary, non-QC-fail, + // mapped, paired, mate also mapped + if is_mapped && is_paired && !is_qcfail && !mate_unmapped { + self.reads_mapped_and_paired += 1; + } + if is_mapped { + self.primary_mapped += 1; + self.bases_mapped += seq_len; + + // samtools stats: reads MQ0 counts primary mapped reads with MAPQ=0 + // (upstream stats.c: MQ0 is counted inside collect_orig_read_stats, + // which is only called for IS_ORIGINAL reads = non-secondary, non-supplementary) + if record.mapq() == 0 { + self.reads_mq0 += 1; + } + + // NOTE: bases_mapped_cigar is now computed in the IC/ID CIGAR + // loop below (for all mapped non-secondary reads) to avoid a + // separate full CIGAR traversal here. + + // NM tag (edit distance) + if let Ok(rust_htslib::bam::record::Aux::U8(nm)) = record.aux(b"NM") { + self.mismatches += u64::from(nm); + } else if let Ok(rust_htslib::bam::record::Aux::U16(nm)) = record.aux(b"NM") { + self.mismatches += u64::from(nm); + } else if let Ok(rust_htslib::bam::record::Aux::U32(nm)) = record.aux(b"NM") { + self.mismatches += u64::from(nm); + } else if let Ok(rust_htslib::bam::record::Aux::I8(nm)) = record.aux(b"NM") { + if nm > 0 { + self.mismatches += nm as u64; + } + } else if let Ok(rust_htslib::bam::record::Aux::I16(nm)) = record.aux(b"NM") { + if nm > 0 { + self.mismatches += nm as u64; + } + } else if let Ok(rust_htslib::bam::record::Aux::I32(nm)) = record.aux(b"NM") { + if nm > 0 { + self.mismatches += nm as u64; + } + } + + // Insert size + orientation for paired primary reads where both + // mates are mapped. Matches samtools stats gate: + // IS_PAIRED_AND_MAPPED && IS_ORIGINAL + // if (isize > 0 || tid == mtid) + // Both mates contribute; samtools divides by 2 at output. + // We do the same in write_insert_size() and the SN section. + if is_paired && !mate_unmapped { + let tid = record.tid(); + let mtid = record.mtid(); + let tlen = record.insert_size(); + let abs_tlen = tlen.unsigned_abs(); + + if abs_tlen > 0 || tid == mtid { + let pos = record.pos(); + let mpos = record.mpos(); + + // Compute orientation (only meaningful for same-chromosome) + let pos_fst = mpos - pos; + let is_fst: i64 = if flags & BAM_FREAD1 != 0 { 1 } else { -1 }; + let is_fwd: i64 = if flags & BAM_FREVERSE != 0 { -1 } else { 1 }; + let is_mfwd: i64 = if flags & BAM_FMREVERSE != 0 { -1 } else { 1 }; + + // orientation_idx: 1=inward, 2=outward, 3=other + let orientation_idx = if is_fwd * is_mfwd > 0 { + self.other_orientation += 1; + 3usize + } else if is_fst * pos_fst > 0 { + if is_fst * is_fwd > 0 { + self.inward_pairs += 1; + 1usize + } else { + self.outward_pairs += 1; + 2usize + } + } else if is_fst * pos_fst < 0 { + if is_fst * is_fwd > 0 { + self.outward_pairs += 1; + 2usize + } else { + self.inward_pairs += 1; + 1usize + } + } else { + self.inward_pairs += 1; + 1usize + }; + + if abs_tlen > 0 { + // Cap at MAX_INSERT_SIZE (8000), matching + // samtools stats which accumulates overflow + // into the cap bucket. + let capped = abs_tlen.min(8000); + let entry = self.is_hist.entry(capped).or_insert([0; 4]); + entry[0] += 1; // total + entry[orientation_idx] += 1; + } + } + } + } + + // Average quality for primary non-QC-fail reads. + // Upstream samtools stats computes per-BASE quality average: + // sum of all individual base qualities / total bases. + // (Not a per-read average of averages.) + if !is_qcfail { + let quals = record.qual(); + if !quals.is_empty() { + let base_qual_sum: f64 = quals.iter().map(|&q| f64::from(q)).sum::(); + self.quality_sum += base_qual_sum; + self.quality_count += quals.len() as u64; + } + } + + // ============================================================= + // MAPQ histogram: primary + mapped + !qcfail + !dup + // (matches samtools stats.c:1239 five-flag exclusion) + // ============================================================= + if is_mapped && !is_qcfail && !is_dup { + self.mapq_hist[mapq as usize] += 1; + } + + // ============================================================= + // Per-cycle quality & base composition histograms: + // FFQ/LFQ, FBC/LBC, GCF/GCL, FTC/LTC, FBC_RO/LBC_RO + // + // Upstream samtools stats includes duplicates, unmapped, and + // qcfail reads in these histograms (collect_orig_read_stats + // has no such checks). Only secondary+supplementary are + // excluded (via IS_ORIGINAL), which is already handled by + // the outer is_primary guard. + // ============================================================= + { + let is_reverse = flags & BAM_FREVERSE != 0; + + let seq = record.seq(); + let quals = record.qual(); + let read_len = seq.len(); + + // Determine which arrays to use (first vs last fragment) + // If paired: read2 = last, read1 = first. If SE: all = first. + let (qual_arr, base_arr, base_ro_arr, gc_arr, tc_arr) = if is_last_fragment { + ( + &mut self.lfq, + &mut self.lbc, + &mut self.lbc_ro, + &mut self.gcl, + &mut self.ltc, + ) + } else { + ( + &mut self.ffq, + &mut self.fbc, + &mut self.fbc_ro, + &mut self.gcf, + &mut self.ftc, + ) + }; + + // Ensure per-cycle arrays are large enough + if read_len > qual_arr.len() { + qual_arr.resize(read_len, [0u64; 64]); + } + if read_len > base_arr.len() { + base_arr.resize(read_len, [0u64; 6]); + } + if read_len > base_ro_arr.len() { + base_ro_arr.resize(read_len, [0u64; 6]); + } + if read_len > self.gcc_rc.len() { + self.gcc_rc.resize(read_len, [0u64; 4]); + } + + let mut gc_count: u64 = 0; + + // Pre-built lookup tables for the per-cycle inner loop, + // avoiding branches and match overhead on every base. + // + // BAM 4-bit encoding: A=1, C=2, G=4, T=8, N=15, others=0,3,5..14 + // BASE_IDX[nibble] → 0=A, 1=C, 2=G, 3=T, 4=N, 5=Other + const BASE_IDX: [u8; 16] = [5, 0, 1, 5, 2, 5, 5, 5, 3, 5, 5, 5, 5, 5, 5, 4]; + // RC_IDX[base_idx] → reverse-complement base_idx (A↔T, C↔G) + // Only meaningful for base_idx 0-3 (ACGT). Index 4/5 not used. + const RC_IDX: [u8; 6] = [3, 2, 1, 0, 4, 5]; // A→T, C→G, G→C, T→A + + // Hoist the is_reverse branch outside the inner loop so the + // compiler can version the loop and potentially auto-vectorize + // each variant independently. + if !is_reverse { + for i in 0..read_len { + let q = quals[i] as usize; + qual_arr[i][q.min(63)] += 1; + + let base_idx = BASE_IDX[seq.encoded_base(i) as usize] as usize; + base_arr[i][base_idx] += 1; + base_ro_arr[i][base_idx] += 1; + if base_idx < 4 { + self.gcc_rc[i][base_idx] += 1; + } + if base_idx == 1 || base_idx == 2 { + gc_count += 1; + } + if base_idx < 5 { + tc_arr[base_idx] += 1; + } + } + } else { + for i in 0..read_len { + let ro_cycle = read_len - 1 - i; + let q = quals[i] as usize; + qual_arr[ro_cycle][q.min(63)] += 1; + + let base_idx = BASE_IDX[seq.encoded_base(i) as usize] as usize; + base_arr[i][base_idx] += 1; + base_ro_arr[ro_cycle][base_idx] += 1; + if base_idx < 4 { + self.gcc_rc[ro_cycle][RC_IDX[base_idx] as usize] += 1; + } + if base_idx == 1 || base_idx == 2 { + gc_count += 1; + } + if base_idx < 5 { + tc_arr[base_idx] += 1; + } + } + } + + // Save gc_count for GCD section below (avoids re-scanning the sequence). + primary_gc_count = gc_count; + + // GC content: cumulative step function with ngc=200 bins. + // Matches samtools stats.c:925-941. For a read with gc_count G/C + // bases out of read_len total, increment bins gc_idx_min..gc_idx_max. + let ngc: usize = 200; + if let (Some(gc_idx_min), Some(gc_idx_max)) = ( + (gc_count as usize * (ngc - 1)).checked_div(read_len), + ((gc_count as usize + 1) * (ngc - 1)).checked_div(read_len), + ) { + let gc_idx_max = gc_idx_max.min(ngc - 1); + for item in gc_arr.iter_mut().take(gc_idx_max).skip(gc_idx_min) { + *item += 1; + } + } + } + } // if is_primary + + // ============================================================= + // Indel distribution (ID) and indels per cycle (IC) from CIGAR. + // + // Upstream samtools stats calls count_indels() AFTER the + // secondary-read early return (line 1206-1210) and the + // IS_UNMAPPED return (line 1255), but OUTSIDE IS_ORIGINAL(). + // This means: all mapped, non-secondary reads are included + // (supplementary, duplicate, qcfail all contribute). + // + // IC uses first-fragment/last-fragment read order (not + // forward/reverse strand) and read-oriented cycle indices, + // matching upstream count_indels(). + // ============================================================= + // ============================================================= + // Combined single-CIGAR-pass block for IC/ID (indel distribution), + // bases_mapped_cigar, and COV (coverage ring-buffer pileup). + // + // Both IC/ID and COV apply to the same read set (mapped, + // non-secondary). Merging them into one CIGAR traversal + // eliminates two redundant record.cigar() calls per read. + // + // IC/ID: Upstream samtools stats calls count_indels() outside + // IS_ORIGINAL() — supplementary/dup/qcfail all contribute. + // IC uses first/last-fragment order and read-oriented cycles. + // + // COV: Circular-buffer pileup; buffer flushed up to read start + // before CIGAR walk; M/=/X blocks inserted as ranges. + // Buffer grown to max_read_len * 5 as needed. + // ============================================================= + if is_mapped && !is_secondary { + use rust_htslib::bam::record::Cigar as C; + let is_reverse = flags & BAM_FREVERSE != 0; + let read_len = record.seq_len(); + let tid = record.tid(); + let pos = record.pos(); // 0-based + + // Upstream order: paired ? (read1?FIRST:0)+(read2?LAST:0) : FIRST + let order: u32 = if is_paired { + (if flags & BAM_FREAD1 != 0 { 1 } else { 0 }) + + (if flags & BAM_FREAD2 != 0 { 2 } else { 0 }) + } else { + 1 // unpaired → FIRST + }; + + // COV buffer setup (must happen before CIGAR walk). + // Skip reads with no sequence (upstream samtools early-return). + let do_cov = read_len > 0; + let buf_size = if do_cov { + // Grow buffer to max_read_len * 5 if needed. + // When growing, linearise the circular data just like + // upstream samtools: copy [idx..old_size] then [0..idx] + // into a fresh buffer, and reset idx to 0. + let need = read_len * 5; + if need > self.cov_buf.len() { + let old_size = self.cov_buf.len(); + let mut new_buf = vec![0u32; need]; + let head = old_size - self.cov_buf_idx; + new_buf[..head].copy_from_slice(&self.cov_buf[self.cov_buf_idx..]); + new_buf[head..head + self.cov_buf_idx] + .copy_from_slice(&self.cov_buf[..self.cov_buf_idx]); + self.cov_buf = new_buf; + self.cov_buf_idx = 0; + } + let bs = self.cov_buf.len(); + // Flush entire buffer on chromosome change + if tid != self.cov_buf_tid { + self.flush_cov_buf_all(); + self.cov_buf_tid = tid; + self.cov_buf_pos = pos; + self.cov_buf_idx = 0; + } + // Flush positions from cov_buf_pos up to read start + self.cov_buf_flush_to(pos, bs); + bs + } else { + 0 + }; + + // Single CIGAR traversal serving IC/ID + bases_mapped_cigar + COV + let cigar = record.cigar(); + let mut icycle: usize = 0; + let mut cigar_mapped: u64 = 0; + let mut ref_pos = pos; + + for op in cigar.iter() { + match op { + C::Ins(n) => { + let ncig = *n as usize; + let len = *n as u64; + cigar_mapped += len; // I counts toward bases_mapped_cigar + + // ID: indel size distribution + let id_entry = self.id_hist.entry(len).or_insert([0; 2]); + id_entry[0] += 1; // insertions + + // IC: indels per cycle (read-oriented index) + let idx = if is_reverse { + read_len.saturating_sub(icycle + ncig) + } else { + icycle + }; + if idx >= self.ic.len() { + self.ic.resize(idx + 1, [0u64; 4]); + } + if order == 1 { + self.ic[idx][0] += 1; // ins_1st + } + if order == 2 { + self.ic[idx][1] += 1; // ins_2nd + } + + icycle += ncig; // I advances query cycle; ref unchanged + // COV: I consumes no reference positions + } + C::Del(n) => { + let len = *n as u64; + // ID: indel size distribution + let id_entry = self.id_hist.entry(len).or_insert([0; 2]); + id_entry[1] += 1; // deletions + + // IC: indels per cycle (read-oriented index) + let idx = if is_reverse { + if icycle == 0 { + // Discard meaningless deletions at cycle 0 + // (upstream: "if (idx<0) continue;") + ref_pos += *n as i64; // still advance ref for COV + continue; + } + read_len.saturating_sub(icycle + 1) + } else { + if icycle == 0 { + ref_pos += *n as i64; + continue; + } + icycle - 1 + }; + if idx >= self.ic.len() { + self.ic.resize(idx + 1, [0u64; 4]); + } + if order == 1 { + self.ic[idx][2] += 1; // del_1st + } + if order == 2 { + self.ic[idx][3] += 1; // del_2nd + } + // D does NOT advance query cycle; does advance ref + ref_pos += *n as i64; + } + C::Match(n) | C::Equal(n) | C::Diff(n) => { + let len = *n as u64; + cigar_mapped += len; // M/=/X count toward bases_mapped_cigar + icycle += *n as usize; + // COV: M/=/X consumes reference positions + if do_cov { + let end = ref_pos + *n as i64; + self.cov_buf_insert(ref_pos, end, buf_size); + ref_pos = end; + } else { + ref_pos += *n as i64; + } + } + C::RefSkip(n) => { + ref_pos += *n as i64; // N advances ref (COV skips it) + } + C::SoftClip(n) => { + icycle += *n as usize; // S advances query cycle + // COV: S consumes no reference positions + } + C::HardClip(_) | C::Pad(_) => {} + } + } + self.bases_mapped_cigar += cigar_mapped; + } // if is_mapped && !is_secondary (IC/ID + COV combined) + + // ============================================================= + // GCD: GC-depth accumulation (no-reference path). + // + // Matches upstream samtools stats without --ref-seq: bins of + // GCD_BIN_SIZE bp, depth incremented for each read, GC fraction + // accumulated from the read's sequence. + // + // Included reads: mapped, non-secondary (same as COV). + // + // NOTE: gc_count_for_gcd is set from the primary-read per-cycle + // loop above (when is_primary is true), or computed here only for + // non-primary mapped reads, avoiding a redundant full sequence scan. + // ============================================================= + if is_mapped && !is_secondary { + let tid = record.tid(); + let pos = record.pos(); + let seq_len = record.seq_len(); + + if seq_len > 0 { + // Start a new bin on: first read, chromosome change, or + // read beyond current bin boundary. + let new_bin = self.gcd_pos < 0 + || tid != self.gcd_tid + || pos - self.gcd_pos > GCD_BIN_SIZE as i64; + + if new_bin { + self.gcd_bins.push(GcDepthBin { gc: 0.0, depth: 0 }); + self.gcd_pos = pos; + self.gcd_tid = tid; + } + + // Increment depth and accumulate GC fraction from read seq. + if let Some(bin) = self.gcd_bins.last_mut() { + bin.depth += 1; + // For primary reads, gc_count was already computed in the + // per-cycle base loop above. For non-primary mapped reads + // (supplementary, etc.) compute it here from the sequence. + let gc_count: u32 = if is_primary { + primary_gc_count as u32 + } else { + let seq = record.seq(); + let mut count: u32 = 0; + for i in 0..seq_len { + let base = seq.encoded_base(i); + if base == 2 || base == 4 { + count += 1; + } + } + count + }; + bin.gc += gc_count as f32 / seq_len as f32; + } + } + } // if is_mapped && !is_secondary (GCD) + + // ================================================================= + // RSeQC bam_stat cascade (original logic, with early returns) + // ================================================================= + + // 1. QC-failed + if is_qcfail { + self.qc_failed += 1; + return; + } + + // 2. Duplicate + if is_dup { + self.duplicates += 1; + return; + } + + // 3. Secondary (non-primary) — NOT supplementary + if is_secondary { + self.non_primary += 1; + return; + } + + // 4. Unmapped + if is_unmapped { + self.unmapped += 1; + return; + } + + // 5. MAPQ classification + if mapq < mapq_cut { + self.non_unique += 1; + return; + } + + // Uniquely mapped + self.unique += 1; + + if flags & BAM_FREAD1 != 0 { + self.read_1 += 1; + } + if flags & BAM_FREAD2 != 0 { + self.read_2 += 1; + } + if flags & BAM_FREVERSE != 0 { + self.reverse += 1; + } else { + self.forward += 1; + } + + // Splice detection: CIGAR N operation + let has_splice = record + .cigar() + .iter() + .any(|op| matches!(op, rust_htslib::bam::record::Cigar::RefSkip(_))); + if has_splice { + self.splice += 1; + } else { + self.non_splice += 1; + } + + // Proper pair analysis + if is_paired && flags & BAM_FPROPER_PAIR != 0 { + self.proper_pairs += 1; + if tid != record.mtid() { + self.proper_pair_diff_chrom += 1; + } + } + } + + /// Flush all remaining positions in the coverage round buffer into cov_hist. + /// Must be called after processing all reads (or when switching chromosomes). + /// Flush the circular buffer from `cov_buf_pos` up to (but not including) `pos`. + /// Each slot's depth is recorded in `cov_hist` and the slot is zeroed. + /// Matches upstream `round_buffer_flush` logic from samtools stats.c. + fn cov_buf_flush_to(&mut self, pos: i64, buf_size: usize) { + if pos - self.cov_buf_pos >= buf_size as i64 { + // Gap exceeds buffer size. Match upstream samtools exactly: + // flush `size - 1` positions (from cov_buf_pos to + // cov_buf_pos + size - 2), leaving the LAST slot untouched. + // Then advance idx by `size - 1` and jump pos. + // + // Upstream (stats.c round_buffer_flush lines 334-366): + // pos = rbuf.pos + size - 1; // cap at last slot + // ito = lidx2ridx(start, size, rbuf.pos, pos-1); + // // flush from start to ito (size-1 slots) + // rbuf.start = lidx2ridx(start, size, rbuf.pos, pos); + // rbuf.pos = new_pos; + let flush_count = buf_size - 1; // flush all but the last slot + for _ in 0..flush_count { + let depth = self.cov_buf[self.cov_buf_idx]; + if depth > 0 { + *self.cov_hist.entry(depth).or_insert(0) += 1; + self.cov_buf[self.cov_buf_idx] = 0; + } + self.cov_buf_idx += 1; + if self.cov_buf_idx >= buf_size { + self.cov_buf_idx = 0; + } + } + // idx now points to the ONE unflushed slot (the last position + // in the old window). Jump pos to the new read position. + self.cov_buf_pos = pos; + } else { + // Normal case: flush slot by slot. + while self.cov_buf_pos < pos { + let depth = self.cov_buf[self.cov_buf_idx]; + if depth > 0 { + *self.cov_hist.entry(depth).or_insert(0) += 1; + self.cov_buf[self.cov_buf_idx] = 0; + } + self.cov_buf_idx += 1; + if self.cov_buf_idx >= buf_size { + self.cov_buf_idx = 0; + } + self.cov_buf_pos += 1; + } + } + } + + /// Insert a contiguous reference range `[from, to)` into the circular buffer, + /// incrementing depth for each position. The range must fit within `buf_size`. + fn cov_buf_insert(&mut self, from: i64, to: i64, buf_size: usize) { + for ref_pos in from..to { + // Map ref_pos to buffer index: offset from cov_buf_idx by (ref_pos - cov_buf_pos) + let offset = (ref_pos - self.cov_buf_pos) as usize; + let idx = (self.cov_buf_idx + offset) % buf_size; + self.cov_buf[idx] += 1; + } + } + + /// Flush the entire circular buffer and reset tracking state. + pub fn flush_cov_buf_all(&mut self) { + for slot in self.cov_buf.iter_mut() { + if *slot > 0 { + *self.cov_hist.entry(*slot).or_insert(0) += 1; + *slot = 0; + } + } + self.cov_buf_idx = 0; + self.cov_buf_pos = 0; + self.cov_buf_tid = -1; + } + + /// Merge another accumulator into this one. + pub fn merge(&mut self, mut other: BamStatAccum) { + // Flush any remaining positions in the other's round buffer into its + // cov_hist before merging. Without this, positions still in the + // round buffer would be silently lost during parallel merges. + other.flush_cov_buf_all(); + + // RSeQC bam_stat fields + self.total_records += other.total_records; + self.qc_failed += other.qc_failed; + self.duplicates += other.duplicates; + self.non_primary += other.non_primary; + self.unmapped += other.unmapped; + self.non_unique += other.non_unique; + self.unique += other.unique; + self.read_1 += other.read_1; + self.read_2 += other.read_2; + self.forward += other.forward; + self.reverse += other.reverse; + self.splice += other.splice; + self.non_splice += other.non_splice; + self.proper_pairs += other.proper_pairs; + self.proper_pair_diff_chrom += other.proper_pair_diff_chrom; + + // samtools flagstat fields + self.secondary += other.secondary; + self.supplementary += other.supplementary; + self.mapped += other.mapped; + self.paired_flagstat += other.paired_flagstat; + self.read1_flagstat += other.read1_flagstat; + self.read2_flagstat += other.read2_flagstat; + self.first_fragments += other.first_fragments; + self.last_fragments += other.last_fragments; + self.properly_paired += other.properly_paired; + self.both_mapped += other.both_mapped; + self.singletons += other.singletons; + self.mate_diff_chr += other.mate_diff_chr; + self.mate_diff_chr_mapq5 += other.mate_diff_chr_mapq5; + + // samtools idxstats fields + for (tid, (m, u)) in other.chrom_counts { + let entry = self.chrom_counts.entry(tid).or_insert((0, 0)); + entry.0 += m; + entry.1 += u; + } + self.unplaced_unmapped += other.unplaced_unmapped; + + // samtools stats SN fields + self.total_len += other.total_len; + self.total_first_fragment_len += other.total_first_fragment_len; + self.total_last_fragment_len += other.total_last_fragment_len; + self.bases_mapped += other.bases_mapped; + self.bases_mapped_cigar += other.bases_mapped_cigar; + self.bases_duplicated += other.bases_duplicated; + if other.max_len > self.max_len { + self.max_len = other.max_len; + } + if other.max_first_fragment_len > self.max_first_fragment_len { + self.max_first_fragment_len = other.max_first_fragment_len; + } + if other.max_last_fragment_len > self.max_last_fragment_len { + self.max_last_fragment_len = other.max_last_fragment_len; + } + self.quality_sum += other.quality_sum; + self.quality_count += other.quality_count; + self.mismatches += other.mismatches; + for (isize_val, counts) in other.is_hist { + let entry = self.is_hist.entry(isize_val).or_insert([0; 4]); + for i in 0..4 { + entry[i] += counts[i]; + } + } + self.inward_pairs += other.inward_pairs; + self.outward_pairs += other.outward_pairs; + self.other_orientation += other.other_orientation; + self.primary_count += other.primary_count; + self.primary_mapped += other.primary_mapped; + self.primary_duplicates += other.primary_duplicates; + self.reads_mq0 += other.reads_mq0; + self.reads_mapped_and_paired += other.reads_mapped_and_paired; + + // Histogram/distribution fields + for (len, count) in other.rl_hist { + *self.rl_hist.entry(len).or_insert(0) += count; + } + for (len, count) in other.frl_hist { + *self.frl_hist.entry(len).or_insert(0) += count; + } + for (len, count) in other.lrl_hist { + *self.lrl_hist.entry(len).or_insert(0) += count; + } + for i in 0..256 { + self.mapq_hist[i] += other.mapq_hist[i]; + } + + // Per-cycle quality arrays (FFQ/LFQ) + merge_vec_arrays(&mut self.ffq, other.ffq); + merge_vec_arrays(&mut self.lfq, other.lfq); + + // GC content distributions (200 bins) + for i in 0..200 { + self.gcf[i] += other.gcf[i]; + self.gcl[i] += other.gcl[i]; + } + + // Per-cycle base composition (FBC/LBC and read-oriented) + merge_vec_arrays(&mut self.fbc, other.fbc); + merge_vec_arrays(&mut self.lbc, other.lbc); + merge_vec_arrays(&mut self.fbc_ro, other.fbc_ro); + merge_vec_arrays(&mut self.lbc_ro, other.lbc_ro); + merge_vec_arrays(&mut self.gcc_rc, other.gcc_rc); + + // Total base counters + for i in 0..5 { + self.ftc[i] += other.ftc[i]; + self.ltc[i] += other.ltc[i]; + } + + // Indel distribution + for (len, counts) in other.id_hist { + let entry = self.id_hist.entry(len).or_insert([0; 2]); + entry[0] += counts[0]; + entry[1] += counts[1]; + } + + // Indels per cycle + merge_vec_arrays(&mut self.ic, other.ic); + + // CHK checksums (wrapping u32 addition) + for i in 0..3 { + self.chk[i] = self.chk[i].wrapping_add(other.chk[i]); + } + + // COV histogram (additive merge) + for (depth, count) in other.cov_hist { + *self.cov_hist.entry(depth).or_insert(0) += count; + } + + // GCD bins (concatenate — bins from different chromosome workers + // are independent and will be sorted during output). + self.gcd_bins.append(&mut other.gcd_bins); + } +} + +impl BamStatAccum { + /// Convert accumulated counters into a `BamStatResult` for output. + pub fn into_result(mut self) -> BamStatResult { + // Flush remaining positions in the coverage round buffer + self.flush_cov_buf_all(); + BamStatResult { + // RSeQC bam_stat fields + total_records: self.total_records, + qc_failed: self.qc_failed, + duplicates: self.duplicates, + non_primary: self.non_primary, + unmapped: self.unmapped, + non_unique: self.non_unique, + unique: self.unique, + read_1: self.read_1, + read_2: self.read_2, + forward: self.forward, + reverse: self.reverse, + splice: self.splice, + non_splice: self.non_splice, + proper_pairs: self.proper_pairs, + proper_pair_diff_chrom: self.proper_pair_diff_chrom, + // samtools flagstat fields + secondary: self.secondary, + supplementary: self.supplementary, + mapped: self.mapped, + paired_flagstat: self.paired_flagstat, + read1_flagstat: self.read1_flagstat, + read2_flagstat: self.read2_flagstat, + first_fragments: self.first_fragments, + last_fragments: self.last_fragments, + properly_paired: self.properly_paired, + both_mapped: self.both_mapped, + singletons: self.singletons, + mate_diff_chr: self.mate_diff_chr, + mate_diff_chr_mapq5: self.mate_diff_chr_mapq5, + // samtools idxstats fields + chrom_counts: self.chrom_counts, + unplaced_unmapped: self.unplaced_unmapped, + // samtools stats SN fields + total_len: self.total_len, + total_first_fragment_len: self.total_first_fragment_len, + total_last_fragment_len: self.total_last_fragment_len, + bases_mapped: self.bases_mapped, + bases_mapped_cigar: self.bases_mapped_cigar, + bases_duplicated: self.bases_duplicated, + max_len: self.max_len, + max_first_fragment_len: self.max_first_fragment_len, + max_last_fragment_len: self.max_last_fragment_len, + quality_sum: self.quality_sum, + quality_count: self.quality_count, + mismatches: self.mismatches, + is_hist: self.is_hist, + inward_pairs: self.inward_pairs, + outward_pairs: self.outward_pairs, + other_orientation: self.other_orientation, + primary_count: self.primary_count, + primary_mapped: self.primary_mapped, + primary_duplicates: self.primary_duplicates, + reads_mq0: self.reads_mq0, + reads_mapped_and_paired: self.reads_mapped_and_paired, + // Histogram/distribution fields + rl_hist: self.rl_hist, + frl_hist: self.frl_hist, + lrl_hist: self.lrl_hist, + mapq_hist: self.mapq_hist, + ffq: self.ffq, + lfq: self.lfq, + gcf: self.gcf, + gcl: self.gcl, + fbc: self.fbc, + lbc: self.lbc, + fbc_ro: self.fbc_ro, + lbc_ro: self.lbc_ro, + gcc_rc: self.gcc_rc, + ftc: self.ftc, + ltc: self.ltc, + id_hist: self.id_hist, + ic: self.ic, + chk: self.chk, + cov_hist: self.cov_hist, + gcd_bins: self.gcd_bins, + } + } +} diff --git a/src/rna/cpp_rng.rs b/src/common/cpp_rng.rs similarity index 100% rename from src/rna/cpp_rng.rs rename to src/common/cpp_rng.rs diff --git a/src/common/mod.rs b/src/common/mod.rs new file mode 100644 index 00000000..31c3a7d3 --- /dev/null +++ b/src/common/mod.rs @@ -0,0 +1,13 @@ +//! Analysis modules shared between the `rna` and `dna` pipelines. +//! +//! Nothing in this module is specific to a library preparation or an assay: +//! BAM flag helpers, the C++ RNG shim used for preseq bootstrap +//! reproducibility, the preseq `lc_extrap` implementation, read-level +//! alignment statistics, and the samtools-compatible output writers. + +pub mod bam_flags; +pub mod bam_stat; +pub mod bam_stat_accum; +pub mod cpp_rng; +pub mod preseq; +pub mod samtools; diff --git a/src/rna/preseq.rs b/src/common/preseq.rs similarity index 100% rename from src/rna/preseq.rs rename to src/common/preseq.rs diff --git a/src/rna/rseqc/flagstat.rs b/src/common/samtools/flagstat.rs similarity index 99% rename from src/rna/rseqc/flagstat.rs rename to src/common/samtools/flagstat.rs index cb1e370d..b611ae03 100644 --- a/src/rna/rseqc/flagstat.rs +++ b/src/common/samtools/flagstat.rs @@ -8,7 +8,7 @@ use std::path::Path; use anyhow::{Context, Result}; use log::debug; -use super::bam_stat::BamStatResult; +use crate::common::bam_stat::BamStatResult; // ============================================================================ // Output formatting diff --git a/src/rna/rseqc/idxstats.rs b/src/common/samtools/idxstats.rs similarity index 98% rename from src/rna/rseqc/idxstats.rs rename to src/common/samtools/idxstats.rs index 93c018fe..61f05d4f 100644 --- a/src/rna/rseqc/idxstats.rs +++ b/src/common/samtools/idxstats.rs @@ -8,7 +8,7 @@ use std::path::Path; use anyhow::{Context, Result}; use log::debug; -use super::bam_stat::BamStatResult; +use crate::common::bam_stat::BamStatResult; // ============================================================================ // Output formatting diff --git a/src/common/samtools/mod.rs b/src/common/samtools/mod.rs new file mode 100644 index 00000000..f9b1d860 --- /dev/null +++ b/src/common/samtools/mod.rs @@ -0,0 +1,10 @@ +//! samtools-compatible output writers. +//! +//! Reproduce the exact output formats of `samtools stats`, `samtools flagstat` +//! and `samtools idxstats` from the counters gathered in +//! [`crate::common::bam_stat::BamStatResult`], so that MultiQC and +//! `plot-bamstats` parse RustQC output as if samtools had produced it. + +pub mod flagstat; +pub mod idxstats; +pub mod stats; diff --git a/src/rna/rseqc/stats.rs b/src/common/samtools/stats.rs similarity index 99% rename from src/rna/rseqc/stats.rs rename to src/common/samtools/stats.rs index 20abf8c0..32f42067 100644 --- a/src/rna/rseqc/stats.rs +++ b/src/common/samtools/stats.rs @@ -10,7 +10,7 @@ use std::path::Path; use anyhow::{Context, Result}; use log::debug; -use super::bam_stat::{BamStatResult, GcDepthBin}; +use crate::common::bam_stat::{BamStatResult, GcDepthBin}; // ============================================================================ // Output formatting @@ -62,7 +62,7 @@ pub fn write_stats(result: &BamStatResult, output_path: &Path) -> Result<()> { writeln!(out, "# This file was produced by samtools stats and RustQC")?; writeln!( out, - "# The command line was: rustqc rna (samtools stats compatible output)" + "# The command line was: rustqc (samtools stats compatible output)" )?; // Derived values diff --git a/src/config.rs b/src/config.rs index 4952a1fd..9d7a1600 100644 --- a/src/config.rs +++ b/src/config.rs @@ -30,6 +30,78 @@ pub struct Config { /// RNA-Seq QC configuration (matches the `rna` subcommand). #[serde(default)] pub rna: RnaConfig, + + /// DNA QC configuration (matches the `dna` subcommand). + #[serde(default)] + pub dna: DnaConfig, + + /// Protein QC configuration (matches the `protein` subcommand). + #[serde(default)] + pub protein: ProteinConfig, +} + +// =================================================================== +// Protein QC configuration +// =================================================================== + +/// Protein QC configuration. +/// +/// Settings are nested under the mode they belong to, since the modes share +/// nothing but the output directory. +/// +/// Example: +/// ```yaml +/// protein: +/// flat_output: true +/// sequence: +/// min_length: 50 +/// expect_stop: true +/// ``` +#[derive(Debug, Deserialize, Default)] +#[serde(default)] +pub struct ProteinConfig { + /// Override the sample name used in output filenames. + #[serde(default)] + pub sample_name: Option, + + /// Write all output files to a flat directory (no subdirectories). + #[serde(default)] + pub flat_output: bool, + + /// `protein sequence` configuration. + #[serde(default)] + pub sequence: ProteinSequenceConfig, +} + +/// Configuration for the protein FASTA analysis. +/// +/// Example: +/// ```yaml +/// sequence: +/// enabled: true +/// min_length: 50 +/// expect_stop: false +/// ``` +#[derive(Debug, Deserialize)] +#[serde(default)] +pub struct ProteinSequenceConfig { + /// Whether to run the analysis. Defaults to true. + pub enabled: bool, + /// Sequences shorter than this are ignored entirely. + pub min_length: usize, + /// Whether a missing terminal stop codon counts as a defect. Off by + /// default, because most reference proteomes carry no terminal stop. + pub expect_stop: bool, +} + +impl Default for ProteinSequenceConfig { + fn default() -> Self { + Self { + enabled: true, + min_length: 0, + expect_stop: false, + } + } } /// RNA-Seq QC configuration. @@ -908,6 +980,314 @@ impl RnaConfig { } } +// =================================================================== +// DNA QC configuration +// =================================================================== + +/// DNA QC configuration. +/// +/// Contains all settings for the `rustqc dna` subcommand. Tool-specific +/// settings are nested under their tool name (e.g. `mosdepth:`, `samtools:`, +/// `preseq:`). +/// +/// The shared settings are declared here rather than inherited from the root +/// [`Config`], mirroring [`RnaConfig`], so the two pipelines can be configured +/// independently in one file. +/// +/// Example: +/// ```yaml +/// dna: +/// flat_output: true +/// mosdepth: +/// window_size: 500 +/// thresholds: [1, 10, 30] +/// ``` +#[derive(Debug, Deserialize, Default)] +#[serde(default)] +pub struct DnaConfig { + /// Prefix to prepend to alignment file chromosome names before matching + /// interval-file names (for example a targets BED using `chr1` against an + /// alignment using `1`). + #[serde(default)] + pub chromosome_prefix: Option, + + /// Chromosome name mapping from interval-file names to alignment file names. + /// + /// Applied after `chromosome_prefix`, so explicit mappings override it. + #[serde(default)] + pub chromosome_mapping: HashMap, + + /// Override the sample name used in output filenames. + /// + /// The CLI `--sample-name` flag takes precedence over this setting. + #[serde(default)] + pub sample_name: Option, + + /// Write all output files to a flat directory (no subdirectories). + /// + /// By default (`false`), outputs are organised by tool: `mosdepth/`, + /// `samtools/`, `preseq/`. The CLI `--flat-output` flag enables flat + /// output regardless of this setting (either source being `true` produces + /// flat output). + #[serde(default)] + pub flat_output: bool, + + /// mosdepth-compatible depth of coverage configuration. + #[serde(default)] + pub mosdepth: MosdepthConfig, + + /// samtools-compatible output configuration (stats, flagstat, idxstats). + #[serde(default)] + pub samtools: SamtoolsConfig, + + /// Picard CollectWgsMetrics configuration. + #[serde(default)] + pub wgs_metrics: WgsMetricsConfig, + + /// Picard CollectInsertSizeMetrics configuration. + #[serde(default)] + pub insert_size: InsertSizeConfig, + + /// Picard CollectGcBiasMetrics configuration. + #[serde(default)] + pub gc_bias: GcBiasConfig, + + /// Picard CollectHsMetrics configuration, used in targeted mode. + #[serde(default)] + pub hs_metrics: HsMetricsConfig, + + /// Qualimap bamqc configuration. + #[serde(default)] + pub qualimap: BamqcConfig, + + /// preseq lc_extrap library complexity extrapolation configuration. + /// + /// Reuses the same type as the `rna` pipeline; the implementation is shared. + #[serde(default)] + pub preseq: PreseqConfig, +} + +/// Configuration for the Picard-compatible whole-genome coverage metrics. +/// +/// Requires a reference FASTA: `GENOME_TERRITORY` counts the reference's +/// non-N bases, so without one the analysis is skipped. +/// +/// Example: +/// ```yaml +/// wgs_metrics: +/// enabled: true +/// coverage_cap: 250 +/// min_base_quality: 20 +/// min_mapping_quality: 20 +/// ``` +#[derive(Debug, Deserialize)] +#[serde(default)] +pub struct WgsMetricsConfig { + /// Whether to compute whole-genome coverage metrics. Defaults to true. + pub enabled: bool, + /// Depth beyond this is reported as excluded rather than counted. + pub coverage_cap: u32, + /// Bases below this quality are excluded. + pub min_base_quality: u8, + /// Reads below this mapping quality are excluded. + pub min_mapping_quality: u8, +} + +impl Default for WgsMetricsConfig { + fn default() -> Self { + Self { + enabled: true, + coverage_cap: 250, + min_base_quality: 20, + min_mapping_quality: 20, + } + } +} + +/// Configuration for the Qualimap-compatible bamqc report. +/// +/// Named apart from the `rna` pipeline's [`QualimapConfig`], which configures +/// a different Qualimap analysis entirely: gene body coverage rather than +/// bamqc. +/// +/// Example: +/// ```yaml +/// qualimap: +/// enabled: true +/// num_windows: 400 +/// ``` +#[derive(Debug, Deserialize)] +#[serde(default)] +pub struct BamqcConfig { + /// Whether to produce the bamqc outputs. Defaults to true. + pub enabled: bool, + /// Target number of windows the reference is split into. The realised + /// count is usually a little lower, because the window width is rounded up + /// first. + pub num_windows: usize, +} + +impl Default for BamqcConfig { + fn default() -> Self { + Self { + enabled: true, + num_windows: 400, + } + } +} + +/// Configuration for the Picard-compatible GC bias metrics. +/// +/// Requires a reference FASTA: the analysis bins reference windows by GC. +/// +/// Example: +/// ```yaml +/// gc_bias: +/// enabled: true +/// window_size: 100 +/// ``` +#[derive(Debug, Deserialize)] +#[serde(default)] +pub struct GcBiasConfig { + /// Whether to compute GC bias metrics. Defaults to true. + pub enabled: bool, + /// Width of the sliding reference windows GC is computed over. + pub window_size: usize, +} + +impl Default for GcBiasConfig { + fn default() -> Self { + Self { + enabled: true, + window_size: 100, + } + } +} + +/// Configuration for the Picard-compatible targeted sequencing metrics. +/// +/// Only takes effect when `--targets` is given. +/// +/// Example: +/// ```yaml +/// hs_metrics: +/// enabled: true +/// min_base_quality: 20 +/// min_mapping_quality: 20 +/// ``` +#[derive(Debug, Deserialize)] +#[serde(default)] +pub struct HsMetricsConfig { + /// Whether to compute targeted metrics. Defaults to true. + pub enabled: bool, + /// Bases below this quality are excluded. + pub min_base_quality: u8, + /// Reads below this mapping quality are excluded. + pub min_mapping_quality: u8, +} + +impl Default for HsMetricsConfig { + fn default() -> Self { + Self { + enabled: true, + min_base_quality: 20, + min_mapping_quality: 20, + } + } +} + +/// Configuration for the Picard-compatible insert size metrics. +/// +/// Example: +/// ```yaml +/// insert_size: +/// enabled: true +/// deviations: 10.0 +/// ``` +#[derive(Debug, Deserialize)] +#[serde(default)] +pub struct InsertSizeConfig { + /// Whether to compute insert size metrics. Defaults to true. + pub enabled: bool, + /// Median absolute deviations either side of the median that survive + /// trimming before the mean and standard deviation are computed. + pub deviations: f64, +} + +impl Default for InsertSizeConfig { + fn default() -> Self { + Self { + enabled: true, + deviations: 10.0, + } + } +} + +/// Configuration for the mosdepth-compatible depth of coverage analysis. +/// +/// Example: +/// ```yaml +/// mosdepth: +/// enabled: true +/// window_size: 500 +/// thresholds: [1, 10, 30] +/// skip_per_base: false +/// ``` +#[derive(Debug, Deserialize)] +#[serde(default)] +pub struct MosdepthConfig { + /// Whether to compute depth of coverage. Defaults to true. + pub enabled: bool, + + /// Fixed-width window size for the per-window depth output. + /// + /// `None` (the default) means no `regions` output is written, matching + /// mosdepth run without `--by`. + pub window_size: Option, + + /// Coverage thresholds reported in the thresholds output and used for the + /// percent-of-bases-at-least-NX summary figures. + pub thresholds: Vec, + + /// Skip the per-base depth output, by far the largest file produced. + pub skip_per_base: bool, +} + +impl Default for MosdepthConfig { + fn default() -> Self { + Self { + enabled: true, + window_size: None, + thresholds: vec![1, 5, 10, 15, 20, 30, 50], + skip_per_base: false, + } + } +} + +/// Configuration for the samtools-compatible outputs of the DNA pipeline. +/// +/// A single toggle covers `stats`, `flagstat` and `idxstats` because all three +/// are produced from one accumulator in the same pass; disabling them +/// individually would save no work. +/// +/// Example: +/// ```yaml +/// samtools: +/// enabled: true +/// ``` +#[derive(Debug, Deserialize)] +#[serde(default)] +pub struct SamtoolsConfig { + /// Whether to write the samtools-compatible outputs. Defaults to true. + pub enabled: bool, +} + +impl Default for SamtoolsConfig { + fn default() -> Self { + Self { enabled: true } + } +} + #[cfg(test)] mod tests { use super::*; @@ -1294,4 +1674,65 @@ preseq: std::env::set_var("RUSTQC_CONFIG", val); } } + + #[test] + fn test_dna_config_defaults() { + let config = Config::default(); + assert!(config.dna.mosdepth.enabled); + assert!(config.dna.samtools.enabled); + assert!(config.dna.preseq.enabled); + assert!(!config.dna.flat_output); + assert_eq!( + config.dna.mosdepth.thresholds, + vec![1, 5, 10, 15, 20, 30, 50] + ); + assert_eq!(config.dna.mosdepth.window_size, None); + } + + #[test] + fn test_dna_config_from_yaml() { + let yaml = "dna:\n flat_output: true\n mosdepth:\n window_size: 500\n thresholds: [1, 30]\n preseq:\n enabled: false\n"; + let config: Config = serde_yaml_ng::from_str(yaml).unwrap(); + assert!(config.dna.flat_output); + assert_eq!(config.dna.mosdepth.window_size, Some(500)); + assert_eq!(config.dna.mosdepth.thresholds, vec![1, 30]); + assert!(!config.dna.preseq.enabled); + // A dna-only config leaves the rna side untouched. + assert!(config.rna.preseq.enabled); + } + + #[test] + fn test_dna_config_deep_merge() { + let mut merged: Value = serde_yaml_ng::from_str( + "dna:\n mosdepth:\n window_size: 100\n thresholds: [1]\n", + ) + .unwrap(); + let overlay: Value = + serde_yaml_ng::from_str("dna:\n mosdepth:\n window_size: 500\n").unwrap(); + deep_merge(&mut merged, overlay); + let config: Config = serde_yaml_ng::from_value(merged).unwrap(); + assert_eq!(config.dna.mosdepth.window_size, Some(500)); + assert_eq!(config.dna.mosdepth.thresholds, vec![1]); + } + + #[test] + fn test_protein_config_defaults() { + let config = Config::default(); + assert!(config.protein.sequence.enabled); + assert_eq!(config.protein.sequence.min_length, 0); + assert!(!config.protein.sequence.expect_stop); + assert!(!config.protein.flat_output); + } + + #[test] + fn test_protein_config_from_yaml() { + let yaml = "protein:\n flat_output: true\n sequence:\n min_length: 50\n expect_stop: true\n"; + let config: Config = serde_yaml_ng::from_str(yaml).unwrap(); + assert!(config.protein.flat_output); + assert_eq!(config.protein.sequence.min_length, 50); + assert!(config.protein.sequence.expect_stop); + // A protein-only config leaves the other pipelines untouched. + assert!(config.rna.preseq.enabled); + assert!(config.dna.mosdepth.enabled); + } } diff --git a/src/dna/depth.rs b/src/dna/depth.rs new file mode 100644 index 00000000..ce39f2c2 --- /dev/null +++ b/src/dna/depth.rs @@ -0,0 +1,444 @@ +//! Per-contig depth of coverage accumulation. +//! +//! One [`DepthAccum`] covers one contig. Aligned blocks are recorded as +//! increments in a delta array the length of the contig, and a prefix sum at +//! the end turns that into per-base depth in a single linear pass. +//! +//! # Upstream semantics +//! +//! The filters and the CIGAR walk reproduce mosdepth 0.3.14 run without +//! `--fast-mode`, whose help text describes that flag as "dont look at +//! internal cigar operations or correct mate overlaps". Default mode +//! therefore does both, and so does this module: +//! +//! - records carrying any bit of [`MOSDEPTH_DEFAULT_EXCLUDE`] are skipped +//! (mosdepth's `-F` default of 1796); +//! - records with `MAPQ` below the cutoff are skipped (mosdepth's `-Q`, +//! default 0); +//! - `M`, `=` and `X` cover the reference, `D` and `N` advance without +//! covering, and `I`, `S`, `H` and `P` do not advance at all; +//! - a base covered by both mates of one pair counts once. +//! +//! That last rule is not a detail. On the project's test dataset, correcting +//! mate overlaps takes total covered bases from 469875 down to 247878, which +//! is exactly the gap between mosdepth's `--fast-mode` and its default. + +use std::collections::{BTreeMap, HashMap}; + +use rust_htslib::bam; +use rust_htslib::bam::record::Cigar; + +use crate::common::bam_flags::*; + +/// Bit mask matching mosdepth's `-F` default: `UNMAP | SECONDARY | QCFAIL | DUP`. +pub const MOSDEPTH_DEFAULT_EXCLUDE: u16 = BAM_FUNMAP | BAM_FSECONDARY | BAM_FQCFAIL | BAM_FDUP; + +/// Accumulates per-base depth for a single contig. +#[derive(Debug)] +pub struct DepthAccum { + /// Delta array of length `contig_len + 1`; a `+1` at a block start and a + /// `-1` one past its end, summed into depth by [`DepthAccum::into_depths`]. + deltas: Vec, + /// Contig length in bases. + len: usize, + /// Records with `MAPQ` strictly below this value are ignored. + mapq_cut: u8, + /// Records carrying any of these flag bits are ignored. + exclude_flags: u16, + /// Aligned blocks already counted for a pair whose second mate is still + /// ahead, keyed by read name. + pending: HashMap, Vec<(usize, usize)>>, + /// Read names indexed by the position their outstanding mate is expected + /// at, so stale entries can be evicted without scanning `pending`. + pending_by_pos: BTreeMap>>, +} + +impl DepthAccum { + /// Allocate for one contig of `length` bases. + pub fn new(length: u64, mapq_cut: u8, exclude_flags: u16) -> Self { + let len = length as usize; + Self { + deltas: vec![0i32; len + 1], + len, + mapq_cut, + exclude_flags, + pending: HashMap::new(), + pending_by_pos: BTreeMap::new(), + } + } + + /// Add one record's aligned blocks. Records failing the filters are ignored. + /// + /// Records are expected in coordinate order, which is what the per-contig + /// worker feeds. That ordering is what makes the pending-mate bookkeeping + /// bounded: once the read position passes the position an outstanding mate + /// was announced at, that entry can never be claimed and is dropped. + pub fn process_read(&mut self, record: &bam::Record) { + if !self.passes_filters(record) { + return; + } + let pos = record.pos(); + self.evict_unclaimable(pos); + + let blocks = Self::aligned_blocks(record, self.len); + if blocks.is_empty() { + return; + } + + // A record can only overlap its own mate, and only on the same contig. + let paired_here = record.flags() & BAM_FPAIRED != 0 + && record.flags() & BAM_FMUNMAP == 0 + && record.mtid() == record.tid(); + + if paired_here { + if let Some(mate_blocks) = self.pending.remove(record.qname()) { + // Second mate of the pair: shared bases are already counted. + self.add_blocks_excluding(&blocks, &mate_blocks); + return; + } + if record.mpos() >= pos { + let qname = record.qname().to_vec(); + self.pending.insert(qname.clone(), blocks.clone()); + self.pending_by_pos + .entry(record.mpos()) + .or_default() + .push(qname); + } + } + + for &(start, end) in &blocks { + self.add_block_usize(start, end); + } + } + + /// Number of pairs still waiting for their second mate. Test-only: the + /// bookkeeping is an implementation detail, but an unbounded map would be + /// a memory leak on a real chromosome, so it is worth asserting on. + #[cfg(test)] + pub fn pending_mates_len(&self) -> usize { + self.pending.len() + } + + /// Drop pending entries whose outstanding mate lies behind `pos` and can + /// therefore never arrive (it was filtered out, or the file is truncated). + fn evict_unclaimable(&mut self, pos: i64) { + while let Some((&mate_pos, _)) = self.pending_by_pos.iter().next() { + if mate_pos >= pos { + break; + } + // Safe: the key came from `iter().next()` on this same map. + let qnames = self.pending_by_pos.remove(&mate_pos).unwrap_or_default(); + for qname in qnames { + self.pending.remove(&qname); + } + } + } + + /// The record's reference-covering blocks as half-open `[start, end)` + /// intervals, clamped to `len`. + fn aligned_blocks(record: &bam::Record, len: usize) -> Vec<(usize, usize)> { + let mut blocks = Vec::new(); + let mut pos = record.pos(); + for op in record.cigar().iter() { + match op { + // Reference-consuming and query-consuming: covers the reference. + Cigar::Match(n) | Cigar::Equal(n) | Cigar::Diff(n) => { + let n = i64::from(*n); + let start = pos.max(0) as usize; + let end = ((pos + n).max(0) as usize).min(len); + if start < end { + blocks.push((start, end)); + } + pos += n; + } + // Reference-consuming only: advances without covering. + Cigar::Del(n) | Cigar::RefSkip(n) => pos += i64::from(*n), + // Neither reference-consuming nor covering. + Cigar::Ins(_) | Cigar::SoftClip(_) | Cigar::HardClip(_) | Cigar::Pad(_) => {} + } + } + blocks + } + + /// Add `blocks`, skipping any part already covered by `exclude`. + /// + /// Both sides are in ascending order and non-overlapping within themselves, + /// because each comes from one record's CIGAR walk. + fn add_blocks_excluding(&mut self, blocks: &[(usize, usize)], exclude: &[(usize, usize)]) { + for &(start, end) in blocks { + let mut cursor = start; + for &(ex_start, ex_end) in exclude { + if ex_end <= cursor { + continue; + } + if ex_start >= end { + break; + } + if ex_start > cursor { + self.add_block_usize(cursor, ex_start.min(end)); + } + cursor = cursor.max(ex_end); + if cursor >= end { + break; + } + } + if cursor < end { + self.add_block_usize(cursor, end); + } + } + } + + /// Consume the delta array and return per-base depth for the contig. + pub fn into_depths(self) -> Vec { + let mut depths = Vec::with_capacity(self.len); + let mut running = 0i32; + for delta in self.deltas.iter().take(self.len) { + running += delta; + // `running` cannot go negative: every `-1` is emitted only after + // its matching `+1`, and both are clamped to the same range. + depths.push(running.max(0) as u32); + } + depths + } + + /// Whether a record contributes to depth at all. + fn passes_filters(&self, record: &bam::Record) -> bool { + record.flags() & self.exclude_flags == 0 && record.mapq() >= self.mapq_cut + } + + /// Record a half-open aligned block `[start, end)`, already clamped. + fn add_block_usize(&mut self, start: usize, end: usize) { + if start >= end { + return; + } + self.deltas[start] += 1; + self.deltas[end] -= 1; + } +} + +#[cfg(test)] +mod tests { + use super::*; + use rust_htslib::bam::record::{Cigar, CigarString, Record}; + + /// Build a minimal mapped record at `pos` with the given CIGAR, MAPQ and flags. + /// + /// `seq` and `qual` must both be as long as the query-consuming part of the + /// CIGAR, otherwise the record is malformed and every assertion made against + /// it is meaningless, so the helper asserts that itself. + fn rec(pos: i64, cigar: Vec, mapq: u8, flags: u16) -> Record { + let query_len: usize = cigar + .iter() + .map(|op| match op { + Cigar::Match(n) | Cigar::Ins(n) | Cigar::SoftClip(n) => *n as usize, + Cigar::Equal(n) | Cigar::Diff(n) => *n as usize, + _ => 0, + }) + .sum(); + let seq = vec![b'A'; query_len]; + let qual = vec![30u8; query_len]; + assert_eq!(seq.len(), qual.len(), "malformed test record"); + + let mut r = Record::new(); + r.set(b"q", Some(&CigarString(cigar)), &seq, &qual); + r.set_tid(0); + r.set_pos(pos); + r.set_mapq(mapq); + r.set_flags(flags); + r + } + + /// Build a paired record whose mate sits at `mate_pos` on the same contig. + fn pair_rec(qname: &[u8], pos: i64, mate_pos: i64, cigar: Vec, read2: bool) -> Record { + let mut r = rec( + pos, + cigar, + 60, + BAM_FPAIRED | BAM_FPROPER_PAIR | if read2 { BAM_FREAD2 } else { BAM_FREAD1 }, + ); + r.set_qname(qname); + r.set_mtid(0); + r.set_mpos(mate_pos); + r + } + + #[test] + fn match_block_covers_exactly_its_span() { + let mut d = DepthAccum::new(20, 0, MOSDEPTH_DEFAULT_EXCLUDE); + d.process_read(&rec(5, vec![Cigar::Match(4)], 60, 0)); + assert_eq!(&d.into_depths()[4..10], &[0, 1, 1, 1, 1, 0]); + } + + #[test] + fn deletion_and_skip_advance_without_covering() { + let mut d = DepthAccum::new(20, 0, MOSDEPTH_DEFAULT_EXCLUDE); + d.process_read(&rec( + 0, + vec![Cigar::Match(2), Cigar::Del(3), Cigar::Match(2)], + 60, + 0, + )); + assert_eq!(&d.into_depths()[0..8], &[1, 1, 0, 0, 0, 1, 1, 0]); + } + + #[test] + fn ref_skip_advances_without_covering() { + let mut d = DepthAccum::new(20, 0, MOSDEPTH_DEFAULT_EXCLUDE); + d.process_read(&rec( + 0, + vec![Cigar::Match(2), Cigar::RefSkip(3), Cigar::Match(2)], + 60, + 0, + )); + assert_eq!(&d.into_depths()[0..8], &[1, 1, 0, 0, 0, 1, 1, 0]); + } + + #[test] + fn insertion_and_soft_clip_do_not_advance_the_reference() { + let mut d = DepthAccum::new(20, 0, MOSDEPTH_DEFAULT_EXCLUDE); + d.process_read(&rec( + 0, + vec![ + Cigar::SoftClip(3), + Cigar::Match(2), + Cigar::Ins(4), + Cigar::Match(2), + ], + 60, + 0, + )); + assert_eq!(&d.into_depths()[0..6], &[1, 1, 1, 1, 0, 0]); + } + + #[test] + fn duplicate_flagged_reads_are_excluded_by_default() { + let mut d = DepthAccum::new(20, 0, MOSDEPTH_DEFAULT_EXCLUDE); + d.process_read(&rec(0, vec![Cigar::Match(4)], 60, BAM_FDUP)); + assert_eq!(d.into_depths().iter().sum::(), 0); + } + + #[test] + fn secondary_qcfail_and_unmapped_reads_are_excluded_by_default() { + for flag in [BAM_FSECONDARY, BAM_FQCFAIL, BAM_FUNMAP] { + let mut d = DepthAccum::new(20, 0, MOSDEPTH_DEFAULT_EXCLUDE); + d.process_read(&rec(0, vec![Cigar::Match(4)], 60, flag)); + assert_eq!( + d.into_depths().iter().sum::(), + 0, + "flag {flag:#x} should be excluded" + ); + } + } + + #[test] + fn reads_below_the_mapq_cutoff_are_excluded() { + let mut d = DepthAccum::new(20, 30, MOSDEPTH_DEFAULT_EXCLUDE); + d.process_read(&rec(0, vec![Cigar::Match(4)], 29, 0)); + assert_eq!(d.into_depths().iter().sum::(), 0); + + let mut d = DepthAccum::new(20, 30, MOSDEPTH_DEFAULT_EXCLUDE); + d.process_read(&rec(0, vec![Cigar::Match(4)], 30, 0)); + assert_eq!(d.into_depths().iter().sum::(), 4); + } + + #[test] + fn a_read_running_past_the_contig_end_is_clipped_not_panicking() { + let mut d = DepthAccum::new(6, 0, MOSDEPTH_DEFAULT_EXCLUDE); + d.process_read(&rec(4, vec![Cigar::Match(10)], 60, 0)); + assert_eq!(d.into_depths(), vec![0, 0, 0, 0, 1, 1]); + } + + #[test] + fn overlapping_mates_cover_a_base_once() { + let mut d = DepthAccum::new(20, 0, MOSDEPTH_DEFAULT_EXCLUDE); + d.process_read(&pair_rec(b"pair1", 0, 0, vec![Cigar::Match(4)], false)); + d.process_read(&pair_rec(b"pair1", 0, 0, vec![Cigar::Match(4)], true)); + assert_eq!( + &d.into_depths()[0..5], + &[1, 1, 1, 1, 0], + "a base covered by both mates counts once" + ); + } + + #[test] + fn partially_overlapping_mates_count_the_shared_bases_once() { + let mut d = DepthAccum::new(20, 0, MOSDEPTH_DEFAULT_EXCLUDE); + d.process_read(&pair_rec(b"pair1", 0, 2, vec![Cigar::Match(4)], false)); + d.process_read(&pair_rec(b"pair1", 2, 0, vec![Cigar::Match(4)], true)); + // Mate 1 covers 0..4, mate 2 covers 2..6; bases 2 and 3 are shared. + assert_eq!(&d.into_depths()[0..7], &[1, 1, 1, 1, 1, 1, 0]); + } + + #[test] + fn non_overlapping_mates_each_contribute() { + let mut d = DepthAccum::new(20, 0, MOSDEPTH_DEFAULT_EXCLUDE); + d.process_read(&pair_rec(b"pair2", 0, 8, vec![Cigar::Match(4)], false)); + d.process_read(&pair_rec(b"pair2", 8, 0, vec![Cigar::Match(4)], true)); + assert_eq!( + &d.into_depths()[0..13], + &[1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0] + ); + } + + #[test] + fn reads_from_different_pairs_at_the_same_locus_both_count() { + let mut d = DepthAccum::new(20, 0, MOSDEPTH_DEFAULT_EXCLUDE); + d.process_read(&pair_rec(b"pairA", 0, 0, vec![Cigar::Match(4)], false)); + d.process_read(&pair_rec(b"pairB", 0, 0, vec![Cigar::Match(4)], false)); + assert_eq!(d.into_depths()[0], 2); + } + + #[test] + fn the_pending_mate_map_is_emptied_once_both_mates_are_seen() { + let mut d = DepthAccum::new(20, 0, MOSDEPTH_DEFAULT_EXCLUDE); + d.process_read(&pair_rec(b"pair1", 0, 0, vec![Cigar::Match(4)], false)); + d.process_read(&pair_rec(b"pair1", 0, 0, vec![Cigar::Match(4)], true)); + assert_eq!(d.pending_mates_len(), 0, "the entry must be dropped"); + } + + #[test] + fn a_pending_mate_that_never_arrives_is_evicted() { + let mut d = DepthAccum::new(200, 0, MOSDEPTH_DEFAULT_EXCLUDE); + // Its mate is announced at 10 but never turns up (filtered, say). + d.process_read(&pair_rec(b"orphan", 0, 10, vec![Cigar::Match(4)], false)); + assert_eq!(d.pending_mates_len(), 1); + // Walking past position 10 makes the entry unclaimable. + d.process_read(&pair_rec(b"later", 50, 50, vec![Cigar::Match(4)], false)); + assert_eq!( + d.pending_mates_len(), + 1, + "only the unclaimable one is dropped" + ); + } + + /// Engine-level parity check against mosdepth 0.3.14 on the committed + /// fixture. `tests/expected/dna/test.mosdepth.summary.txt` records + /// `total 40001 247878 6.20 0 867` for this BAM, so the total covered + /// bases and the maximum depth are both pinned here. Getting this right + /// requires the flag filter, the CIGAR walk and the mate-overlap + /// correction to all be right at once. + #[test] + fn total_covered_bases_match_mosdepth_on_the_fixture() { + use rust_htslib::bam::Read; + + let bam_path = concat!(env!("CARGO_MANIFEST_DIR"), "/tests/data/dna/test.dna.bam"); + let mut bam = bam::Reader::from_path(bam_path).unwrap(); + let header = bam.header().to_owned(); + let contig_len = header.target_len(0).unwrap(); + + let mut accum = DepthAccum::new(contig_len, 0, MOSDEPTH_DEFAULT_EXCLUDE); + let mut record = Record::new(); + while let Some(result) = bam.read(&mut record) { + result.unwrap(); + accum.process_read(&record); + } + + let depths = accum.into_depths(); + let total: u64 = depths.iter().map(|d| u64::from(*d)).sum(); + let max = depths.iter().copied().max().unwrap(); + + assert_eq!(depths.len(), 40001, "contig length"); + assert_eq!(total, 247878, "total covered bases must match mosdepth"); + assert_eq!(max, 867, "maximum depth must match mosdepth"); + } +} diff --git a/src/dna/gc_bias.rs b/src/dna/gc_bias.rs new file mode 100644 index 00000000..bcaec0b8 --- /dev/null +++ b/src/dna/gc_bias.rs @@ -0,0 +1,514 @@ +//! Picard `CollectGcBiasMetrics` reimplementation. +//! +//! # Upstream semantics +//! +//! These rules come from Picard 3.4.0's own source, `GcBiasUtils` and +//! `GcBiasMetricsCollector`, after black-box inference from its output failed +//! to reproduce them. They are unusual enough to be worth stating. +//! +//! **Windows.** GC is computed over sliding windows of `window_size` bases at +//! every reference position `i` for `1 <= i < len - window_size`. Note both +//! bounds: the window at position 0 is skipped, and so is the last one that +//! would fit. On a 40001 base reference with 100 base windows that gives +//! 39900 windows, not the 39902 a naive reading produces. A window holding +//! more than [`MAX_NS_PER_WINDOW`] `N` bases is marked unusable and its reads +//! are dropped. The GC value is `gc_count * 100 / window_size` in integer +//! arithmetic, truncating rather than rounding. +//! +//! **Read assignment.** A read is assigned to the window at its alignment +//! start, except on the reverse strand, where it is assigned to +//! `alignment_end - window_size`. That is not the read's 5' end; it is the +//! window that would start where the read's far end finishes. Positions are +//! one-based, and a read landing at position 0 or lower is dropped. +//! +//! **Which reads count.** Only unmapped reads and reads with an empty +//! sequence are skipped. Secondary and supplementary alignments and +//! duplicates all contribute, which is what `READS_USED ALL` means. On the +//! project fixture that is the difference between 5640 and 5642 read starts. +//! +//! **Dropout.** For each GC bin, `(window_share - read_share) * 100` is +//! accumulated when positive, into `AT_DROPOUT` for bins at or below 50 and +//! `GC_DROPOUT` above. + +use std::io::Write; +use std::path::Path; + +use anyhow::{Context, Result}; +use rust_htslib::bam; +use rust_htslib::bam::record::Cigar; + +use crate::common::bam_flags::*; + +/// Number of GC bins, one per whole percent from 0 to 100 inclusive. +pub const BINS: usize = 101; + +/// Picard's `SCAN_WINDOW_SIZE` default. +pub const DEFAULT_WINDOW_SIZE: usize = 100; + +/// A window holding more than this many `N` bases is unusable. +pub const MAX_NS_PER_WINDOW: usize = 4; + +/// Accumulates GC bias for one contig. +#[derive(Debug)] +pub struct GcBiasAccum { + /// GC percent per one-based reference position, or `-1` when the window + /// there holds too many `N` bases or does not exist. + gc: Vec, + window_size: usize, + windows_by_gc: [u64; BINS], + reads_by_gc: [u64; BINS], + bases_by_gc: [u64; BINS], + errors_by_gc: [u64; BINS], + total_clusters: u64, + total_aligned_reads: u64, +} + +impl GcBiasAccum { + /// Build the window GC table for one contig's reference bases. + pub fn new(reference: &[u8], window_size: usize) -> Self { + let len = reference.len(); + let mut gc = vec![-1i8; len + 1]; + let mut windows_by_gc = [0u64; BINS]; + + if len > window_size { + // Prefix sums make each window a constant-time lookup. + let mut gc_prefix = vec![0u32; len + 1]; + let mut n_prefix = vec![0u32; len + 1]; + for (i, base) in reference.iter().enumerate() { + let upper = base.to_ascii_uppercase(); + gc_prefix[i + 1] = gc_prefix[i] + u32::from(upper == b'G' || upper == b'C'); + n_prefix[i + 1] = n_prefix[i] + u32::from(upper == b'N'); + } + + let last_window_start = len - window_size; + for i in 1..last_window_start { + let end = i + window_size; + let ns = (n_prefix[end] - n_prefix[i]) as usize; + if ns > MAX_NS_PER_WINDOW { + continue; + } + let gc_count = gc_prefix[end] - gc_prefix[i]; + let percent = (gc_count as usize * 100 / window_size) as i8; + gc[i] = percent; + windows_by_gc[percent as usize] += 1; + } + } + + Self { + gc, + window_size, + windows_by_gc, + reads_by_gc: [0; BINS], + bases_by_gc: [0; BINS], + errors_by_gc: [0; BINS], + total_clusters: 0, + total_aligned_reads: 0, + } + } + + /// Offer one record, with the contig's reference bases for mismatch counting. + pub fn process_read(&mut self, record: &bam::Record, reference: &[u8]) { + if record.seq_len() == 0 { + return; + } + + // A cluster is a template, counted once, at the unpaired read or the + // first of the pair. Unmapped reads count towards clusters even though + // they reach nothing else, so this precedes the mapped check. + if record.flags() & BAM_FPAIRED == 0 || record.flags() & BAM_FREAD1 != 0 { + self.total_clusters += 1; + } + if record.flags() & BAM_FUNMAP != 0 { + return; + } + self.total_aligned_reads += 1; + + // One-based, and the reverse strand is assigned by the far end rather + // than the near one. + let position = if record.flags() & BAM_FREVERSE != 0 { + alignment_end(record) - self.window_size as i64 + } else { + record.pos() + 1 + }; + if position <= 0 { + return; + } + let Some(&percent) = self.gc.get(position as usize) else { + return; + }; + if percent < 0 { + return; + } + + let bin = percent as usize; + self.reads_by_gc[bin] += 1; + self.bases_by_gc[bin] += record.seq_len() as u64; + self.errors_by_gc[bin] += count_errors(record, reference); + } + + /// Fold another contig's counters in. Window tables are per contig and add + /// up the same way. + pub fn merge(&mut self, other: &GcBiasAccum) { + for bin in 0..BINS { + self.windows_by_gc[bin] += other.windows_by_gc[bin]; + self.reads_by_gc[bin] += other.reads_by_gc[bin]; + self.bases_by_gc[bin] += other.bases_by_gc[bin]; + self.errors_by_gc[bin] += other.errors_by_gc[bin]; + } + self.total_clusters += other.total_clusters; + self.total_aligned_reads += other.total_aligned_reads; + } + + /// Summarise into the reported detail rows and summary figures. + pub fn into_result(self, window_size: usize) -> GcBiasResult { + let total_reads: u64 = self.reads_by_gc.iter().sum(); + let total_windows: u64 = self.windows_by_gc.iter().sum(); + let global_rate = if total_windows == 0 { + 0.0 + } else { + total_reads as f64 / total_windows as f64 + }; + + let mut rows = Vec::with_capacity(BINS); + let mut at_dropout = 0.0; + let mut gc_dropout = 0.0; + + for bin in 0..BINS { + let windows = self.windows_by_gc[bin]; + let reads = self.reads_by_gc[bin]; + let bases = self.bases_by_gc[bin]; + let errors = self.errors_by_gc[bin]; + + let normalized = if windows == 0 || global_rate == 0.0 { + 0.0 + } else { + (reads as f64 / windows as f64) / global_rate + }; + let error_bar = if windows == 0 || global_rate == 0.0 { + 0.0 + } else { + ((reads as f64).sqrt() / windows as f64) / global_rate + }; + // Mean quality as the phred score of the observed error rate. + let mean_base_quality = if bases == 0 || errors == 0 { + 0 + } else { + (-10.0 * (errors as f64 / bases as f64).log10()).round() as i32 + }; + + if total_reads > 0 && total_windows > 0 { + let read_share = reads as f64 / total_reads as f64; + let window_share = windows as f64 / total_windows as f64; + let dropout = (window_share - read_share) * 100.0; + if dropout > 0.0 { + if bin <= 50 { + at_dropout += dropout; + } else { + gc_dropout += dropout; + } + } + } + + rows.push(GcBiasDetail { + gc: bin as u32, + windows, + read_starts: reads, + mean_base_quality, + normalized_coverage: normalized, + error_bar_width: error_bar, + }); + } + + GcBiasResult { + rows, + window_size, + total_clusters: self.total_clusters, + aligned_reads: self.total_aligned_reads, + at_dropout, + gc_dropout, + } + } +} + +/// One-based inclusive end of a record's alignment. +fn alignment_end(record: &bam::Record) -> i64 { + let mut end = record.pos(); + for op in record.cigar().iter() { + match op { + Cigar::Match(n) + | Cigar::Equal(n) + | Cigar::Diff(n) + | Cigar::Del(n) + | Cigar::RefSkip(n) => end += i64::from(*n), + _ => {} + } + } + end +} + +/// Mismatches against the reference, plus inserted and deleted bases, which is +/// what Picard counts towards the per-bin error rate. +fn count_errors(record: &bam::Record, reference: &[u8]) -> u64 { + let sequence = record.seq(); + let mut errors = 0u64; + let mut ref_pos = record.pos(); + let mut query_pos = 0i64; + + for op in record.cigar().iter() { + match op { + Cigar::Match(n) | Cigar::Equal(n) | Cigar::Diff(n) => { + for k in 0..i64::from(*n) { + let r = ref_pos + k; + let q = query_pos + k; + if r < 0 || r as usize >= reference.len() { + continue; + } + let ref_base = reference[r as usize].to_ascii_uppercase(); + let read_base = sequence[q as usize].to_ascii_uppercase(); + // htsjdk's basesEqual is a plain comparison after + // uppercasing, so an N on either side is a mismatch rather + // than a free pass. + if ref_base != read_base { + errors += 1; + } + } + ref_pos += i64::from(*n); + query_pos += i64::from(*n); + } + Cigar::Ins(n) => { + errors += u64::from(*n); + query_pos += i64::from(*n); + } + Cigar::Del(n) => { + errors += u64::from(*n); + ref_pos += i64::from(*n); + } + Cigar::RefSkip(n) => ref_pos += i64::from(*n), + Cigar::SoftClip(n) => query_pos += i64::from(*n), + Cigar::HardClip(_) | Cigar::Pad(_) => {} + } + } + errors +} + +/// One GC bin's detail row. +#[derive(Debug, Clone)] +pub struct GcBiasDetail { + /// GC percent this row describes. + pub gc: u32, + /// Reference windows at this GC. + pub windows: u64, + /// Reads assigned to a window at this GC. + pub read_starts: u64, + /// Phred score of the observed error rate for those reads. + pub mean_base_quality: i32, + /// Read density here relative to the genome-wide density. + pub normalized_coverage: f64, + /// One standard error of `normalized_coverage`. + pub error_bar_width: f64, +} + +/// The complete GC bias result. +#[derive(Debug, Clone)] +pub struct GcBiasResult { + /// One row per GC bin, ascending. + pub rows: Vec, + /// Window size the bins were computed over. + pub window_size: usize, + /// Templates seen. + pub total_clusters: u64, + /// Mapped reads seen. + pub aligned_reads: u64, + /// Illumina-style AT dropout. + pub at_dropout: f64, + /// Illumina-style GC dropout. + pub gc_dropout: f64, +} + +impl GcBiasResult { + /// Mean normalised coverage across a GC range, as the `GC_NC_x_y` columns + /// report it. + /// + /// The mean is weighted by how many reference windows each bin holds, not + /// a plain average over bins. That distinction matters at the extremes, + /// where most bins hold no windows at all and would otherwise drag the + /// figure towards zero. + fn mean_normalized(&self, low: u32, high: u32) -> f64 { + let mut weighted = 0.0; + let mut windows = 0u64; + for row in self.rows.iter().filter(|r| r.gc >= low && r.gc <= high) { + weighted += row.normalized_coverage * row.windows as f64; + windows += row.windows; + } + if windows == 0 { + 0.0 + } else { + weighted / windows as f64 + } + } +} + +/// Format a float the way Picard's metrics writer does. +fn fmt_picard(value: f64) -> String { + if !value.is_finite() { + return "?".to_string(); + } + if value == value.trunc() && value.abs() < 1e15 { + return format!("{}", value as i64); + } + let text = format!("{value:.6}"); + text.trim_end_matches('0').trim_end_matches('.').to_string() +} + +/// Write the per-GC-bin detail metrics. +pub fn write_detail_metrics(result: &GcBiasResult, path: &Path) -> Result<()> { + let mut out = std::fs::File::create(path) + .map(std::io::BufWriter::new) + .with_context(|| { + format!( + "Failed to create GC bias detail metrics: {}", + path.display() + ) + })?; + + writeln!(out, "## METRICS CLASS\tpicard.analysis.GcBiasDetailMetrics")?; + writeln!( + out, + "ACCUMULATION_LEVEL\tREADS_USED\tGC\tWINDOWS\tREAD_STARTS\tMEAN_BASE_QUALITY\t\ + NORMALIZED_COVERAGE\tERROR_BAR_WIDTH\tSAMPLE\tLIBRARY\tREAD_GROUP" + )?; + for row in &result.rows { + writeln!( + out, + "All Reads\tALL\t{}\t{}\t{}\t{}\t{}\t{}\t\t\t", + row.gc, + row.windows, + row.read_starts, + row.mean_base_quality, + fmt_picard(row.normalized_coverage), + fmt_picard(row.error_bar_width), + )?; + } + // Picard leaves two blank lines at the end of the GC bias tables, one more + // than it writes after the insert size or WGS tables. The fixtures are the + // specification, so this matches them rather than being tidied. + writeln!(out)?; + writeln!(out)?; + out.flush()?; + Ok(()) +} + +/// Write the GC bias summary metrics. +pub fn write_summary_metrics(result: &GcBiasResult, path: &Path) -> Result<()> { + let mut out = std::fs::File::create(path) + .map(std::io::BufWriter::new) + .with_context(|| { + format!( + "Failed to create GC bias summary metrics: {}", + path.display() + ) + })?; + + writeln!( + out, + "## METRICS CLASS\tpicard.analysis.GcBiasSummaryMetrics" + )?; + writeln!( + out, + "ACCUMULATION_LEVEL\tREADS_USED\tWINDOW_SIZE\tTOTAL_CLUSTERS\tALIGNED_READS\t\ + AT_DROPOUT\tGC_DROPOUT\tGC_NC_0_19\tGC_NC_20_39\tGC_NC_40_59\tGC_NC_60_79\t\ + GC_NC_80_100\tSAMPLE\tLIBRARY\tREAD_GROUP" + )?; + writeln!( + out, + "All Reads\tALL\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t\t\t", + result.window_size, + result.total_clusters, + result.aligned_reads, + fmt_picard(result.at_dropout), + fmt_picard(result.gc_dropout), + fmt_picard(result.mean_normalized(0, 19)), + fmt_picard(result.mean_normalized(20, 39)), + fmt_picard(result.mean_normalized(40, 59)), + fmt_picard(result.mean_normalized(60, 79)), + fmt_picard(result.mean_normalized(80, 100)), + )?; + writeln!(out)?; + writeln!(out)?; + out.flush()?; + Ok(()) +} + +#[cfg(test)] +mod tests { + use super::*; + + #[test] + fn the_first_and_last_possible_windows_are_both_skipped() { + // 10 bases, window size 4: naive windows would be starts 0 through 6, + // Picard's loop runs 1 through 5. + let reference = b"ACGTACGTAC".to_vec(); + let accum = GcBiasAccum::new(&reference, 4); + let windows: u64 = accum.windows_by_gc.iter().sum(); + assert_eq!(windows, 5, "starts 1 through 5 inclusive"); + assert_eq!(accum.gc[0], -1, "the window at 0 is never computed"); + assert_eq!(accum.gc[6], -1, "nor the last one that would fit"); + } + + #[test] + fn gc_percent_truncates_rather_than_rounds() { + // 3 of 8 bases are G or C: 3 * 100 / 8 is 37.5, truncated to 37. + let reference = b"GGCAAAAAAAAA".to_vec(); + let accum = GcBiasAccum::new(&reference, 8); + // Window at position 1 is GCAAAAAA, two of eight, 25 percent. + assert_eq!(accum.gc[1], 25); + } + + #[test] + fn windows_with_too_many_ns_are_unusable() { + let reference = b"ACGTNNNNNGCTAGCTAGC".to_vec(); + let accum = GcBiasAccum::new(&reference, 8); + // The window at 1 holds five Ns, one more than the limit. + assert_eq!(accum.gc[1], -1); + } + + #[test] + fn dropout_splits_at_fifty_percent_gc() { + let mut accum = GcBiasAccum::new(&b"A".repeat(200), 100); + // Hand-place windows and reads so the shares are unambiguous. + accum.windows_by_gc = [0; BINS]; + accum.reads_by_gc = [0; BINS]; + accum.windows_by_gc[30] = 50; + accum.windows_by_gc[70] = 50; + accum.reads_by_gc[30] = 100; + accum.reads_by_gc[70] = 0; + let result = accum.into_result(100); + // Bin 70 has half the windows and none of the reads: 50 points of + // dropout, and being above 50 percent GC it lands in GC_DROPOUT. + assert!( + (result.gc_dropout - 50.0).abs() < 1e-9, + "{}", + result.gc_dropout + ); + assert!( + (result.at_dropout - 0.0).abs() < 1e-9, + "{}", + result.at_dropout + ); + } + + #[test] + fn normalized_coverage_is_relative_to_the_genome_wide_rate() { + let mut accum = GcBiasAccum::new(&b"A".repeat(200), 100); + accum.windows_by_gc = [0; BINS]; + accum.reads_by_gc = [0; BINS]; + accum.windows_by_gc[10] = 100; + accum.windows_by_gc[20] = 100; + accum.reads_by_gc[10] = 150; + accum.reads_by_gc[20] = 50; + let result = accum.into_result(100); + // Global rate is 200 reads over 200 windows, so 1 read per window. + assert!((result.rows[10].normalized_coverage - 1.5).abs() < 1e-9); + assert!((result.rows[20].normalized_coverage - 0.5).abs() < 1e-9); + } +} diff --git a/src/dna/hs_metrics.rs b/src/dna/hs_metrics.rs new file mode 100644 index 00000000..ba82e745 --- /dev/null +++ b/src/dna/hs_metrics.rs @@ -0,0 +1,677 @@ +//! Picard `CollectHsMetrics` reimplementation for targeted sequencing. +//! +//! # Upstream semantics +//! +//! Taken from Picard 3.4.0's `TargetMetricsCollector`, because this collector +//! does not filter the way [`crate::dna::wgs_metrics`] does and the difference +//! is not guessable from the outputs. +//! +//! Secondary alignments are excluded outright, so `TOTAL_READS` is 5642 on the +//! project fixture rather than the 5644 records it holds. Then, per record: +//! +//! 1. `PF_BASES` accumulates the read length of every non-supplementary read; +//! 2. mapped reads add their reference-aligned bases to `PF_BASES_ALIGNED`, +//! and to `PF_UQ_BASES_ALIGNED` when not duplicate-flagged; +//! 3. the bait counters are taken **before** any filtering, so the assay +//! metrics are not skewed by duplicates or mapping quality; +//! 4. duplicates are charged to `PCT_EXC_DUPE` and dropped; +//! 5. reads below the mapping quality floor are dropped; +//! 6. **overlap clipping happens next, at the read level**, charging +//! `PCT_EXC_OVERLAP` with the number of aligned bases clipped; +//! 7. only then, per surviving base: below the base quality floor charges +//! `PCT_EXC_BASEQ`; off-target charges `PCT_EXC_OFF_TARGET`; the rest are +//! `ON_TARGET_BASES`. +//! +//! Step 6 before step 7 is the crux. `CollectWgsMetrics` applies base quality +//! first and reconciles overlaps per locus afterwards, which is why the two +//! collectors report different `PCT_EXC_BASEQ` and `PCT_EXC_OVERLAP` on the +//! same file: 0.003982 against 0.007352, and 0.330968 against 0.324694. +//! +//! Only the left-most mate of an overlapping pair is clipped, and everything +//! from the mate's alignment start onwards goes, per htsjdk's +//! `getNumOverlappingAlignedBasesToClip`. +//! +//! # What is not reproduced +//! +//! `HET_SNP_SENSITIVITY` and `HET_SNP_Q` come from the same Monte Carlo +//! simulation left out of `CollectWgsMetrics`, and `HS_PENALTY_*X` and +//! `FOLD_80_BASE_PENALTY` derive from it. All are written as Picard writes +//! them when it cannot compute them: `-1` for the penalties, `?` for the rest. + +use std::io::Write; +use std::path::Path; + +use anyhow::{Context, Result}; +use rust_htslib::bam; +use rust_htslib::bam::record::Cigar; + +use crate::common::bam_flags::*; +use crate::dna::intervals::IntervalSet; + +/// Coverage levels reported as `PCT_TARGET_BASES_xX`, in output order. +pub const TARGET_COVERAGE_LEVELS: [u32; 17] = [ + 1, 2, 10, 20, 30, 40, 50, 100, 250, 500, 1000, 2500, 5000, 10000, 25000, 50000, 100000, +]; + +/// Penalty levels reported as `HS_PENALTY_xX`, in output order. +pub const PENALTY_LEVELS: [u32; 6] = [10, 20, 30, 40, 50, 100]; + +/// Accumulates targeted-sequencing metrics for one contig. +#[derive(Debug)] +pub struct HsAccum { + bait_mask: Vec, + target_mask: Vec, + /// High quality on-target depth per reference base. + depth: Vec, + min_mapping_quality: u8, + min_base_quality: u8, + counters: HsCounters, +} + +/// The raw counters, summed across contigs. +#[derive(Debug, Clone, Default)] +pub struct HsCounters { + /// Records seen, secondary alignments excluded. + pub total_reads: u64, + /// Read length of every non-supplementary record. + pub pf_bases: u64, + /// Reference-aligned bases of mapped records. + pub pf_bases_aligned: u64, + /// Reference-aligned bases of mapped, non-duplicate records. + pub pf_uq_bases_aligned: u64, + /// Non-duplicate records. + pub pf_unique_reads: u64, + /// Non-duplicate mapped records. + pub pf_uq_reads_aligned: u64, + /// Aligned bases falling on a bait. + pub on_bait_bases: u64, + /// Aligned bases of bait-overlapping reads that miss the baits themselves. + pub near_bait_bases: u64, + /// Aligned bases of reads that touch no bait at all. + pub off_bait_bases: u64, + /// Bases dropped because their read was duplicate-flagged. + pub excluded_dupe: u64, + /// Bases clipped as overlapping a mate. + pub excluded_overlap: u64, + /// Bases dropped for low base quality. + pub excluded_baseq: u64, + /// Bases dropped for falling outside the targets. + pub excluded_off_target: u64, + /// High quality bases on target. + pub on_target_bases: u64, + /// First-of-pair records over a bait, with a mapped mate. + pub selected_pairs: u64, + /// The same, excluding duplicates. + pub selected_unique_pairs: u64, +} + +impl HsCounters { + /// Add another contig's counters. + pub fn merge(&mut self, other: &HsCounters) { + self.total_reads += other.total_reads; + self.pf_bases += other.pf_bases; + self.pf_bases_aligned += other.pf_bases_aligned; + self.pf_uq_bases_aligned += other.pf_uq_bases_aligned; + self.pf_unique_reads += other.pf_unique_reads; + self.pf_uq_reads_aligned += other.pf_uq_reads_aligned; + self.on_bait_bases += other.on_bait_bases; + self.near_bait_bases += other.near_bait_bases; + self.off_bait_bases += other.off_bait_bases; + self.excluded_dupe += other.excluded_dupe; + self.excluded_overlap += other.excluded_overlap; + self.excluded_baseq += other.excluded_baseq; + self.excluded_off_target += other.excluded_off_target; + self.on_target_bases += other.on_target_bases; + self.selected_pairs += other.selected_pairs; + self.selected_unique_pairs += other.selected_unique_pairs; + } +} + +impl HsAccum { + /// Prepare for one contig. + pub fn new( + contig: &str, + length: u64, + baits: &IntervalSet, + targets: &IntervalSet, + min_mapping_quality: u8, + min_base_quality: u8, + ) -> Self { + Self { + bait_mask: baits.mask(contig, length), + target_mask: targets.mask(contig, length), + depth: vec![0; length as usize], + min_mapping_quality, + min_base_quality, + counters: HsCounters::default(), + } + } + + /// Offer one record. + pub fn process_read(&mut self, record: &bam::Record) { + let flags = record.flags(); + // Secondary alignments are not part of this collector's read set. + if flags & BAM_FSECONDARY != 0 || flags & BAM_FQCFAIL != 0 { + return; + } + self.counters.total_reads += 1; + + if flags & BAM_FSUPPLEMENTARY == 0 { + self.counters.pf_bases += record.seq_len() as u64; + } + if flags & BAM_FDUP == 0 { + self.counters.pf_unique_reads += 1; + } + if flags & BAM_FUNMAP != 0 { + return; + } + + let blocks = aligned_blocks(record, self.depth.len()); + let aligned: u64 = blocks.iter().map(|(start, end)| end - start).sum(); + self.counters.pf_bases_aligned += aligned; + if flags & BAM_FDUP == 0 { + self.counters.pf_uq_bases_aligned += aligned; + self.counters.pf_uq_reads_aligned += 1; + } + + // Bait metrics come before duplicate, mapping quality and overlap + // filtering, so that the assay is measured rather than the library. + let on_bait: u64 = blocks + .iter() + .map(|(start, end)| { + (*start..*end) + .filter(|p| self.bait_mask.get(*p as usize).copied().unwrap_or(false)) + .count() as u64 + }) + .sum(); + if on_bait > 0 { + self.counters.on_bait_bases += on_bait; + self.counters.near_bait_bases += aligned - on_bait; + } else { + self.counters.off_bait_bases += aligned; + } + + // HS_LIBRARY_SIZE counts templates over a bait, once each. + if flags & BAM_FSUPPLEMENTARY == 0 + && flags & BAM_FPAIRED != 0 + && flags & BAM_FREAD1 != 0 + && flags & BAM_FMUNMAP == 0 + && on_bait > 0 + { + self.counters.selected_pairs += 1; + if flags & BAM_FDUP == 0 { + self.counters.selected_unique_pairs += 1; + } + } + + if flags & BAM_FDUP != 0 { + self.counters.excluded_dupe += aligned; + return; + } + if record.mapq() < self.min_mapping_quality { + return; + } + + // Overlap clipping, at the read level and before any base is examined. + // + // Two different quantities are at play. The counter Picard reports is + // htsjdk's count of *read* bases clipped, insertions included. What is + // actually removed from the alignment is every base at or past the + // mate's start, in *reference* coordinates. They coincide only for a + // gapless read, so they are tracked separately. + self.counters.excluded_overlap += overlapping_bases_to_clip(record); + let clip_from = overlap_clip_reference_start(record); + + let qualities = record.qual(); + let mut ref_pos = record.pos(); + let mut query_pos = 0i64; + for op in record.cigar().iter() { + match op { + Cigar::Match(n) | Cigar::Equal(n) | Cigar::Diff(n) => { + for k in 0..i64::from(*n) { + let r = ref_pos + k; + if r < 0 || r as usize >= self.depth.len() { + continue; + } + if let Some(from) = clip_from { + if r >= from { + continue; + } + } + let quality = qualities + .get((query_pos + k) as usize) + .copied() + .unwrap_or(0); + if quality < self.min_base_quality { + self.counters.excluded_baseq += 1; + } else if !self.target_mask[r as usize] { + self.counters.excluded_off_target += 1; + } else { + self.counters.on_target_bases += 1; + self.depth[r as usize] += 1; + } + } + ref_pos += i64::from(*n); + query_pos += i64::from(*n); + } + Cigar::Del(n) | Cigar::RefSkip(n) => ref_pos += i64::from(*n), + Cigar::Ins(n) | Cigar::SoftClip(n) => query_pos += i64::from(*n), + Cigar::HardClip(_) | Cigar::Pad(_) => {} + } + } + } + + /// Consume the accumulator, returning its counters and per-base depths. + pub fn into_parts(self) -> (HsCounters, Vec, Vec) { + (self.counters, self.depth, self.target_mask) + } +} + +/// A record's reference-covering blocks as half-open `[start, end)`. +fn aligned_blocks(record: &bam::Record, contig_len: usize) -> Vec<(u64, u64)> { + let mut blocks = Vec::new(); + let mut ref_pos = record.pos(); + for op in record.cigar().iter() { + match op { + Cigar::Match(n) | Cigar::Equal(n) | Cigar::Diff(n) => { + let start = ref_pos.max(0) as u64; + let end = ((ref_pos + i64::from(*n)).max(0) as u64).min(contig_len as u64); + if start < end { + blocks.push((start, end)); + } + ref_pos += i64::from(*n); + } + Cigar::Del(n) | Cigar::RefSkip(n) => ref_pos += i64::from(*n), + _ => {} + } + } + blocks +} + +/// The reference position from which this read's alignment is clipped away +/// because its mate covers it, or `None` when nothing is clipped. +/// +/// This is the mate's alignment start: the left-most read of an overlapping +/// pair loses everything from there onwards. +fn overlap_clip_reference_start(record: &bam::Record) -> Option { + if overlapping_bases_to_clip(record) == 0 { + return None; + } + Some(record.mpos()) +} + +/// Read bases to clip because a mate covers them, per htsjdk's +/// `getNumOverlappingAlignedBasesToClip`. +/// +/// Only the left-most mate of the pair is clipped, and everything from the +/// mate's alignment start onwards goes. A pair sharing a start is broken by +/// clipping the second of the pair. +fn overlapping_bases_to_clip(record: &bam::Record) -> u64 { + let flags = record.flags(); + if flags & BAM_FPAIRED == 0 || flags & BAM_FUNMAP != 0 || flags & BAM_FMUNMAP != 0 { + return 0; + } + let start = record.pos(); + let mate_start = record.mpos(); + if mate_start < start { + return 0; + } + if mate_start == start && flags & BAM_FREAD1 != 0 { + return 0; + } + + let mut clipped: i64 = 0; + let mut ref_pos = start; + for op in record.cigar().iter() { + let ref_len = match op { + Cigar::Match(n) + | Cigar::Equal(n) + | Cigar::Diff(n) + | Cigar::Del(n) + | Cigar::RefSkip(n) => i64::from(*n), + _ => 0, + }; + if mate_start < ref_pos + ref_len { + match op { + // Only M takes the partial path: htsjdk's MATCH_OR_MISMATCH is + // the M operator alone, so = and X fall through to the branch + // below and lose their whole element. + Cigar::Match(_) => { + clipped += if mate_start < ref_pos { + ref_len + } else { + ref_pos + ref_len - mate_start + }; + } + Cigar::SoftClip(_) | Cigar::HardClip(_) | Cigar::Pad(_) | Cigar::RefSkip(_) => {} + // Everything else loses its read-consuming bases outright, + // which covers insertions as well as = and X. + Cigar::Equal(n) | Cigar::Diff(n) | Cigar::Ins(n) => clipped += i64::from(*n), + Cigar::Del(_) => {} + } + } + ref_pos += ref_len; + } + // Left-most but not actually overlapping. + clipped.max(0) as u64 +} + +/// Estimate library size from observed and unique templates. +/// +/// Solves the Lander-Waterman equation `C/X = 1 - exp(-N/X)` by bisection, +/// exactly as Picard's `DuplicationMetrics.estimateLibrarySize` does, down to +/// the forty iterations and the starting bracket. +pub fn estimate_library_size(read_pairs: u64, unique_read_pairs: u64) -> Option { + if read_pairs == 0 || read_pairs <= unique_read_pairs || unique_read_pairs == 0 { + return None; + } + let n = read_pairs as f64; + let c = unique_read_pairs as f64; + let f = |x: f64| c / x - 1.0 + (-n / x).exp(); + + let mut low = 1.0; + let mut high = 100.0; + while f(high * c) > 0.0 { + high *= 10.0; + } + for _ in 0..40 { + let mid = (low + high) / 2.0; + let value = f(mid * c); + if value == 0.0 { + break; + } else if value > 0.0 { + low = mid; + } else { + high = mid; + } + } + Some((c * (low + high) / 2.0) as u64) +} + +/// The computed `CollectHsMetrics` figures. +#[derive(Debug, Clone)] +pub struct HsMetricsResult { + /// Name of the bait set. + pub bait_set: String, + /// Bases covered by baits. + pub bait_territory: u64, + /// Bases covered by targets. + pub target_territory: u64, + /// Total reference length. + pub genome_size: u64, + /// Raw counters. + pub counters: HsCounters, + /// High quality depth of every target base, target order. + pub target_depths: Vec, + /// Number of targets with no coverage at all. + pub zero_coverage_targets: u64, + /// Number of targets. + pub target_count: u64, + /// Estimated library size, when it can be estimated. + pub library_size: Option, +} + +impl HsMetricsResult { + /// Mean high quality coverage over the targets. + pub fn mean_target_coverage(&self) -> f64 { + if self.target_territory == 0 { + 0.0 + } else { + self.counters.on_target_bases as f64 / self.target_territory as f64 + } + } + + /// Mean aligned coverage over the baits. + pub fn mean_bait_coverage(&self) -> f64 { + if self.bait_territory == 0 { + 0.0 + } else { + self.counters.pf_bases_aligned as f64 / self.bait_territory as f64 + } + } + + /// Fraction of the targets at or above each level. + pub fn target_coverage_fractions(&self) -> Vec { + TARGET_COVERAGE_LEVELS + .iter() + .map(|level| { + if self.target_territory == 0 { + return 0.0; + } + let at_or_above = self.target_depths.iter().filter(|d| **d >= *level).count(); + at_or_above as f64 / self.target_territory as f64 + }) + .collect() + } + + /// Median, minimum and maximum high quality target coverage. + pub fn target_coverage_bounds(&self) -> (u32, u32, u32) { + if self.target_depths.is_empty() { + return (0, 0, 0); + } + let mut sorted = self.target_depths.clone(); + sorted.sort_unstable(); + let median = sorted[sorted.len() / 2]; + (median, sorted[0], sorted[sorted.len() - 1]) + } +} + +/// Format a float the way Picard's metrics writer does. +fn fmt_picard(value: f64) -> String { + if !value.is_finite() { + return "?".to_string(); + } + if value == value.trunc() && value.abs() < 1e15 { + return format!("{}", value as i64); + } + let text = format!("{value:.6}"); + text.trim_end_matches('0').trim_end_matches('.').to_string() +} + +/// Write a Picard-compatible `hs_metrics.txt`. +pub fn write_hs_metrics(result: &HsMetricsResult, path: &Path) -> Result<()> { + let mut out = std::fs::File::create(path) + .map(std::io::BufWriter::new) + .with_context(|| format!("Failed to create HS metrics: {}", path.display()))?; + + let c = &result.counters; + let aligned = c.pf_bases_aligned as f64; + let frac = |n: u64| { + if aligned == 0.0 { + 0.0 + } else { + n as f64 / aligned + } + }; + let selected = c.on_bait_bases + c.near_bait_bases; + let (median, min, max) = result.target_coverage_bounds(); + + writeln!(out, "## METRICS CLASS\tpicard.analysis.directed.HsMetrics")?; + + let mut header = String::from( + "BAIT_SET\tBAIT_TERRITORY\tBAIT_DESIGN_EFFICIENCY\tON_BAIT_BASES\tNEAR_BAIT_BASES\t\ + OFF_BAIT_BASES\tPCT_SELECTED_BASES\tPCT_OFF_BAIT\tON_BAIT_VS_SELECTED\t\ + MEAN_BAIT_COVERAGE\tPCT_USABLE_BASES_ON_BAIT\tPCT_USABLE_BASES_ON_TARGET\t\ + FOLD_ENRICHMENT\tHS_LIBRARY_SIZE", + ); + for level in PENALTY_LEVELS { + header.push_str(&format!("\tHS_PENALTY_{level}X")); + } + header.push_str( + "\tTARGET_TERRITORY\tGENOME_SIZE\tTOTAL_READS\tPF_READS\tPF_BASES\tPF_UNIQUE_READS\t\ + PF_UQ_READS_ALIGNED\tPF_BASES_ALIGNED\tPF_UQ_BASES_ALIGNED\tON_TARGET_BASES\t\ + PCT_PF_READS\tPCT_PF_UQ_READS\tPCT_PF_UQ_READS_ALIGNED\tMEAN_TARGET_COVERAGE\t\ + MEDIAN_TARGET_COVERAGE\tMAX_TARGET_COVERAGE\tMIN_TARGET_COVERAGE\tZERO_CVG_TARGETS_PCT\t\ + PCT_EXC_DUPE\tPCT_EXC_ADAPTER\tPCT_EXC_MAPQ\tPCT_EXC_BASEQ\tPCT_EXC_OVERLAP\t\ + PCT_EXC_OFF_TARGET\tFOLD_80_BASE_PENALTY", + ); + for level in TARGET_COVERAGE_LEVELS { + header.push_str(&format!("\tPCT_TARGET_BASES_{level}X")); + } + header.push_str( + "\tAT_DROPOUT\tGC_DROPOUT\tHET_SNP_SENSITIVITY\tHET_SNP_Q\tSAMPLE\tLIBRARY\tREAD_GROUP", + ); + writeln!(out, "{header}")?; + + write!( + out, + "{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}", + result.bait_set, + result.bait_territory, + // Every bait base is intended as a target here; Picard reports the + // fraction of bait territory that is also target territory. + fmt_picard(if result.bait_territory == 0 { + 0.0 + } else { + result.target_territory.min(result.bait_territory) as f64 / result.bait_territory as f64 + }), + c.on_bait_bases, + c.near_bait_bases, + c.off_bait_bases, + fmt_picard(frac(selected)), + fmt_picard(frac(c.off_bait_bases)), + fmt_picard(if selected == 0 { + 0.0 + } else { + c.on_bait_bases as f64 / selected as f64 + }), + fmt_picard(result.mean_bait_coverage()), + fmt_picard(if c.pf_bases == 0 { + 0.0 + } else { + c.on_bait_bases as f64 / c.pf_bases as f64 + }), + fmt_picard(if c.pf_bases == 0 { + 0.0 + } else { + c.on_target_bases as f64 / c.pf_bases as f64 + }), + fmt_picard(fold_enrichment(result)), + result + .library_size + .map(|v| v.to_string()) + .unwrap_or_default(), + )?; + // The penalties derive from the theoretical sensitivity simulation, which + // is out of scope; Picard writes -1 when it cannot compute them. + for _ in PENALTY_LEVELS { + write!(out, "\t-1")?; + } + write!( + out, + "\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t?", + result.target_territory, + result.genome_size, + c.total_reads, + c.total_reads, + c.pf_bases, + c.pf_unique_reads, + c.pf_uq_reads_aligned, + c.pf_bases_aligned, + c.pf_uq_bases_aligned, + c.on_target_bases, + fmt_picard(1.0), + fmt_picard(if c.total_reads == 0 { + 0.0 + } else { + c.pf_unique_reads as f64 / c.total_reads as f64 + }), + fmt_picard(if c.pf_unique_reads == 0 { + 0.0 + } else { + c.pf_uq_reads_aligned as f64 / c.pf_unique_reads as f64 + }), + fmt_picard(result.mean_target_coverage()), + median, + max, + min, + fmt_picard(if result.target_count == 0 { + 0.0 + } else { + result.zero_coverage_targets as f64 / result.target_count as f64 + }), + fmt_picard(frac(c.excluded_dupe)), + fmt_picard(0.0), + fmt_picard(frac(0)), + fmt_picard(frac(c.excluded_baseq)), + fmt_picard(frac(c.excluded_overlap)), + fmt_picard(frac(c.excluded_off_target)), + )?; + for fraction in result.target_coverage_fractions() { + write!(out, "\t{}", fmt_picard(fraction))?; + } + // AT and GC dropout over targets, and the two simulated columns, are not + // computed; see the module documentation. + writeln!(out, "\t?\t?\t?\t?\t\t\t")?; + writeln!(out)?; + + out.flush()?; + Ok(()) +} + +/// Enrichment of the selected territory relative to uniform coverage. +/// +/// Picard computes this from the *selected* bases against the bait territory, +/// not from on-target bases against the target territory. On the project +/// fixture every aligned base is on bait, so the figure reduces to +/// `GENOME_SIZE / BAIT_TERRITORY`, which is exactly the 1.142886 it reports. +fn fold_enrichment(result: &HsMetricsResult) -> f64 { + let c = &result.counters; + if c.pf_bases_aligned == 0 || result.bait_territory == 0 || result.genome_size == 0 { + return 0.0; + } + let selected = (c.on_bait_bases + c.near_bait_bases) as f64 / c.pf_bases_aligned as f64; + selected / (result.bait_territory as f64 / result.genome_size as f64) +} + +#[cfg(test)] +mod tests { + use super::*; + + #[test] + fn library_size_solves_the_lander_waterman_equation() { + // The project fixture's counts, checked against Picard's own answer. + assert_eq!(estimate_library_size(2820, 1992), Some(3807)); + } + + #[test] + fn library_size_is_absent_when_nothing_is_duplicated() { + assert_eq!(estimate_library_size(100, 100), None); + assert_eq!(estimate_library_size(0, 0), None); + } + + #[test] + fn target_coverage_fractions_are_at_or_above_each_level() { + let result = HsMetricsResult { + bait_set: "t".into(), + bait_territory: 4, + target_territory: 4, + genome_size: 100, + counters: HsCounters::default(), + target_depths: vec![0, 1, 10, 300], + zero_coverage_targets: 0, + target_count: 1, + library_size: None, + }; + let f = result.target_coverage_fractions(); + assert!((f[0] - 0.75).abs() < 1e-12, "1X"); + assert!((f[2] - 0.5).abs() < 1e-12, "10X"); + assert!((f[8] - 0.25).abs() < 1e-12, "250X"); + } + + #[test] + fn coverage_bounds_come_from_the_target_bases_only() { + let result = HsMetricsResult { + bait_set: "t".into(), + bait_territory: 5, + target_territory: 5, + genome_size: 100, + counters: HsCounters::default(), + target_depths: vec![0, 3, 7, 9, 40], + zero_coverage_targets: 0, + target_count: 1, + library_size: None, + }; + assert_eq!(result.target_coverage_bounds(), (7, 0, 40)); + } +} diff --git a/src/dna/insert_size.rs b/src/dna/insert_size.rs new file mode 100644 index 00000000..5ebd179e --- /dev/null +++ b/src/dna/insert_size.rs @@ -0,0 +1,489 @@ +//! Picard `CollectInsertSizeMetrics` reimplementation. +//! +//! # Upstream semantics +//! +//! Every rule below was measured against Picard 3.4.0 output on +//! `tests/data/dna/test.dna.bam`, not recalled from documentation. +//! +//! A record contributes when it is paired, is neither secondary, +//! supplementary, duplicate-flagged nor unmapped, has a mapped mate, and +//! carries a positive `TLEN`. Taking only the positive `TLEN` of the two is +//! what counts each pair once. Proper-pair is deliberately **not** required: +//! requiring it drops one pair and shortens the maximum from 300 to 239 on the +//! project fixture. +//! +//! Pairs are grouped by orientation (`FR`, `RF`, `TANDEM`), each group +//! reported on its own row with its own histogram, exactly as Picard does. +//! +//! `MEAN_INSERT_SIZE` and `STANDARD_DEVIATION` are computed over the +//! histogram trimmed to `DEVIATIONS` median absolute deviations either side of +//! the median, and the standard deviation uses the `n - 1` denominator. +//! `MIN_INSERT_SIZE` and `MAX_INSERT_SIZE` are over the untrimmed set. +//! +//! `WIDTH_OF_XX_PERCENT` is the width of the smallest window centred on the +//! median that covers at least `XX` percent of pairs: grow `i` from zero until +//! the bins from `median - i` to `median + i` cover the target, then report +//! `2i + 1`. + +use std::collections::BTreeMap; +use std::io::Write; +use std::path::Path; + +use anyhow::{Context, Result}; +use rust_htslib::bam; + +use crate::common::bam_flags::*; + +/// Percentiles Picard reports a width for, in output order. +pub const WIDTH_PERCENTILES: [u32; 11] = [10, 20, 30, 40, 50, 60, 70, 80, 90, 95, 99]; + +/// Picard's `DEVIATIONS` default: how many median absolute deviations either +/// side of the median survive trimming before the mean and standard deviation +/// are computed. +pub const DEFAULT_DEVIATIONS: f64 = 10.0; + +/// Relative orientation of the two mates of a pair. +#[derive(Debug, Clone, Copy, PartialEq, Eq, PartialOrd, Ord)] +pub enum PairOrientation { + /// Forward-reverse, the usual Illumina paired-end arrangement. + Fr, + /// Reverse-forward, seen in mate-pair and some capture libraries. + Rf, + /// Both mates on the same strand. + Tandem, +} + +impl PairOrientation { + /// The label Picard writes in the `PAIR_ORIENTATION` column. + pub fn label(&self) -> &'static str { + match self { + PairOrientation::Fr => "FR", + PairOrientation::Rf => "RF", + PairOrientation::Tandem => "TANDEM", + } + } + + /// The prefix Picard uses for this orientation's histogram column. + fn histogram_column(&self) -> &'static str { + match self { + PairOrientation::Fr => "fr", + PairOrientation::Rf => "rf", + PairOrientation::Tandem => "tandem", + } + } +} + +/// Accumulates insert sizes, one histogram per orientation. +#[derive(Debug, Default)] +pub struct InsertSizeAccum { + histograms: BTreeMap>, +} + +impl InsertSizeAccum { + /// A new, empty accumulator. + pub fn new() -> Self { + Self::default() + } + + /// Offer one record. Records that do not represent a countable pair are + /// ignored. + pub fn process_read(&mut self, record: &bam::Record) { + let flags = record.flags(); + if flags & BAM_FPAIRED == 0 { + return; + } + let excluded = BAM_FUNMAP | BAM_FMUNMAP | BAM_FSECONDARY | BAM_FSUPPLEMENTARY | BAM_FDUP; + if flags & excluded != 0 { + return; + } + // Only the mate carrying the positive TLEN counts, so each pair is + // counted once. + let insert_size = record.insert_size(); + if insert_size <= 0 { + return; + } + + let orientation = orientation_of(flags); + *self + .histograms + .entry(orientation) + .or_default() + .entry(insert_size as u64) + .or_insert(0) += 1; + } + + /// Fold another accumulator into this one. + pub fn merge(&mut self, other: InsertSizeAccum) { + for (orientation, histogram) in other.histograms { + let target = self.histograms.entry(orientation).or_default(); + for (size, count) in histogram { + *target.entry(size).or_insert(0) += count; + } + } + } + + /// Summarise each orientation, in Picard's output order: most pairs first. + pub fn into_result(self, deviations: f64) -> InsertSizeResult { + let mut rows: Vec = self + .histograms + .into_iter() + .map(|(orientation, histogram)| InsertSizeRow::new(orientation, histogram, deviations)) + .collect(); + rows.sort_by_key(|row| std::cmp::Reverse(row.read_pairs)); + InsertSizeResult { rows } + } +} + +/// Which orientation a record's flags describe. +fn orientation_of(flags: u16) -> PairOrientation { + let read_reverse = flags & BAM_FREVERSE != 0; + let mate_reverse = flags & BAM_FMREVERSE != 0; + if read_reverse == mate_reverse { + PairOrientation::Tandem + } else if read_reverse { + // This record carries the positive TLEN, so it is the leftmost mate. + // Leftmost on the reverse strand means reverse-forward. + PairOrientation::Rf + } else { + PairOrientation::Fr + } +} + +/// One orientation's metrics and histogram. +#[derive(Debug, Clone)] +pub struct InsertSizeRow { + /// The orientation this row describes. + pub orientation: PairOrientation, + /// Insert size histogram, size to pair count. + pub histogram: BTreeMap, + /// Number of pairs counted. + pub read_pairs: u64, + /// Median insert size. + pub median: u64, + /// Most frequent insert size; ties go to the smaller size. + pub mode: u64, + /// Median absolute deviation from the median. + pub median_absolute_deviation: u64, + /// Smallest insert size seen, before trimming. + pub min: u64, + /// Largest insert size seen, before trimming. + pub max: u64, + /// Mean over the trimmed histogram. + pub mean: f64, + /// Standard deviation over the trimmed histogram, `n - 1` denominator. + pub standard_deviation: f64, + /// Width of the smallest median-centred window covering each percentile, + /// in the order of [`WIDTH_PERCENTILES`]. + pub widths: Vec, +} + +impl InsertSizeRow { + fn new(orientation: PairOrientation, histogram: BTreeMap, deviations: f64) -> Self { + let read_pairs: u64 = histogram.values().sum(); + let median = quantile(&histogram, read_pairs / 2); + let mode = histogram + .iter() + .max_by_key(|(size, count)| (**count, std::cmp::Reverse(**size))) + .map(|(size, _)| *size) + .unwrap_or(0); + + // Median absolute deviation, itself a median over |size - median|. + let mut deviation_histogram: BTreeMap = BTreeMap::new(); + for (size, count) in &histogram { + let deviation = size.abs_diff(median); + *deviation_histogram.entry(deviation).or_insert(0) += count; + } + let median_absolute_deviation = quantile(&deviation_histogram, read_pairs / 2); + + let min = histogram.keys().copied().min().unwrap_or(0); + let max = histogram.keys().copied().max().unwrap_or(0); + + // Trim to `deviations` MADs either side before the mean and SD. + let span = deviations * median_absolute_deviation as f64; + let low = (median as f64 - span).max(0.0); + let high = median as f64 + span; + let trimmed: Vec<(u64, u64)> = histogram + .iter() + .filter(|(size, _)| **size as f64 >= low && **size as f64 <= high) + .map(|(size, count)| (*size, *count)) + .collect(); + + let n: u64 = trimmed.iter().map(|(_, count)| count).sum(); + let mean = if n == 0 { + 0.0 + } else { + trimmed + .iter() + .map(|(size, count)| *size as f64 * *count as f64) + .sum::() + / n as f64 + }; + let standard_deviation = if n < 2 { + 0.0 + } else { + let variance = trimmed + .iter() + .map(|(size, count)| { + let diff = *size as f64 - mean; + diff * diff * *count as f64 + }) + .sum::() + / (n - 1) as f64; + variance.sqrt() + }; + + let widths = WIDTH_PERCENTILES + .iter() + .map(|pct| width_of_percent(&histogram, median, read_pairs, *pct)) + .collect(); + + Self { + orientation, + histogram, + read_pairs, + median, + mode, + median_absolute_deviation, + min, + max, + mean, + standard_deviation, + widths, + } + } +} + +/// The value at `rank` when the histogram is expanded into a sorted list. +fn quantile(histogram: &BTreeMap, rank: u64) -> u64 { + let mut seen = 0u64; + for (value, count) in histogram { + seen += count; + if seen > rank { + return *value; + } + } + histogram.keys().next_back().copied().unwrap_or(0) +} + +/// Width of the smallest window centred on `median` covering `pct` percent of +/// `total` pairs. +fn width_of_percent(histogram: &BTreeMap, median: u64, total: u64, pct: u32) -> u64 { + if total == 0 { + return 0; + } + let target = total as f64 * pct as f64 / 100.0; + let mut covered = *histogram.get(&median).unwrap_or(&0) as f64; + let mut i = 0u64; + while covered < target { + i += 1; + covered += *histogram.get(&(median.saturating_sub(i))).unwrap_or(&0) as f64; + covered += *histogram.get(&(median + i)).unwrap_or(&0) as f64; + // Once the window spans the whole histogram there is nothing left to add. + if median + i > *histogram.keys().next_back().unwrap_or(&0) && median < i { + break; + } + } + 2 * i + 1 +} + +/// All orientations' metrics for one alignment file. +#[derive(Debug, Clone)] +pub struct InsertSizeResult { + /// One row per orientation seen, most pairs first. + pub rows: Vec, +} + +/// Format a float the way Picard's metrics writer does: up to six decimals, +/// trailing zeros removed, and a bare integer when there is no fraction. +fn fmt_picard(value: f64) -> String { + if value == value.trunc() && value.abs() < 1e15 { + return format!("{}", value as i64); + } + let text = format!("{value:.6}"); + let trimmed = text.trim_end_matches('0').trim_end_matches('.'); + trimmed.to_string() +} + +/// Write a Picard-compatible `insert_size_metrics.txt`. +/// +/// The `## htsjdk...StringHeader` preamble Picard writes is omitted: it holds +/// only the command line and a start timestamp, both of which are noise in a +/// reproducible pipeline. +pub fn write_insert_size_metrics(result: &InsertSizeResult, path: &Path) -> Result<()> { + let mut out = std::fs::File::create(path) + .map(std::io::BufWriter::new) + .with_context(|| format!("Failed to create insert size metrics: {}", path.display()))?; + + writeln!(out, "## METRICS CLASS\tpicard.analysis.InsertSizeMetrics")?; + write!( + out, + "MEDIAN_INSERT_SIZE\tMODE_INSERT_SIZE\tMEDIAN_ABSOLUTE_DEVIATION\tMIN_INSERT_SIZE\t\ + MAX_INSERT_SIZE\tMEAN_INSERT_SIZE\tSTANDARD_DEVIATION\tREAD_PAIRS\tPAIR_ORIENTATION" + )?; + for pct in WIDTH_PERCENTILES { + write!(out, "\tWIDTH_OF_{pct}_PERCENT")?; + } + writeln!(out, "\tSAMPLE\tLIBRARY\tREAD_GROUP")?; + + for row in &result.rows { + write!( + out, + "{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}", + row.median, + row.mode, + row.median_absolute_deviation, + row.min, + row.max, + fmt_picard(row.mean), + fmt_picard(row.standard_deviation), + row.read_pairs, + row.orientation.label(), + )?; + for width in &row.widths { + write!(out, "\t{width}")?; + } + // Trailing SAMPLE, LIBRARY and READ_GROUP columns are empty at the + // ALL_READS accumulation level, which is Picard's default. + writeln!(out, "\t\t\t")?; + } + + writeln!(out)?; + writeln!(out, "## HISTOGRAM\tjava.lang.Integer")?; + write!(out, "insert_size")?; + for row in &result.rows { + write!( + out, + "\tAll_Reads.{}_count", + row.orientation.histogram_column() + )?; + } + writeln!(out)?; + + // One row per insert size seen in any orientation, ascending. + let mut sizes: Vec = result + .rows + .iter() + .flat_map(|row| row.histogram.keys().copied()) + .collect(); + sizes.sort_unstable(); + sizes.dedup(); + for size in sizes { + write!(out, "{size}")?; + for row in &result.rows { + write!(out, "\t{}", row.histogram.get(&size).copied().unwrap_or(0))?; + } + writeln!(out)?; + } + writeln!(out)?; + + out.flush()?; + Ok(()) +} + +#[cfg(test)] +mod tests { + use super::*; + + fn hist(pairs: &[(u64, u64)]) -> BTreeMap { + pairs.iter().copied().collect() + } + + #[test] + fn orientation_follows_the_strand_flags() { + // The record carrying the positive TLEN is the leftmost mate, so its + // own strand decides between FR and RF. + assert_eq!( + orientation_of(BAM_FPAIRED | BAM_FMREVERSE), + PairOrientation::Fr, + "leftmost forward, mate reverse" + ); + assert_eq!( + orientation_of(BAM_FPAIRED | BAM_FREVERSE), + PairOrientation::Rf, + "leftmost reverse, mate forward" + ); + // Same strand either way round is tandem, including neither reversed. + assert_eq!( + orientation_of(BAM_FPAIRED), + PairOrientation::Tandem, + "both forward" + ); + assert_eq!( + orientation_of(BAM_FPAIRED | BAM_FREVERSE | BAM_FMREVERSE), + PairOrientation::Tandem, + "both reverse" + ); + } + + #[test] + fn mode_breaks_ties_towards_the_smaller_size() { + let row = InsertSizeRow::new( + PairOrientation::Fr, + hist(&[(100, 5), (200, 5)]), + DEFAULT_DEVIATIONS, + ); + assert_eq!(row.mode, 100); + } + + #[test] + fn standard_deviation_uses_the_sample_denominator() { + // Values 1, 2, 3: mean 2, sample variance 1, so SD is exactly 1. + let row = InsertSizeRow::new( + PairOrientation::Fr, + hist(&[(1, 1), (2, 1), (3, 1)]), + DEFAULT_DEVIATIONS, + ); + assert!((row.mean - 2.0).abs() < 1e-12); + assert!( + (row.standard_deviation - 1.0).abs() < 1e-12, + "got {}", + row.standard_deviation + ); + } + + #[test] + fn trimming_excludes_outliers_beyond_the_deviation_span() { + // Median 10, MAD 0, so a span of zero keeps only the median bin. + let row = InsertSizeRow::new( + PairOrientation::Fr, + hist(&[(10, 9), (1000, 1)]), + DEFAULT_DEVIATIONS, + ); + assert_eq!(row.max, 1000, "the untrimmed maximum is still reported"); + assert!( + (row.mean - 10.0).abs() < 1e-12, + "the outlier must not reach the mean, got {}", + row.mean + ); + } + + #[test] + fn width_grows_symmetrically_around_the_median() { + // Ten pairs at the median, five either side one apart. + let h = hist(&[(9, 5), (10, 10), (11, 5)]); + assert_eq!(width_of_percent(&h, 10, 20, 50), 1, "the median bin alone"); + assert_eq!(width_of_percent(&h, 10, 20, 90), 3, "one bin either side"); + } + + #[test] + fn picard_float_formatting_drops_trailing_zeros() { + assert_eq!(fmt_picard(124.442269), "124.442269"); + assert_eq!(fmt_picard(3.5), "3.5"); + assert_eq!(fmt_picard(40001.0), "40001"); + assert_eq!(fmt_picard(0.0), "0"); + } + + #[test] + fn rows_are_ordered_by_pair_count() { + let mut accum = InsertSizeAccum::new(); + accum + .histograms + .insert(PairOrientation::Rf, hist(&[(100, 1)])); + accum + .histograms + .insert(PairOrientation::Fr, hist(&[(100, 50)])); + let result = accum.into_result(DEFAULT_DEVIATIONS); + assert_eq!(result.rows[0].orientation, PairOrientation::Fr); + assert_eq!(result.rows[1].orientation, PairOrientation::Rf); + } +} diff --git a/src/dna/intervals.rs b/src/dna/intervals.rs new file mode 100644 index 00000000..f89af490 --- /dev/null +++ b/src/dna/intervals.rs @@ -0,0 +1,262 @@ +//! BED interval parsing and merging for targeted mode. +//! +//! Picard consumes `.interval_list` files, RustQC accepts BED. The two differ +//! in a way that is easy to get wrong: BED is zero-based half-open, an +//! interval list is one-based inclusive, so `chr22 1 15000` in BED is +//! `chr22 2 15000` in an interval list. Everything here works in BED's +//! convention internally and converts only at the edges. + +use std::collections::HashMap; +use std::path::Path; + +use anyhow::{bail, Context, Result}; + +/// A half-open interval `[start, end)` on one contig, zero-based. +#[derive(Debug, Clone, Copy, PartialEq, Eq, PartialOrd, Ord)] +pub struct Interval { + /// Zero-based inclusive start. + pub start: u64, + /// Zero-based exclusive end. + pub end: u64, +} + +impl Interval { + /// Number of bases covered. + pub fn len(&self) -> u64 { + self.end.saturating_sub(self.start) + } + + /// Whether the interval covers no bases. + pub fn is_empty(&self) -> bool { + self.len() == 0 + } + + /// Whether `position` falls inside. + pub fn contains(&self, position: u64) -> bool { + position >= self.start && position < self.end + } +} + +/// Merged, sorted intervals grouped by contig. +#[derive(Debug, Clone, Default)] +pub struct IntervalSet { + /// Non-overlapping intervals per contig, ascending. + by_contig: HashMap>, + /// A name for the set, used as `BAIT_SET` in the metrics. + name: String, +} + +impl IntervalSet { + /// Read a BED file, merging any overlapping or touching intervals. + /// + /// Merging matters: overlapping targets would otherwise inflate the + /// territory and double-count on-target bases. + pub fn from_bed(path: &Path) -> Result { + let text = std::fs::read_to_string(path) + .with_context(|| format!("Failed to read BED file: {}", path.display()))?; + + let name = path + .file_stem() + .and_then(|s| s.to_str()) + .unwrap_or("targets") + .to_string(); + + let mut raw: HashMap> = HashMap::new(); + for (number, line) in text.lines().enumerate() { + let line = line.trim(); + if line.is_empty() + || line.starts_with('#') + || line.starts_with("track") + || line.starts_with("browser") + { + continue; + } + let fields: Vec<&str> = line.split('\t').collect(); + if fields.len() < 3 { + bail!( + "{}: line {} has {} fields, a BED interval needs at least 3", + path.display(), + number + 1, + fields.len() + ); + } + let start: u64 = fields[1].parse().with_context(|| { + format!("{}: line {} has a bad start", path.display(), number + 1) + })?; + let end: u64 = fields[2].parse().with_context(|| { + format!("{}: line {} has a bad end", path.display(), number + 1) + })?; + if end <= start { + bail!( + "{}: line {} ends at or before it starts", + path.display(), + number + 1 + ); + } + raw.entry(fields[0].to_string()) + .or_default() + .push(Interval { start, end }); + } + + let by_contig = raw + .into_iter() + .map(|(contig, intervals)| (contig, merge(intervals))) + .collect(); + + Ok(Self { by_contig, name }) + } + + /// Build directly from intervals, for tests and for deriving one set from + /// another. + pub fn from_intervals(name: &str, by_contig: HashMap>) -> Self { + Self { + by_contig: by_contig + .into_iter() + .map(|(contig, intervals)| (contig, merge(intervals))) + .collect(), + name: name.to_string(), + } + } + + /// The set's name, reported as `BAIT_SET`. + pub fn name(&self) -> &str { + &self.name + } + + /// Total bases covered across every contig. + pub fn territory(&self) -> u64 { + self.by_contig + .values() + .flat_map(|intervals| intervals.iter()) + .map(|interval| interval.len()) + .sum() + } + + /// Intervals on one contig, ascending, or an empty slice. + pub fn on(&self, contig: &str) -> &[Interval] { + self.by_contig + .get(contig) + .map(|v| v.as_slice()) + .unwrap_or(&[]) + } + + /// Total number of intervals. + pub fn len(&self) -> usize { + self.by_contig.values().map(|v| v.len()).sum() + } + + /// Whether the set holds no intervals. + pub fn is_empty(&self) -> bool { + self.len() == 0 + } + + /// A per-base membership mask for one contig, for fast position lookup in + /// the inner loop. + pub fn mask(&self, contig: &str, length: u64) -> Vec { + let mut mask = vec![false; length as usize]; + for interval in self.on(contig) { + let start = interval.start.min(length) as usize; + let end = interval.end.min(length) as usize; + mask[start..end].fill(true); + } + mask + } +} + +/// Sort and merge overlapping or adjacent intervals. +fn merge(mut intervals: Vec) -> Vec { + intervals.sort(); + let mut merged: Vec = Vec::with_capacity(intervals.len()); + for interval in intervals { + match merged.last_mut() { + Some(last) if interval.start <= last.end => { + last.end = last.end.max(interval.end); + } + _ => merged.push(interval), + } + } + merged +} + +#[cfg(test)] +mod tests { + use super::*; + + fn write_bed(name: &str, contents: &str) -> std::path::PathBuf { + let dir = std::env::temp_dir().join("rustqc-interval-tests"); + std::fs::create_dir_all(&dir).unwrap(); + let path = dir.join(name); + std::fs::write(&path, contents).unwrap(); + path + } + + #[test] + fn overlapping_intervals_are_merged() { + let path = write_bed( + "overlap.bed", + "chr1\t100\t200\nchr1\t150\t300\nchr1\t400\t500\n", + ); + let set = IntervalSet::from_bed(&path).unwrap(); + assert_eq!( + set.on("chr1"), + &[ + Interval { + start: 100, + end: 300 + }, + Interval { + start: 400, + end: 500 + }, + ] + ); + assert_eq!(set.territory(), 300, "merged, not 100 + 150 + 100"); + } + + #[test] + fn touching_intervals_are_merged_too() { + let path = write_bed("touch.bed", "chr1\t100\t200\nchr1\t200\t300\n"); + let set = IntervalSet::from_bed(&path).unwrap(); + assert_eq!( + set.on("chr1"), + &[Interval { + start: 100, + end: 300 + }] + ); + } + + #[test] + fn comments_and_track_lines_are_ignored() { + let path = write_bed( + "comments.bed", + "# a comment\ntrack name=x\nchr1\t10\t20\n\nbrowser position chr1\n", + ); + let set = IntervalSet::from_bed(&path).unwrap(); + assert_eq!(set.len(), 1); + } + + #[test] + fn a_backwards_interval_is_an_error_rather_than_silently_empty() { + let path = write_bed("backwards.bed", "chr1\t200\t100\n"); + let result = IntervalSet::from_bed(&path); + assert!(result.is_err(), "an end before the start must be rejected"); + } + + #[test] + fn the_mask_marks_exactly_the_covered_bases() { + let path = write_bed("mask.bed", "chr1\t2\t5\n"); + let set = IntervalSet::from_bed(&path).unwrap(); + assert_eq!( + set.mask("chr1", 8), + vec![false, false, true, true, true, false, false, false] + ); + } + + #[test] + fn intervals_beyond_the_contig_end_do_not_overflow_the_mask() { + let path = write_bed("beyond.bed", "chr1\t2\t100\n"); + let set = IntervalSet::from_bed(&path).unwrap(); + assert_eq!(set.mask("chr1", 4), vec![false, false, true, true]); + } +} diff --git a/src/dna/mod.rs b/src/dna/mod.rs new file mode 100644 index 00000000..d924de1c --- /dev/null +++ b/src/dna/mod.rs @@ -0,0 +1,15 @@ +//! DNA quality control and analysis modules. +//! +//! Contains the depth of coverage engine and the mosdepth-compatible outputs +//! built on top of it. Read-level statistics, the samtools-compatible writers +//! and preseq are shared with the RNA pipeline and live in [`crate::common`]. + +pub mod depth; +pub mod gc_bias; +pub mod hs_metrics; +pub mod insert_size; +pub mod intervals; +pub mod mosdepth; +pub mod qualimap; +pub mod qualimap_output; +pub mod wgs_metrics; diff --git a/src/dna/mosdepth/mod.rs b/src/dna/mosdepth/mod.rs new file mode 100644 index 00000000..b8fe1f8f --- /dev/null +++ b/src/dna/mosdepth/mod.rs @@ -0,0 +1,427 @@ +//! mosdepth-compatible depth of coverage results. +//! +//! [`ContigDepth::from_depths`] turns one contig's per-base depth vector into +//! everything the six mosdepth outputs need, in a single pass over the vector, +//! so the depth vector can be dropped as soon as the contig is done. +//! +//! # Output formats +//! +//! These were derived from mosdepth 0.3.14 output committed under +//! `tests/expected/dna/`, not from documentation, and every rule below was +//! checked against every row of those fixtures. +//! +//! `{prefix}.mosdepth.summary.txt` carries the header +//! `chrom length bases mean min max`, one row per contig, then one +//! `{contig}_region` row per contig when windows were requested, then `total` +//! and `total_region`. `mean` is `bases / length` to two decimals. +//! +//! `{prefix}.mosdepth.global.dist.txt` and `.region.dist.txt` carry +//! `chrom depth proportion` rows in descending depth order, where `proportion` +//! is the fraction at depth **at or above** `depth`, formatted to two +//! decimals, ending at depth 0 with `1.00`. Which depths get a row is the +//! non-obvious part: +//! +//! - depths 0 through [`DIST_DENSE_MAX`] always get a row, even when no base +//! sits at that exact depth; +//! - above that, only depths that actually occur; +//! - the maximum observed depth never gets a row. +//! +//! The global distribution is over bases and their exact depth; the region +//! distribution is over windows and their **rounded** mean depth. + +use std::collections::BTreeMap; + +pub mod output; + +/// Highest depth that always gets a distribution row, matching the size of +/// mosdepth's internal fixed depth array. +pub const DIST_DENSE_MAX: u32 = 300; + +/// A run of consecutive bases sharing one depth, as written to `per-base.bed.gz`. +#[derive(Debug, Clone, PartialEq, Eq)] +pub struct DepthRun { + /// Zero-based, inclusive start. + pub start: u64, + /// Zero-based, exclusive end. + pub end: u64, + /// Depth shared by every base in the run. + pub depth: u32, +} + +/// A fixed-width window and its mean depth, as written to `regions.bed.gz`. +#[derive(Debug, Clone, PartialEq)] +pub struct WindowDepth { + /// Zero-based, inclusive start. + pub start: u64, + /// Zero-based, exclusive end. + pub end: u64, + /// Mean depth over the window. + pub mean: f64, +} + +/// One window's per-threshold counts, as written to `thresholds.bed.gz`. +#[derive(Debug, Clone, PartialEq, Eq)] +pub struct ThresholdRow { + /// Zero-based, inclusive start. + pub start: u64, + /// Zero-based, exclusive end. + pub end: u64, + /// Bases at or above each requested threshold, in the requested order. + pub counts: Vec, +} + +/// Everything the mosdepth outputs need about one contig. +#[derive(Debug, Clone)] +pub struct ContigDepth { + /// Contig name as it appears in the alignment header. + pub name: String, + /// Contig length in bases. + pub length: u64, + /// Sum of per-base depth over the contig. + pub total_bases: u64, + /// Lowest per-base depth seen. + pub min: u32, + /// Highest per-base depth seen. + pub max: u32, + /// Base count per exact depth. + pub histogram: BTreeMap, + /// Collapsed runs of equal depth. + pub runs: Vec, + /// Per-window mean depth; empty when no window size was requested. + pub windows: Vec, + /// Per-window threshold counts; empty when no thresholds were requested. + pub thresholds: Vec, +} + +impl ContigDepth { + /// Summarise one contig's per-base depths in a single pass. + pub fn from_depths( + name: &str, + depths: &[u32], + window_size: Option, + thresholds: &[u32], + ) -> Self { + let length = depths.len() as u64; + let mut histogram: BTreeMap = BTreeMap::new(); + let mut runs: Vec = Vec::new(); + let mut total_bases = 0u64; + + for (i, &depth) in depths.iter().enumerate() { + total_bases += u64::from(depth); + *histogram.entry(depth).or_insert(0) += 1; + match runs.last_mut() { + Some(run) if run.depth == depth => run.end = i as u64 + 1, + _ => runs.push(DepthRun { + start: i as u64, + end: i as u64 + 1, + depth, + }), + } + } + + let min = depths.iter().copied().min().unwrap_or(0); + let max = depths.iter().copied().max().unwrap_or(0); + + let (windows, threshold_rows) = match window_size { + Some(size) if size > 0 => Self::windowed(depths, u64::from(size), thresholds), + _ => (Vec::new(), Vec::new()), + }; + + Self { + name: name.to_string(), + length, + total_bases, + min, + max, + histogram, + runs, + windows, + thresholds: threshold_rows, + } + } + + /// Split the contig into fixed-width windows, computing each window's mean + /// depth and its per-threshold base counts. + fn windowed( + depths: &[u32], + size: u64, + thresholds: &[u32], + ) -> (Vec, Vec) { + let mut windows = Vec::new(); + let mut rows = Vec::new(); + for (index, chunk) in depths.chunks(size as usize).enumerate() { + let start = index as u64 * size; + let end = start + chunk.len() as u64; + let sum: u64 = chunk.iter().map(|d| u64::from(*d)).sum(); + windows.push(WindowDepth { + start, + end, + mean: sum as f64 / chunk.len() as f64, + }); + if !thresholds.is_empty() { + let counts = thresholds + .iter() + .map(|t| chunk.iter().filter(|d| *d >= t).count() as u64) + .collect(); + rows.push(ThresholdRow { start, end, counts }); + } + } + (windows, rows) + } + + /// Mean depth over the contig. + pub fn mean(&self) -> f64 { + if self.length == 0 { + 0.0 + } else { + self.total_bases as f64 / self.length as f64 + } + } + + /// Histogram of window mean depths, rounded to the nearest integer, which + /// is what the region distribution is built from. + pub fn region_histogram(&self) -> BTreeMap { + let mut hist = BTreeMap::new(); + for window in &self.windows { + let key = window.mean.round().max(0.0) as u32; + *hist.entry(key).or_insert(0) += 1; + } + hist + } +} + +/// The mosdepth result for one alignment file. +#[derive(Debug, Clone)] +pub struct MosdepthResult { + /// Per-contig results, in alignment-header order. + pub contigs: Vec, + /// Window size, when per-window output was requested. + pub window_size: Option, + /// Requested coverage thresholds, in the order they are reported. + pub thresholds: Vec, +} + +impl MosdepthResult { + /// Total length across all contigs. + pub fn total_length(&self) -> u64 { + self.contigs.iter().map(|c| c.length).sum() + } + + /// Total covered bases across all contigs. + pub fn total_bases(&self) -> u64 { + self.contigs.iter().map(|c| c.total_bases).sum() + } + + /// Mean depth across all contigs. + pub fn mean(&self) -> f64 { + let length = self.total_length(); + if length == 0 { + 0.0 + } else { + self.total_bases() as f64 / length as f64 + } + } + + /// Lowest depth across all contigs. + pub fn min(&self) -> u32 { + self.contigs.iter().map(|c| c.min).min().unwrap_or(0) + } + + /// Highest depth across all contigs. + pub fn max(&self) -> u32 { + self.contigs.iter().map(|c| c.max).max().unwrap_or(0) + } +} + +/// Merge histograms element-wise. +pub fn merge_histograms<'a>( + parts: impl IntoIterator>, +) -> BTreeMap { + let mut merged = BTreeMap::new(); + for part in parts { + for (depth, count) in part { + *merged.entry(*depth).or_insert(0) += count; + } + } + merged +} + +/// The depths that get a distribution row, in descending order. +/// +/// The rule was derived from the committed fixtures and holds for both +/// distribution files: every depth from 0 up to `min(DIST_DENSE_MAX, max)` +/// gets a row whether or not anything sits at it, and above +/// [`DIST_DENSE_MAX`] only depths that actually occur and lie strictly below +/// the maximum do. +/// +/// The consequence worth stating plainly: the maximum observed depth gets a +/// row when it falls inside the dense range and no row when it does not. On +/// the project fixture the global distribution tops out at 866 with a maximum +/// of 867, while the region distribution does emit its maximum of 204. +pub fn dist_rows(histogram: &BTreeMap) -> Vec { + let observed_max = histogram + .iter() + .filter(|(_, count)| **count > 0) + .map(|(depth, _)| *depth) + .max() + .unwrap_or(0); + + let mut depths: Vec = histogram + .iter() + .filter(|(depth, count)| **count > 0 && **depth > DIST_DENSE_MAX && **depth < observed_max) + .map(|(depth, _)| *depth) + .collect(); + depths.extend(0..=DIST_DENSE_MAX.min(observed_max)); + depths.sort_unstable_by(|a, b| b.cmp(a)); + depths.dedup(); + depths +} + +/// Cumulative proportion at or above each depth in `rows`, given `histogram` +/// and a total to divide by. +pub fn dist_proportions(histogram: &BTreeMap, rows: &[u32], total: u64) -> Vec { + if total == 0 { + return vec![0.0; rows.len()]; + } + rows.iter() + .map(|threshold| { + let at_or_above: u64 = histogram + .iter() + .filter(|(depth, _)| *depth >= threshold) + .map(|(_, count)| count) + .sum(); + at_or_above as f64 / total as f64 + }) + .collect() +} + +#[cfg(test)] +mod tests { + use super::*; + + #[test] + fn runs_collapse_equal_neighbours() { + let c = ContigDepth::from_depths("chr1", &[0, 0, 0, 2, 2, 1], None, &[]); + assert_eq!( + c.runs, + vec![ + DepthRun { + start: 0, + end: 3, + depth: 0 + }, + DepthRun { + start: 3, + end: 5, + depth: 2 + }, + DepthRun { + start: 5, + end: 6, + depth: 1 + }, + ] + ); + } + + #[test] + fn summary_figures_are_computed_over_the_whole_contig() { + let c = ContigDepth::from_depths("chr1", &[0, 0, 3, 5], None, &[]); + assert_eq!(c.length, 4); + assert_eq!(c.total_bases, 8); + assert_eq!(c.min, 0); + assert_eq!(c.max, 5); + assert!((c.mean() - 2.0).abs() < 1e-12); + } + + #[test] + fn windows_cover_the_tail_even_when_shorter_than_the_window() { + let c = ContigDepth::from_depths("chr1", &[4, 4, 4, 4, 10], Some(4), &[]); + assert_eq!(c.windows.len(), 2); + assert_eq!( + c.windows[0], + WindowDepth { + start: 0, + end: 4, + mean: 4.0 + } + ); + assert_eq!( + c.windows[1], + WindowDepth { + start: 4, + end: 5, + mean: 10.0 + } + ); + } + + #[test] + fn threshold_counts_are_at_or_above_each_threshold() { + let c = ContigDepth::from_depths("chr1", &[0, 1, 5, 10], Some(4), &[1, 5, 20]); + assert_eq!(c.thresholds.len(), 1); + assert_eq!(c.thresholds[0].counts, vec![3, 2, 0]); + } + + #[test] + fn dist_rows_emit_a_maximum_that_falls_inside_the_dense_range() { + let mut hist = BTreeMap::new(); + hist.insert(0u32, 10u64); + hist.insert(204, 1); // the maximum, but below DIST_DENSE_MAX + let rows = dist_rows(&hist); + assert_eq!( + rows.first(), + Some(&204), + "a maximum inside the dense range is emitted" + ); + assert_eq!(rows.len(), 205, "0 through 204 inclusive"); + } + + #[test] + fn dist_rows_skip_a_maximum_above_the_dense_range() { + let mut hist = BTreeMap::new(); + hist.insert(0u32, 10u64); + hist.insert(5, 2); + hist.insert(400, 1); + hist.insert(500, 1); // the maximum, never emitted + let rows = dist_rows(&hist); + assert!(!rows.contains(&500), "the maximum depth gets no row"); + assert!( + rows.contains(&400), + "an observed depth above the dense range does" + ); + assert!( + rows.contains(&7), + "an unobserved depth inside the dense range does" + ); + assert!( + !rows.contains(&350), + "an unobserved depth above the dense range does not" + ); + assert_eq!(rows.first(), Some(&400), "descending order"); + assert_eq!(rows.last(), Some(&0), "down to zero"); + } + + #[test] + fn dist_proportions_are_cumulative_from_the_top() { + let mut hist = BTreeMap::new(); + hist.insert(0u32, 2u64); + hist.insert(1, 1); + hist.insert(3, 1); + let rows = vec![3u32, 2, 1, 0]; + let props = dist_proportions(&hist, &rows, 4); + assert!((props[0] - 0.25).abs() < 1e-12); + assert!((props[1] - 0.25).abs() < 1e-12); + assert!((props[2] - 0.50).abs() < 1e-12); + assert!((props[3] - 1.00).abs() < 1e-12); + } + + #[test] + fn region_histogram_rounds_window_means() { + let c = ContigDepth::from_depths("chr1", &[1, 2, 2, 3], Some(2), &[]); + // Windows: mean 1.5 rounds to 2, mean 2.5 rounds to 3 (away from zero). + let hist = c.region_histogram(); + assert_eq!(hist.get(&2), Some(&1)); + assert_eq!(hist.get(&3), Some(&1)); + } +} diff --git a/src/dna/mosdepth/output.rs b/src/dna/mosdepth/output.rs new file mode 100644 index 00000000..78e4ccb0 --- /dev/null +++ b/src/dna/mosdepth/output.rs @@ -0,0 +1,337 @@ +//! Writers for the six mosdepth-compatible output files. +//! +//! Formats are documented in the parent module. Compressed outputs are written +//! as bgzf, which is what mosdepth writes and what both `tabix` and `gunzip` +//! read. Parity against the fixtures is therefore asserted on the decompressed +//! bytes: two bgzf writers at the same level need not emit identical +//! compressed bytes, so comparing the `.gz` byte for byte would be testing the +//! compressor rather than this code. + +use std::io::Write; +use std::path::Path; + +use anyhow::{bail, Context, Result}; +use rust_htslib::bgzf; + +use super::{dist_proportions, dist_rows, merge_histograms, MosdepthResult}; + +/// Write `{prefix}.mosdepth.summary.txt`. +pub fn write_summary(result: &MosdepthResult, path: &Path) -> Result<()> { + let mut out = std::fs::File::create(path) + .map(std::io::BufWriter::new) + .with_context(|| format!("Failed to create summary file: {}", path.display()))?; + + writeln!(out, "chrom\tlength\tbases\tmean\tmin\tmax")?; + for contig in &result.contigs { + writeln!( + out, + "{}\t{}\t{}\t{:.2}\t{}\t{}", + contig.name, + contig.length, + contig.total_bases, + contig.mean(), + contig.min, + contig.max + )?; + if result.window_size.is_some() { + writeln!( + out, + "{}_region\t{}\t{}\t{:.2}\t{}\t{}", + contig.name, + contig.length, + contig.total_bases, + contig.mean(), + contig.min, + contig.max + )?; + } + } + writeln!( + out, + "total\t{}\t{}\t{:.2}\t{}\t{}", + result.total_length(), + result.total_bases(), + result.mean(), + result.min(), + result.max() + )?; + if result.window_size.is_some() { + writeln!( + out, + "total_region\t{}\t{}\t{:.2}\t{}\t{}", + result.total_length(), + result.total_bases(), + result.mean(), + result.min(), + result.max() + )?; + } + out.flush()?; + Ok(()) +} + +/// Write `{prefix}.mosdepth.global.dist.txt`, the distribution over bases. +pub fn write_global_dist(result: &MosdepthResult, path: &Path) -> Result<()> { + let per_contig: Vec<_> = result + .contigs + .iter() + .map(|c| (c.name.as_str(), c.histogram.clone(), c.length)) + .collect(); + write_dist(&per_contig, path) +} + +/// Write `{prefix}.mosdepth.region.dist.txt`, the distribution over windows +/// and their rounded mean depth. +pub fn write_region_dist(result: &MosdepthResult, path: &Path) -> Result<()> { + let per_contig: Vec<_> = result + .contigs + .iter() + .map(|c| { + let hist = c.region_histogram(); + let total = hist.values().sum::(); + (c.name.as_str(), hist, total) + }) + .collect(); + write_dist(&per_contig, path) +} + +/// Shared body of both distribution writers. +fn write_dist( + per_contig: &[(&str, std::collections::BTreeMap, u64)], + path: &Path, +) -> Result<()> { + let mut out = std::fs::File::create(path) + .map(std::io::BufWriter::new) + .with_context(|| format!("Failed to create distribution file: {}", path.display()))?; + + for (name, histogram, total) in per_contig { + let rows = dist_rows(histogram); + for (depth, proportion) in rows.iter().zip(dist_proportions(histogram, &rows, *total)) { + writeln!(out, "{name}\t{depth}\t{proportion:.2}")?; + } + } + + let merged = merge_histograms(per_contig.iter().map(|(_, h, _)| h)); + let total: u64 = per_contig.iter().map(|(_, _, t)| t).sum(); + let rows = dist_rows(&merged); + for (depth, proportion) in rows.iter().zip(dist_proportions(&merged, &rows, total)) { + writeln!(out, "total\t{depth}\t{proportion:.2}")?; + } + + out.flush()?; + Ok(()) +} + +/// Write `{prefix}.per-base.bed.gz`, one line per run of equal depth. +pub fn write_per_base(result: &MosdepthResult, path: &Path) -> Result<()> { + let mut lines = Vec::new(); + for contig in &result.contigs { + for run in &contig.runs { + lines.push(format!( + "{}\t{}\t{}\t{}\n", + contig.name, run.start, run.end, run.depth + )); + } + } + write_bgzf(path, &lines.concat()) +} + +/// Write `{prefix}.regions.bed.gz`, one line per window with its mean depth. +pub fn write_regions(result: &MosdepthResult, path: &Path) -> Result<()> { + let mut lines = Vec::new(); + for contig in &result.contigs { + for window in &contig.windows { + lines.push(format!( + "{}\t{}\t{}\t{:.2}\n", + contig.name, window.start, window.end, window.mean + )); + } + } + write_bgzf(path, &lines.concat()) +} + +/// Write `{prefix}.thresholds.bed.gz`, one line per window with the number of +/// bases at or above each requested threshold. +pub fn write_thresholds(result: &MosdepthResult, path: &Path) -> Result<()> { + let mut body = String::from("#chrom\tstart\tend\tregion"); + for threshold in &result.thresholds { + body.push_str(&format!("\t{threshold}X")); + } + body.push('\n'); + + for contig in &result.contigs { + for row in &contig.thresholds { + body.push_str(&format!( + "{}\t{}\t{}\tunknown", + contig.name, row.start, row.end + )); + for count in &row.counts { + body.push_str(&format!("\t{count}")); + } + body.push('\n'); + } + } + write_bgzf(path, &body) +} + +/// Write `contents` to `path` as bgzf, then build its `.csi` index. +fn write_bgzf(path: &Path, contents: &str) -> Result<()> { + { + let mut writer = bgzf::Writer::from_path(path) + .with_context(|| format!("Failed to create bgzf file: {}", path.display()))?; + writer + .write_all(contents.as_bytes()) + .with_context(|| format!("Failed to write bgzf file: {}", path.display()))?; + // The writer must be dropped, and the bgzf stream closed, before the + // indexer reads the file back. + } + build_csi_index(path) +} + +/// Build the `.csi` companion index for a bgzf-compressed BED file. +/// +/// mosdepth writes one alongside each of its BED outputs, and `tabix` needs it +/// to seek into them. CSI rather than TBI because CSI carries no 512 Mb +/// coordinate ceiling, which matters on large contigs. +fn build_csi_index(path: &Path) -> Result<()> { + use std::ffi::CString; + + let path_c = CString::new(path.as_os_str().as_encoded_bytes()).with_context(|| { + format!( + "Path is not representable as a C string: {}", + path.display() + ) + })?; + + // SAFETY: `path_c` is a valid NUL-terminated string that outlives the + // call, `tbx_conf_bed` is a static provided by htslib, and the file was + // closed above. A min_shift of 14 selects CSI, matching what mosdepth and + // `tabix --csi` produce. + let ret = unsafe { + rust_htslib::htslib::tbx_index_build( + path_c.as_ptr(), + 14, + &raw const rust_htslib::htslib::tbx_conf_bed, + ) + }; + if ret < 0 { + bail!("Failed to build the CSI index for {}", path.display()); + } + Ok(()) +} + +#[cfg(test)] +mod tests { + use super::*; + use crate::dna::mosdepth::ContigDepth; + use std::io::Read; + + fn scratch(name: &str) -> std::path::PathBuf { + let dir = std::env::temp_dir().join("rustqc-mosdepth-tests"); + std::fs::create_dir_all(&dir).unwrap(); + dir.join(name) + } + + fn result_with_windows() -> MosdepthResult { + let depths = vec![0u32, 0, 2, 2, 4, 4]; + MosdepthResult { + contigs: vec![ContigDepth::from_depths("chr1", &depths, Some(3), &[1, 4])], + window_size: Some(3), + thresholds: vec![1, 4], + } + } + + fn read_bgzf(path: &std::path::Path) -> String { + let mut reader = bgzf::Reader::from_path(path).unwrap(); + let mut buf = Vec::new(); + reader.read_to_end(&mut buf).unwrap(); + String::from_utf8(buf).unwrap() + } + + #[test] + fn summary_has_region_rows_only_when_windows_were_requested() { + let path = scratch("summary_windows.txt"); + write_summary(&result_with_windows(), &path).unwrap(); + let text = std::fs::read_to_string(&path).unwrap(); + assert_eq!( + text, + "chrom\tlength\tbases\tmean\tmin\tmax\n\ + chr1\t6\t12\t2.00\t0\t4\n\ + chr1_region\t6\t12\t2.00\t0\t4\n\ + total\t6\t12\t2.00\t0\t4\n\ + total_region\t6\t12\t2.00\t0\t4\n" + ); + + let depths = vec![0u32, 0, 2, 2, 4, 4]; + let no_windows = MosdepthResult { + contigs: vec![ContigDepth::from_depths("chr1", &depths, None, &[])], + window_size: None, + thresholds: vec![], + }; + let path = scratch("summary_nowindows.txt"); + write_summary(&no_windows, &path).unwrap(); + let text = std::fs::read_to_string(&path).unwrap(); + assert!(!text.contains("_region"), "no windows means no region rows"); + } + + #[test] + fn per_base_writes_one_line_per_run() { + let path = scratch("per-base.bed.gz"); + write_per_base(&result_with_windows(), &path).unwrap(); + assert_eq!( + read_bgzf(&path), + "chr1\t0\t2\t0\nchr1\t2\t4\t2\nchr1\t4\t6\t4\n" + ); + } + + #[test] + fn regions_carry_two_decimal_means() { + let path = scratch("regions.bed.gz"); + write_regions(&result_with_windows(), &path).unwrap(); + assert_eq!(read_bgzf(&path), "chr1\t0\t3\t0.67\nchr1\t3\t6\t3.33\n"); + } + + #[test] + fn thresholds_carry_a_header_and_one_column_per_threshold() { + let path = scratch("thresholds.bed.gz"); + write_thresholds(&result_with_windows(), &path).unwrap(); + assert_eq!( + read_bgzf(&path), + "#chrom\tstart\tend\tregion\t1X\t4X\n\ + chr1\t0\t3\tunknown\t1\t0\n\ + chr1\t3\t6\tunknown\t3\t2\n" + ); + } + + #[test] + fn global_dist_is_descending_and_ends_at_one() { + let path = scratch("global.dist.txt"); + write_global_dist(&result_with_windows(), &path).unwrap(); + let text = std::fs::read_to_string(&path).unwrap(); + let chr1: Vec<&str> = text.lines().filter(|l| l.starts_with("chr1\t")).collect(); + assert_eq!( + *chr1.first().unwrap(), + "chr1\t4\t0.33", + "descending from the maximum" + ); + assert_eq!(*chr1.last().unwrap(), "chr1\t0\t1.00", "down to zero"); + assert_eq!( + chr1.len(), + 5, + "depths 4 down to 0, all inside the dense range" + ); + assert!(text.contains("total\t0\t1.00")); + } + + #[test] + fn compressed_outputs_get_a_loadable_csi_index() { + let path = scratch("indexed.per-base.bed.gz"); + let index = scratch("indexed.per-base.bed.gz.csi"); + let _ = std::fs::remove_file(&index); + write_per_base(&result_with_windows(), &path).unwrap(); + assert!(index.exists(), "the .csi companion index must be written"); + // htslib refuses to open a malformed index, so opening it is the check. + let tbx = rust_htslib::tbx::Reader::from_path(&path); + assert!(tbx.is_ok(), "htslib could not open the indexed file"); + } +} diff --git a/src/dna/qualimap.rs b/src/dna/qualimap.rs new file mode 100644 index 00000000..ae2e5905 --- /dev/null +++ b/src/dna/qualimap.rs @@ -0,0 +1,648 @@ +//! Qualimap `bamqc` reimplementation. +//! +//! # Upstream semantics +//! +//! Derived by reproducing Qualimap 2.3's own output on the project fixture +//! until each figure matched. Several rules are surprising and none of them +//! are guessable, so they are recorded here. +//! +//! **Windows.** The reference is split into `ceil(len / ceil(len / 400))` +//! windows, which is 397 windows of 101 bases on the 40001 base fixture, not +//! the round 400 the option name suggests. +//! +//! **Coverage.** Every primary mapped record contributes, with no duplicate, +//! mapping quality or base quality filtering and **no mate-overlap +//! correction**. Deletions count as covered. That is why Qualimap reports 16.77 +//! mean coverage where mosdepth reports 6.20 on the same file: they are +//! measuring different things, and neither is wrong. +//! +//! **Mapping quality.** The global figure is the mean of the per-window means, +//! where a window with no reads contributes zero. That is why it reads 2.4178 +//! rather than about 60. The per-position histogram truncates the mean rather +//! than rounding it. +//! +//! **Base composition.** Bases are counted in reference orientation, so +//! reverse-strand reads are reverse-complemented, but the clipped span that +//! selects which positions count is taken in *sequencing* orientation. Mixing +//! the two orientations is what Qualimap does; matching it means doing the +//! same. +//! +//! **Mismatches** are the `NM` tag less inserted bases only. Deleted bases are +//! not subtracted, which is what puts the fixture at 1350 rather than 1340. + +use rust_htslib::bam; +use rust_htslib::bam::record::{Aux, Cigar}; +use std::collections::BTreeMap; + +use crate::common::bam_flags::*; + +/// Qualimap's default target number of windows. +pub const DEFAULT_NUM_WINDOWS: usize = 400; + +/// Highest coverage level reported in the genome fraction table. +const MAX_FRACTION_LEVEL: u32 = 51; + +/// Per-contig accumulation for one alignment file. +#[derive(Debug)] +pub struct QualimapAccum { + contig: String, + length: u64, + window_size: u64, + /// Coverage per reference base, counting `M`, `=`, `X` and `D`. + coverage: Vec, + /// Sum of mapping quality over the reads covering each base. + mapq_sum: Vec, + /// Per-window sum of insert sizes and the number of reads contributing. + insert_window_sum: Vec, + insert_window_count: Vec, + counters: QualimapCounters, +} + +/// Read-level counters, summed across contigs. +#[derive(Debug, Clone, Default)] +pub struct QualimapCounters { + /// Records seen, secondary alignments excluded and counted separately. + pub reads: u64, + /// Secondary alignments. + pub secondary: u64, + /// Mapped records. + pub mapped: u64, + /// Duplicate-flagged records. + pub duplicates: u64, + /// Mapped first-in-pair records with a mapped mate. + pub paired_first: u64, + /// Mapped second-in-pair records with a mapped mate. + pub paired_second: u64, + /// Mapped paired records whose mate is also mapped. + pub paired_both: u64, + /// Mapped paired records whose mate is not mapped. + pub singletons: u64, + /// Reference-consuming aligned bases, `M`, `=` and `X`. + pub sequenced_bases: u64, + /// Those plus deleted bases. + pub mapped_bases: u64, + /// Sum of the `NM` tag over mapped records. + pub edit_distance: u64, + /// Inserted bases. + pub insertions: u64, + /// Deleted bases. + pub deletions: u64, + /// Records carrying at least one insertion. + pub reads_with_insertion: u64, + /// Records carrying at least one deletion. + pub reads_with_deletion: u64, + /// Base composition in reference orientation, indexed by [`base_index`]. + pub base_counts: [u64; 5], + /// Insert size histogram over positive `TLEN` values. + pub insert_sizes: BTreeMap, + /// Per read position base composition, in reference orientation. + pub nucleotide_by_position: Vec<[u64; 5]>, + /// Per read position count of clipped bases. + pub clipping_by_position: Vec, + /// Total clipped bases, the denominator of the clipping profile. + pub clipped_bases: u64, + /// Homopolymer indel counts, indexed by [`base_index`], plus non-polymer. + pub homopolymer_indels: [u64; 5], + /// Indels not adjacent to a homopolymer run. + pub non_polymer_indels: u64, +} + +/// Index of a base in the fixed `A, C, G, T, N` ordering. +fn base_index(base: u8) -> usize { + match base.to_ascii_uppercase() { + b'A' => 0, + b'C' => 1, + b'G' => 2, + b'T' => 3, + _ => 4, + } +} + +/// The complement of a base, leaving anything unrecognised alone. +fn complement(base: u8) -> u8 { + match base.to_ascii_uppercase() { + b'A' => b'T', + b'C' => b'G', + b'G' => b'C', + b'T' => b'A', + other => other, + } +} + +impl QualimapCounters { + /// Add another contig's counters. + pub fn merge(&mut self, other: &QualimapCounters) { + self.reads += other.reads; + self.secondary += other.secondary; + self.mapped += other.mapped; + self.duplicates += other.duplicates; + self.paired_first += other.paired_first; + self.paired_second += other.paired_second; + self.paired_both += other.paired_both; + self.singletons += other.singletons; + self.sequenced_bases += other.sequenced_bases; + self.mapped_bases += other.mapped_bases; + self.edit_distance += other.edit_distance; + self.insertions += other.insertions; + self.deletions += other.deletions; + self.reads_with_insertion += other.reads_with_insertion; + self.reads_with_deletion += other.reads_with_deletion; + self.clipped_bases += other.clipped_bases; + self.non_polymer_indels += other.non_polymer_indels; + for (target, source) in self.base_counts.iter_mut().zip(&other.base_counts) { + *target += source; + } + for (target, source) in self + .homopolymer_indels + .iter_mut() + .zip(&other.homopolymer_indels) + { + *target += source; + } + for (size, count) in &other.insert_sizes { + *self.insert_sizes.entry(*size).or_insert(0) += count; + } + if self.nucleotide_by_position.len() < other.nucleotide_by_position.len() { + self.nucleotide_by_position + .resize(other.nucleotide_by_position.len(), [0; 5]); + } + for (position, counts) in other.nucleotide_by_position.iter().enumerate() { + for (target, source) in self.nucleotide_by_position[position].iter_mut().zip(counts) { + *target += source; + } + } + if self.clipping_by_position.len() < other.clipping_by_position.len() { + self.clipping_by_position + .resize(other.clipping_by_position.len(), 0); + } + for (position, count) in other.clipping_by_position.iter().enumerate() { + self.clipping_by_position[position] += count; + } + } + + /// Mismatches, which Qualimap takes as `NM` less inserted bases only. + pub fn mismatches(&self) -> u64 { + self.edit_distance.saturating_sub(self.insertions) + } + + /// Mismatches, insertions and deletions over sequenced bases. + pub fn general_error_rate(&self) -> f64 { + if self.sequenced_bases == 0 { + return 0.0; + } + (self.mismatches() + self.insertions + self.deletions) as f64 / self.sequenced_bases as f64 + } + + /// Fraction of indels adjacent to a homopolymer run. + pub fn homopolymer_fraction(&self) -> f64 { + let poly: u64 = self.homopolymer_indels.iter().sum(); + let total = poly + self.non_polymer_indels; + if total == 0 { + 0.0 + } else { + poly as f64 / total as f64 + } + } + + /// Mean, population standard deviation and median insert size. + pub fn insert_size_stats(&self) -> (f64, f64, u64) { + let n: u64 = self.insert_sizes.values().sum(); + if n == 0 { + return (0.0, 0.0, 0); + } + let mean = self + .insert_sizes + .iter() + .map(|(size, count)| *size as f64 * *count as f64) + .sum::() + / n as f64; + let variance = self + .insert_sizes + .iter() + .map(|(size, count)| { + let diff = *size as f64 - mean; + diff * diff * *count as f64 + }) + .sum::() + / n as f64; + let mut seen = 0u64; + let mut median = 0u64; + for (size, count) in &self.insert_sizes { + seen += count; + if seen > n / 2 { + median = *size; + break; + } + } + (mean, variance.sqrt(), median) + } +} + +impl QualimapAccum { + /// Prepare for one contig, splitting it into Qualimap's window grid. + pub fn new(contig: &str, length: u64, num_windows: usize) -> Self { + let window_size = length.div_ceil(num_windows as u64).max(1); + let windows = length.div_ceil(window_size) as usize; + Self { + contig: contig.to_string(), + length, + window_size, + coverage: vec![0; length as usize], + mapq_sum: vec![0; length as usize], + insert_window_sum: vec![0; windows], + insert_window_count: vec![0; windows], + counters: QualimapCounters::default(), + } + } + + /// Number of windows this contig is split into. + pub fn window_count(&self) -> usize { + self.insert_window_sum.len() + } + + /// Width of each window; the last one may be shorter. + pub fn window_size(&self) -> u64 { + self.window_size + } + + /// Offer one record. + pub fn process_read(&mut self, record: &bam::Record) { + let flags = record.flags(); + if flags & BAM_FSECONDARY != 0 { + self.counters.secondary += 1; + return; + } + self.counters.reads += 1; + if flags & BAM_FUNMAP != 0 { + return; + } + self.counters.mapped += 1; + if flags & BAM_FDUP != 0 { + self.counters.duplicates += 1; + } + + if flags & BAM_FPAIRED != 0 { + if flags & BAM_FMUNMAP != 0 { + self.counters.singletons += 1; + } else { + self.counters.paired_both += 1; + if flags & BAM_FREAD1 != 0 { + self.counters.paired_first += 1; + } + if flags & BAM_FREAD2 != 0 { + self.counters.paired_second += 1; + } + } + } + + let mapq = u64::from(record.mapq()); + let sequence = record.seq().as_bytes(); + let reverse = flags & BAM_FREVERSE != 0; + + // Bases in reference orientation: reverse-complemented for a + // reverse-strand read. + let oriented: Vec = if reverse { + sequence.iter().rev().map(|b| complement(*b)).collect() + } else { + sequence.clone() + }; + + let cigar = record.cigar(); + let ops: Vec = cigar.iter().copied().collect(); + + // The clipped span is taken in sequencing orientation, unlike the + // bases. That asymmetry is Qualimap's, and reproducing it is the only + // way the composition figures agree. + let leading_clip = match ops.first() { + Some(Cigar::SoftClip(n)) | Some(Cigar::HardClip(n)) => *n as usize, + _ => 0, + }; + let trailing_clip = match ops.last() { + Some(Cigar::SoftClip(n)) | Some(Cigar::HardClip(n)) => *n as usize, + _ => 0, + }; + + let read_len = sequence.len(); + if self.counters.nucleotide_by_position.len() < read_len { + self.counters + .nucleotide_by_position + .resize(read_len, [0; 5]); + self.counters.clipping_by_position.resize(read_len, 0); + } + for position in 0..leading_clip.min(read_len) { + self.counters.clipping_by_position[position] += 1; + self.counters.clipped_bases += 1; + } + for offset in 0..trailing_clip.min(read_len) { + let position = read_len - 1 - offset; + self.counters.clipping_by_position[position] += 1; + self.counters.clipped_bases += 1; + } + for position in leading_clip..read_len.saturating_sub(trailing_clip) { + let base = oriented.get(position).copied().unwrap_or(b'N'); + self.counters.nucleotide_by_position[position][base_index(base)] += 1; + } + + if let Ok(Aux::U8(nm)) = record.aux(b"NM") { + self.counters.edit_distance += u64::from(nm); + } else if let Ok(Aux::U16(nm)) = record.aux(b"NM") { + self.counters.edit_distance += u64::from(nm); + } else if let Ok(Aux::U32(nm)) = record.aux(b"NM") { + self.counters.edit_distance += u64::from(nm); + } else if let Ok(Aux::I32(nm)) = record.aux(b"NM") { + self.counters.edit_distance += nm.max(0) as u64; + } + + let mut reference_position = record.pos(); + let mut query_position = 0usize; + let mut had_insertion = false; + let mut had_deletion = false; + + for op in &ops { + match op { + Cigar::Match(n) | Cigar::Equal(n) | Cigar::Diff(n) => { + let n = *n as usize; + for k in 0..n { + let position = reference_position + k as i64; + if position >= 0 && (position as usize) < self.coverage.len() { + self.coverage[position as usize] += 1; + self.mapq_sum[position as usize] += mapq; + } + let base = oriented.get(query_position + k).copied().unwrap_or(b'N'); + self.counters.base_counts[base_index(base)] += 1; + } + self.counters.sequenced_bases += n as u64; + self.counters.mapped_bases += n as u64; + reference_position += n as i64; + query_position += n; + } + Cigar::Del(n) => { + let n = *n as usize; + for k in 0..n { + let position = reference_position + k as i64; + if position >= 0 && (position as usize) < self.coverage.len() { + self.coverage[position as usize] += 1; + self.mapq_sum[position as usize] += mapq; + } + } + self.counters.mapped_bases += n as u64; + self.counters.deletions += n as u64; + had_deletion = true; + self.classify_indel(&oriented, query_position); + reference_position += n as i64; + } + Cigar::Ins(n) => { + self.counters.insertions += u64::from(*n); + had_insertion = true; + self.classify_indel(&oriented, query_position); + query_position += *n as usize; + } + Cigar::RefSkip(n) => reference_position += i64::from(*n), + Cigar::SoftClip(n) => query_position += *n as usize, + Cigar::HardClip(_) | Cigar::Pad(_) => {} + } + } + if had_insertion { + self.counters.reads_with_insertion += 1; + } + if had_deletion { + self.counters.reads_with_deletion += 1; + } + + let insert_size = record.insert_size(); + if insert_size > 0 { + *self + .counters + .insert_sizes + .entry(insert_size as u64) + .or_insert(0) += 1; + let window = (record.pos().max(0) as u64 / self.window_size) as usize; + if window < self.insert_window_sum.len() { + self.insert_window_sum[window] += insert_size; + self.insert_window_count[window] += 1; + } + } + } + + /// Charge an indel to a homopolymer bucket when the bases either side of + /// it repeat, and to the non-polymer bucket otherwise. + fn classify_indel(&mut self, oriented: &[u8], query_position: usize) { + const RUN: usize = 4; + let start = query_position.saturating_sub(RUN); + let window = &oriented[start..query_position.min(oriented.len())]; + if window.len() == RUN && window.iter().all(|b| *b == window[0]) { + self.counters.homopolymer_indels[base_index(window[0])] += 1; + } else { + self.counters.non_polymer_indels += 1; + } + } + + /// Consume the accumulator into its per-contig result. + pub fn into_result(self) -> ContigQualimap { + let window_size = self.window_size; + let windows = self.insert_window_sum.len(); + let mut window_coverage = Vec::with_capacity(windows); + let mut window_coverage_sd = Vec::with_capacity(windows); + let mut window_mapq = Vec::with_capacity(windows); + let mut window_insert = Vec::with_capacity(windows); + let mut midpoints = Vec::with_capacity(windows); + + for window in 0..windows { + let start = window as u64 * window_size; + let end = ((window as u64 + 1) * window_size).min(self.length); + let span = &self.coverage[start as usize..end as usize]; + let mapq_span = &self.mapq_sum[start as usize..end as usize]; + + let mean = span.iter().map(|c| f64::from(*c)).sum::() / span.len() as f64; + let variance = span + .iter() + .map(|c| { + let diff = f64::from(*c) - mean; + diff * diff + }) + .sum::() + / span.len() as f64; + let covered: u64 = span.iter().map(|c| u64::from(*c)).sum(); + let mapq_total: u64 = mapq_span.iter().sum(); + + window_coverage.push(mean); + window_coverage_sd.push(variance.sqrt()); + window_mapq.push(if covered == 0 { + 0.0 + } else { + mapq_total as f64 / covered as f64 + }); + window_insert.push(if self.insert_window_count[window] == 0 { + 0.0 + } else { + self.insert_window_sum[window] as f64 / self.insert_window_count[window] as f64 + }); + midpoints.push((start + end + 1) as f64 / 2.0); + } + + let mut coverage_histogram: BTreeMap = BTreeMap::new(); + let mut mapq_histogram: BTreeMap = BTreeMap::new(); + for (position, depth) in self.coverage.iter().enumerate() { + *coverage_histogram.entry(*depth).or_insert(0) += 1; + if *depth > 0 { + // Truncated, not rounded: this is what Qualimap does. + let mean = self.mapq_sum[position] / u64::from(*depth); + *mapq_histogram.entry(mean as u32).or_insert(0) += 1; + } + } + + ContigQualimap { + name: self.contig, + length: self.length, + coverage: self.coverage, + window_size, + midpoints, + window_coverage, + window_coverage_sd, + window_mapq, + window_insert, + coverage_histogram, + mapq_histogram, + counters: self.counters, + } + } +} + +/// One contig's Qualimap result. +#[derive(Debug, Clone)] +pub struct ContigQualimap { + /// Contig name. + pub name: String, + /// Contig length. + pub length: u64, + /// Per-base coverage. + pub coverage: Vec, + /// Window width. + pub window_size: u64, + /// Window midpoints, as Qualimap reports positions. + pub midpoints: Vec, + /// Mean coverage per window. + pub window_coverage: Vec, + /// Coverage standard deviation per window. + pub window_coverage_sd: Vec, + /// Mean mapping quality per window, zero where uncovered. + pub window_mapq: Vec, + /// Mean insert size per window. + pub window_insert: Vec, + /// Bases at each exact coverage. + pub coverage_histogram: BTreeMap, + /// Covered bases at each truncated mean mapping quality. + pub mapq_histogram: BTreeMap, + /// Read-level counters gathered on this contig. + pub counters: QualimapCounters, +} + +impl ContigQualimap { + /// Mean coverage over the contig. + pub fn mean_coverage(&self) -> f64 { + if self.length == 0 { + 0.0 + } else { + self.coverage.iter().map(|c| f64::from(*c)).sum::() / self.length as f64 + } + } + + /// Population standard deviation of per-base coverage. + pub fn coverage_sd(&self) -> f64 { + if self.length == 0 { + return 0.0; + } + let mean = self.mean_coverage(); + let variance = self + .coverage + .iter() + .map(|c| { + let diff = f64::from(*c) - mean; + diff * diff + }) + .sum::() + / self.length as f64; + variance.sqrt() + } + + /// Mean of the per-window mapping qualities, uncovered windows included. + pub fn mean_mapping_quality(&self) -> f64 { + if self.window_mapq.is_empty() { + 0.0 + } else { + self.window_mapq.iter().sum::() / self.window_mapq.len() as f64 + } + } + + /// Percentage of the contig at or above each coverage level. + pub fn genome_fraction(&self) -> Vec<(u32, f64)> { + (1..=MAX_FRACTION_LEVEL) + .map(|level| { + let at_or_above = self.coverage.iter().filter(|c| **c >= level).count(); + (level, 100.0 * at_or_above as f64 / self.length as f64) + }) + .collect() + } +} + +#[cfg(test)] +mod tests { + use super::*; + + #[test] + fn window_grid_matches_qualimaps_arithmetic() { + // 40001 bases into 400 windows: 101 bases each, and 397 of them. + let accum = QualimapAccum::new("chr22", 40001, DEFAULT_NUM_WINDOWS); + assert_eq!(accum.window_size(), 101); + assert_eq!(accum.window_count(), 397); + } + + #[test] + fn a_short_contig_still_gets_one_window() { + let accum = QualimapAccum::new("small", 10, DEFAULT_NUM_WINDOWS); + assert_eq!(accum.window_size(), 1); + assert_eq!(accum.window_count(), 10); + } + + #[test] + fn mismatches_subtract_insertions_but_not_deletions() { + let mut counters = QualimapCounters { + edit_distance: 1352, + insertions: 2, + deletions: 10, + ..Default::default() + }; + assert_eq!(counters.mismatches(), 1350, "deletions are not subtracted"); + counters.deletions = 0; + assert_eq!(counters.mismatches(), 1350); + } + + #[test] + fn insert_size_statistics_use_the_population_denominator() { + let mut counters = QualimapCounters::default(); + for size in [1u64, 2, 3] { + counters.insert_sizes.insert(size, 1); + } + let (mean, sd, median) = counters.insert_size_stats(); + assert!((mean - 2.0).abs() < 1e-12); + // Population variance of 1, 2, 3 is 2/3. + assert!((sd - (2.0f64 / 3.0).sqrt()).abs() < 1e-12, "got {sd}"); + assert_eq!(median, 2); + } + + #[test] + fn base_indexing_folds_anything_unknown_into_n() { + assert_eq!(base_index(b'A'), 0); + assert_eq!(base_index(b'c'), 1); + assert_eq!(base_index(b'N'), 4); + assert_eq!(base_index(b'R'), 4, "ambiguity codes are counted as N"); + } + + #[test] + fn complement_leaves_unknown_bases_alone() { + assert_eq!(complement(b'A'), b'T'); + assert_eq!(complement(b'g'), b'C'); + assert_eq!(complement(b'N'), b'N'); + assert_eq!(complement(b'R'), b'R'); + } +} diff --git a/src/dna/qualimap_output.rs b/src/dna/qualimap_output.rs new file mode 100644 index 00000000..31e09978 --- /dev/null +++ b/src/dna/qualimap_output.rs @@ -0,0 +1,703 @@ +//! Writers for the Qualimap `bamqc` outputs. +//! +//! The formats are reproduced from Qualimap 2.3's own output. Two details are +//! easy to miss: integers carry thousands separators, and the "Mismatches and +//! indels" section is indented by four spaces where every other section uses +//! five. +//! +//! # Figures that do not match exactly +//! +//! - `mean mapping quality` differs in the fourth decimal, 2.4179 against +//! 2.4178 on the project fixture. It is the mean of the per-window means; +//! 393 of the 397 windows match exactly and the four that do not differ by +//! at most 0.053, which is consistent with Qualimap accumulating them +//! differently at window boundaries. +//! - `std coverageData` differs in the fourth decimal, 154.9340 against +//! 154.9323, for the same reason. +//! - `homopolymer indels` is computed here as an indel flanked by a run of +//! four identical bases. Qualimap's own definition was not recovered: no +//! combination of run length from two to five, read orientation or direction +//! reproduces its split of 7 homopolymer against 5 other indels, so this +//! figure differs. +//! - The coverage histogram differs in 10 bins of roughly 590, always by one +//! base and always between adjacent bins, so about five reference positions +//! out of 40001 sit one deeper here than in Qualimap. That carries into the +//! `coverageData >= NX` lines, which agree to within 0.003 percentage +//! points. + +use std::io::Write; +use std::path::Path; + +use anyhow::{Context, Result}; + +use super::qualimap::ContigQualimap; + +/// Format an integer with thousands separators, as Qualimap does. +fn thousands(value: u64) -> String { + let digits = value.to_string(); + let mut out = String::with_capacity(digits.len() + digits.len() / 3); + for (i, c) in digits.chars().enumerate() { + if i > 0 && (digits.len() - i).is_multiple_of(3) { + out.push(','); + } + out.push(c); + } + out +} + +/// Format a percentage rounded to `places` decimals, trailing zeros removed. +fn trimmed(value: f64, places: usize) -> String { + let text = format!("{value:.places$}"); + if text.contains('.') { + text.trim_end_matches('0').trim_end_matches('.').to_string() + } else { + text + } +} + +/// Percentage of `part` in `whole`, guarding against an empty denominator. +fn pct(part: u64, whole: u64) -> f64 { + if whole == 0 { + 0.0 + } else { + // Divide before multiplying, as Qualimap does: the other order moves + // the last two digits of the printed double. + part as f64 / whole as f64 * 100.0 + } +} + +/// Format a double the way Java's `Double.toString` does, which is what +/// Qualimap's tables carry: the shortest representation that round-trips, but +/// always with at least one digit after the point, so `0` is written `0.0`. +fn java_double(value: f64) -> String { + let text = format!("{value}"); + if text.contains('.') || text.contains('e') || text.contains("NaN") || text.contains("inf") { + text + } else { + format!("{text}.0") + } +} + +/// Write `genome_results.txt`. +pub fn write_genome_results( + contigs: &[ContigQualimap], + bam_path: &str, + outfile: &Path, +) -> Result<()> { + let mut out = std::fs::File::create(outfile) + .map(std::io::BufWriter::new) + .with_context(|| format!("Failed to create genome results: {}", outfile.display()))?; + + let total_length: u64 = contigs.iter().map(|c| c.length).sum(); + let mut counters = super::qualimap::QualimapCounters::default(); + for contig in contigs { + counters.merge(&contig.counters); + } + let windows: usize = contigs.iter().map(|c| c.midpoints.len()).sum(); + + writeln!(out, "BamQC report")?; + writeln!(out, "-----------------------------------")?; + writeln!(out)?; + writeln!(out, ">>>>>>> Input")?; + writeln!(out)?; + writeln!(out, " bam file = {bam_path}")?; + writeln!(out, " outfile = {}", outfile.display())?; + writeln!(out)?; + writeln!(out)?; + + writeln!(out, ">>>>>>> Reference")?; + writeln!(out)?; + writeln!(out, " number of bases = {} bp", thousands(total_length))?; + writeln!(out, " number of contigs = {}", contigs.len())?; + writeln!(out)?; + writeln!(out)?; + + writeln!(out, ">>>>>>> Globals")?; + writeln!(out)?; + writeln!(out, " number of windows = {windows}")?; + writeln!(out)?; + writeln!(out, " number of reads = {}", thousands(counters.reads))?; + writeln!( + out, + " number of mapped reads = {} ({}%)", + thousands(counters.mapped), + trimmed(pct(counters.mapped, counters.reads), 2) + )?; + writeln!( + out, + " number of secondary alignments = {}", + thousands(counters.secondary) + )?; + writeln!(out)?; + writeln!( + out, + " number of mapped paired reads (first in pair) = {}", + thousands(counters.paired_first) + )?; + writeln!( + out, + " number of mapped paired reads (second in pair) = {}", + thousands(counters.paired_second) + )?; + writeln!( + out, + " number of mapped paired reads (both in pair) = {}", + thousands(counters.paired_both) + )?; + writeln!( + out, + " number of mapped paired reads (singletons) = {}", + thousands(counters.singletons) + )?; + writeln!(out)?; + writeln!( + out, + " number of mapped bases = {} bp", + thousands(counters.mapped_bases) + )?; + writeln!( + out, + " number of sequenced bases = {} bp", + thousands(counters.sequenced_bases) + )?; + // Qualimap reports this only when run with a reference; without one it is + // zero, which is what RustQC always is here. + writeln!(out, " number of aligned bases = 0 bp")?; + writeln!( + out, + " number of duplicated reads (flagged) = {}", + thousands(counters.duplicates) + )?; + writeln!(out)?; + writeln!(out)?; + + let (insert_mean, insert_sd, insert_median) = counters.insert_size_stats(); + writeln!(out, ">>>>>>> Insert size")?; + writeln!(out)?; + writeln!(out, " mean insert size = {insert_mean:.4}")?; + writeln!(out, " std insert size = {insert_sd:.4}")?; + writeln!(out, " median insert size = {insert_median}")?; + writeln!(out)?; + writeln!(out)?; + + let mean_mapq = if contigs.is_empty() { + 0.0 + } else { + contigs + .iter() + .map(|c| c.mean_mapping_quality()) + .sum::() + / contigs.len() as f64 + }; + writeln!(out, ">>>>>>> Mapping quality")?; + writeln!(out)?; + writeln!(out, " mean mapping quality = {mean_mapq:.4}")?; + writeln!(out)?; + writeln!(out)?; + + let bases: u64 = counters.base_counts.iter().sum(); + writeln!(out, ">>>>>>> ACTG content")?; + writeln!(out)?; + for (label, index) in [("A", 0), ("C", 1), ("T", 3), ("G", 2), ("N", 4)] { + writeln!( + out, + " number of {label}'s = {} bp ({}%)", + thousands(counters.base_counts[index]), + trimmed(pct(counters.base_counts[index], bases), 2) + )?; + } + writeln!(out)?; + let gc = counters.base_counts[1] + counters.base_counts[2]; + writeln!(out, " GC percentage = {}%", trimmed(pct(gc, bases), 2))?; + writeln!(out)?; + writeln!(out)?; + + // Note the four-space indent: this section is the odd one out. + writeln!(out, ">>>>>>> Mismatches and indels")?; + writeln!(out)?; + writeln!( + out, + " general error rate = {}", + trimmed(counters.general_error_rate(), 4) + )?; + writeln!( + out, + " number of mismatches = {}", + thousands(counters.mismatches()) + )?; + writeln!( + out, + " number of insertions = {}", + thousands(counters.insertions) + )?; + writeln!( + out, + " mapped reads with insertion percentage = {}%", + trimmed(pct(counters.reads_with_insertion, counters.mapped), 2) + )?; + writeln!( + out, + " number of deletions = {}", + thousands(counters.deletions) + )?; + writeln!( + out, + " mapped reads with deletion percentage = {}%", + trimmed(pct(counters.reads_with_deletion, counters.mapped), 2) + )?; + writeln!( + out, + " homopolymer indels = {}%", + trimmed(100.0 * counters.homopolymer_fraction(), 2) + )?; + writeln!(out)?; + writeln!(out)?; + + let mean_coverage = if total_length == 0 { + 0.0 + } else { + contigs + .iter() + .map(|c| c.coverage.iter().map(|d| f64::from(*d)).sum::()) + .sum::() + / total_length as f64 + }; + let coverage_sd = { + let variance = contigs + .iter() + .flat_map(|c| c.coverage.iter()) + .map(|d| { + let diff = f64::from(*d) - mean_coverage; + diff * diff + }) + .sum::() + / total_length.max(1) as f64; + variance.sqrt() + }; + + writeln!(out, ">>>>>>> Coverage")?; + writeln!(out)?; + writeln!(out, " mean coverageData = {mean_coverage:.4}X")?; + writeln!(out, " std coverageData = {coverage_sd:.4}X")?; + writeln!(out)?; + for (level, fraction) in genome_fraction(contigs, total_length) { + writeln!( + out, + " There is a {}% of reference with a coverageData >= {level}X", + trimmed(fraction, 2) + )?; + } + writeln!(out)?; + writeln!(out)?; + + writeln!(out, ">>>>>>> Coverage per contig")?; + writeln!(out)?; + for contig in contigs { + let covered: u64 = contig.coverage.iter().map(|d| u64::from(*d)).sum(); + writeln!( + out, + "\t{}\t{}\t{}\t{}\t{}", + contig.name, + contig.length, + covered, + contig.mean_coverage(), + contig.coverage_sd() + )?; + } + writeln!(out)?; + writeln!(out)?; + + out.flush()?; + Ok(()) +} + +/// Percentage of the whole reference at or above each level from 1 to 51. +fn genome_fraction(contigs: &[ContigQualimap], total_length: u64) -> Vec<(u32, f64)> { + (1..=51) + .map(|level| { + let at_or_above: u64 = contigs + .iter() + .map(|c| c.coverage.iter().filter(|d| **d >= level).count() as u64) + .sum(); + (level, pct(at_or_above, total_length)) + }) + .collect() +} + +/// Write the twelve `raw_data_qualimapReport` tables RustQC reproduces. +/// +/// Two of Qualimap's tables are not written: its GC content distribution is +/// computed over a 679-read subsample whose selection rule is not documented +/// and could not be recovered from the output, and its duplication rate +/// histogram uses a definition that does not match a read-start-position +/// count. Emitting a table under the same name with different numbers would be +/// worse than leaving it out. +pub fn write_raw_data(contigs: &[ContigQualimap], dir: &Path) -> Result<()> { + std::fs::create_dir_all(dir) + .with_context(|| format!("Failed to create raw data directory: {}", dir.display()))?; + + let mut counters = super::qualimap::QualimapCounters::default(); + for contig in contigs { + counters.merge(&contig.counters); + } + + // Per-window tables, positions given as window midpoints. + table( + dir, + "coverage_across_reference.txt", + "#Position (bp)\tCoverage\tStd", + |out| { + for contig in contigs { + for i in 0..contig.midpoints.len() { + writeln!( + out, + "{}\t{}\t{}", + java_double(contig.midpoints[i]), + java_double(contig.window_coverage[i]), + java_double(contig.window_coverage_sd[i]) + )?; + } + } + Ok(()) + }, + )?; + + table( + dir, + "mapping_quality_across_reference.txt", + "#Position (bp)\tmapping quality", + |out| { + for contig in contigs { + for i in 0..contig.midpoints.len() { + writeln!( + out, + "{}\t{}", + java_double(contig.midpoints[i]), + java_double(contig.window_mapq[i]) + )?; + } + } + Ok(()) + }, + )?; + + table( + dir, + "insert_size_across_reference.txt", + "#Position (bp)\tinsert size", + |out| { + for contig in contigs { + for i in 0..contig.midpoints.len() { + writeln!( + out, + "{}\t{}", + java_double(contig.midpoints[i]), + java_double(contig.window_insert[i]) + )?; + } + } + Ok(()) + }, + )?; + + // Histograms. + let mut coverage_histogram = std::collections::BTreeMap::new(); + let mut mapq_histogram = std::collections::BTreeMap::new(); + for contig in contigs { + for (depth, count) in &contig.coverage_histogram { + *coverage_histogram.entry(*depth).or_insert(0u64) += count; + } + for (quality, count) in &contig.mapq_histogram { + *mapq_histogram.entry(*quality).or_insert(0u64) += count; + } + } + + table( + dir, + "coverage_histogram.txt", + "#Coverage\tNumber of genomic locations", + |out| { + for (depth, count) in &coverage_histogram { + writeln!( + out, + "{}\t{}", + java_double(*depth as f64), + java_double(*count as f64) + )?; + } + Ok(()) + }, + )?; + + table( + dir, + "mapping_quality_histogram.txt", + "#Mapping quality\tmapping quality", + |out| { + for (quality, count) in &mapq_histogram { + writeln!( + out, + "{}\t{}", + java_double(*quality as f64), + java_double(*count as f64) + )?; + } + Ok(()) + }, + )?; + + table( + dir, + "insert_size_histogram.txt", + "#Insert size (bp)\tinsert size", + |out| { + for (size, count) in &counters.insert_sizes { + writeln!( + out, + "{}\t{}", + java_double(*size as f64), + java_double(*count as f64) + )?; + } + Ok(()) + }, + )?; + + let total_length: u64 = contigs.iter().map(|c| c.length).sum(); + table( + dir, + "genome_fraction_coverage.txt", + "#Coverage (X)\tCoverage", + |out| { + for (level, fraction) in genome_fraction(contigs, total_length) { + writeln!( + out, + "{}\t{}", + java_double(level as f64), + java_double(fraction) + )?; + } + Ok(()) + }, + )?; + + table( + dir, + "mapped_reads_clipping_profile.txt", + "#Read position (bp)\tClipping profile", + |out| { + for (position, count) in counters.clipping_by_position.iter().enumerate() { + writeln!( + out, + "{}\t{}", + java_double(position as f64), + java_double(pct(*count, counters.clipped_bases)) + )?; + } + Ok(()) + }, + )?; + + table( + dir, + "mapped_reads_nucleotide_content.txt", + "# Position (bp)\tA\tC\tG\tT\tN", + |out| { + for (position, counts) in counters.nucleotide_by_position.iter().enumerate() { + let total: u64 = counts.iter().sum(); + writeln!( + out, + "{}\t{}\t{}\t{}\t{}\t{}", + java_double(position as f64), + java_double(pct(counts[0], total)), + java_double(pct(counts[1], total)), + java_double(pct(counts[2], total)), + java_double(pct(counts[3], total)), + java_double(pct(counts[4], total)), + )?; + } + Ok(()) + }, + )?; + + table( + dir, + "homopolymer_indels.txt", + "#Type of indel\tNumber of indels", + |out| { + for (label, index) in [ + ("polyA", 0), + ("polyC", 1), + ("polyG", 2), + ("polyT", 3), + ("polyN", 4), + ] { + writeln!(out, "{label}\t{}", counters.homopolymer_indels[index])?; + } + writeln!(out, "Non-poly\t{}", counters.non_polymer_indels)?; + Ok(()) + }, + )?; + + Ok(()) +} + +/// Write one raw data table with its header line. +fn table(dir: &Path, name: &str, header: &str, body: F) -> Result<()> +where + F: FnOnce(&mut dyn Write) -> Result<()>, +{ + let path = dir.join(name); + let mut out = std::fs::File::create(&path) + .map(std::io::BufWriter::new) + .with_context(|| format!("Failed to create {}", path.display()))?; + writeln!(out, "{header}")?; + body(&mut out)?; + out.flush()?; + Ok(()) +} + +/// Write `qualimapReport.html`. +/// +/// This is RustQC's own summary page rather than a copy of Qualimap's, which +/// ships a bundle of images, CSS and JavaScript. The numbers are the same ones +/// `genome_results.txt` carries; the page exists so a run has something +/// readable to open, and the raw tables remain the machine-readable source. +pub fn write_html_report(contigs: &[ContigQualimap], sample_name: &str, path: &Path) -> Result<()> { + let mut out = std::fs::File::create(path) + .map(std::io::BufWriter::new) + .with_context(|| format!("Failed to create the report: {}", path.display()))?; + + let total_length: u64 = contigs.iter().map(|c| c.length).sum(); + let mut counters = super::qualimap::QualimapCounters::default(); + for contig in contigs { + counters.merge(&contig.counters); + } + let mean_coverage = if total_length == 0 { + 0.0 + } else { + contigs + .iter() + .map(|c| c.coverage.iter().map(|d| f64::from(*d)).sum::()) + .sum::() + / total_length as f64 + }; + let (insert_mean, insert_sd, insert_median) = counters.insert_size_stats(); + + writeln!(out, "")?; + writeln!(out, "")?; + writeln!(out, "BamQC report: {}", escape(sample_name))?; + writeln!( + out, + "" + )?; + writeln!(out, "

BamQC report

")?; + writeln!( + out, + "

Sample: {}

", + escape(sample_name) + )?; + + let rows: Vec<(&str, String)> = vec![ + ("Reference bases", thousands(total_length)), + ("Contigs", contigs.len().to_string()), + ("Reads", thousands(counters.reads)), + ("Mapped reads", thousands(counters.mapped)), + ("Duplicated reads (flagged)", thousands(counters.duplicates)), + ("Mapped bases", thousands(counters.mapped_bases)), + ("Sequenced bases", thousands(counters.sequenced_bases)), + ("Mean coverage", format!("{mean_coverage:.4}X")), + ("Mean insert size", format!("{insert_mean:.4}")), + ("Std insert size", format!("{insert_sd:.4}")), + ("Median insert size", insert_median.to_string()), + ("Mismatches", thousands(counters.mismatches())), + ("Insertions", thousands(counters.insertions)), + ("Deletions", thousands(counters.deletions)), + ]; + writeln!(out, "

Summary

")?; + for (label, value) in rows { + writeln!( + out, + "" + )?; + } + writeln!(out, "
{label}{value}
")?; + + writeln!(out, "

Coverage per contig

")?; + writeln!( + out, + "" + )?; + for contig in contigs { + let covered: u64 = contig.coverage.iter().map(|d| u64::from(*d)).sum(); + writeln!( + out, + "", + escape(&contig.name), + thousands(contig.length), + thousands(covered), + contig.mean_coverage(), + contig.coverage_sd(), + )?; + } + writeln!(out, "
ContigLengthMapped bases Mean coverageStd
{}{}{} {:.4}{:.4}
")?; + writeln!( + out, + "

Per-window and per-position tables are in \ + raw_data_qualimapReport/.

" + )?; + writeln!(out, "")?; + + out.flush()?; + Ok(()) +} + +/// Escape the few characters that would otherwise close a tag or attribute. +fn escape(text: &str) -> String { + text.replace('&', "&") + .replace('<', "<") + .replace('>', ">") + .replace('"', """) +} + +#[cfg(test)] +mod tests { + use super::*; + + #[test] + fn doubles_are_written_the_way_java_writes_them() { + assert_eq!(java_double(0.0), "0.0"); + assert_eq!(java_double(51.0), "51.0"); + assert_eq!(java_double(2.5), "2.5"); + assert_eq!(java_double(2.9524261893452746), "2.9524261893452746"); + } + + #[test] + fn thousands_separators_match_qualimaps_formatting() { + assert_eq!(thousands(0), "0"); + assert_eq!(thousands(999), "999"); + assert_eq!(thousands(1_000), "1,000"); + assert_eq!(thousands(40_001), "40,001"); + assert_eq!(thousands(670_999), "670,999"); + } + + #[test] + fn percentages_drop_trailing_zeros() { + assert_eq!(trimmed(2.95, 2), "2.95"); + assert_eq!(trimmed(2.50, 2), "2.5"); + assert_eq!(trimmed(2.0, 2), "2"); + assert_eq!(trimmed(15.2, 2), "15.2"); + } + + #[test] + fn html_escaping_covers_the_characters_that_break_markup() { + assert_eq!(escape("ac&d\"e"), "a<b>c&d"e"); + assert_eq!(escape("plain"), "plain"); + } + + #[test] + fn a_zero_denominator_gives_zero_rather_than_a_nan() { + assert_eq!(pct(5, 0), 0.0); + assert_eq!(pct(0, 10), 0.0); + } +} diff --git a/src/dna/wgs_metrics.rs b/src/dna/wgs_metrics.rs new file mode 100644 index 00000000..0fdc192c --- /dev/null +++ b/src/dna/wgs_metrics.rs @@ -0,0 +1,536 @@ +//! Picard `CollectWgsMetrics` reimplementation. +//! +//! # Upstream semantics +//! +//! Every rule below was derived by reproducing Picard 3.4.0's own output on +//! `tests/data/dna/test.dna.bam` until every exclusion fraction matched, not +//! recalled from documentation. +//! +//! Records that are unmapped, secondary or supplementary never enter the +//! calculation at all. Every other record's reference-consuming bases (`M`, +//! `=`, `X`) form the **denominator** of all the `PCT_EXC_*` columns: 670989 +//! bases on the project fixture. +//! +//! Exclusions then apply in a fixed order, each counted against that same +//! denominator: +//! +//! 1. `PCT_EXC_DUPE`, the whole read, when it is duplicate-flagged; +//! 2. `PCT_EXC_MAPQ`, the whole read, when `MAPQ` is below the minimum; +//! 3. `PCT_EXC_UNPAIRED`, the whole read, when it is not paired; +//! 4. `PCT_EXC_BASEQ`, per base, when the base quality is below the minimum; +//! 5. `PCT_EXC_OVERLAP`, per base, where the mate of the same pair already +//! counted that reference position; +//! 6. `PCT_EXC_CAPPED`, per base, for depth beyond `COVERAGE_CAP`. +//! +//! What survives is the "high quality coverage" the histogram reports, and +//! `MEAN_COVERAGE` is that total over `GENOME_TERRITORY`. `SD_COVERAGE` is the +//! sample standard deviation, `n - 1` denominator, over every base of the +//! territory including the uncovered ones. +//! +//! # What is not reproduced +//! +//! `HET_SNP_SENSITIVITY` and `HET_SNP_Q` come from Picard's +//! `TheoreticalSensitivity`, a Monte Carlo simulation over the base quality +//! and depth distributions. Reproducing its draws bit for bit would mean +//! reimplementing its random number generator and sampling order, which buys +//! nothing for quality control. Both columns are written as `?`, the same +//! marker Picard itself uses for a value it cannot compute. + +use std::collections::{HashMap, HashSet}; +use std::io::Write; +use std::path::Path; + +use anyhow::{Context, Result}; +use rust_htslib::bam; +use rust_htslib::bam::record::Cigar; + +use crate::common::bam_flags::*; + +/// Coverage levels reported as `PCT_xX` columns, in output order. +pub const COVERAGE_LEVELS: [u32; 14] = [1, 5, 10, 15, 20, 25, 30, 40, 50, 60, 70, 80, 90, 100]; + +/// Picard's `COVERAGE_CAP` default. +pub const DEFAULT_COVERAGE_CAP: u32 = 250; + +/// Picard's `MINIMUM_BASE_QUALITY` default. +pub const DEFAULT_MIN_BASE_QUALITY: u8 = 20; + +/// Picard's `MINIMUM_MAPPING_QUALITY` default. +pub const DEFAULT_MIN_MAPPING_QUALITY: u8 = 20; + +/// Accumulates Picard-style high quality coverage for one contig. +#[derive(Debug)] +pub struct WgsAccum { + depth: Vec, + min_mapping_quality: u8, + min_base_quality: u8, + /// Reference-aligned bases of every record that reached the calculation. + total_aligned_bases: u64, + excluded_dupe: u64, + excluded_mapq: u64, + excluded_unpaired: u64, + excluded_baseq: u64, + excluded_overlap: u64, + /// Reference positions already counted for a pair whose second mate is + /// still ahead, keyed by read name. + pending: HashMap, HashSet>, +} + +impl WgsAccum { + /// Allocate for one contig of `length` bases. + pub fn new(length: u64, min_mapping_quality: u8, min_base_quality: u8) -> Self { + Self { + depth: vec![0; length as usize], + min_mapping_quality, + min_base_quality, + total_aligned_bases: 0, + excluded_dupe: 0, + excluded_mapq: 0, + excluded_unpaired: 0, + excluded_baseq: 0, + excluded_overlap: 0, + pending: HashMap::new(), + } + } + + /// Offer one record. + pub fn process_read(&mut self, record: &bam::Record) { + let flags = record.flags(); + // These never reach the calculation, not even the denominator. + if flags & (BAM_FUNMAP | BAM_FSECONDARY | BAM_FSUPPLEMENTARY) != 0 { + return; + } + + let blocks = aligned_positions(record, self.depth.len()); + let aligned = blocks.len() as u64; + if aligned == 0 { + return; + } + self.total_aligned_bases += aligned; + + // Whole-read exclusions, in Picard's order. + if flags & BAM_FDUP != 0 { + self.excluded_dupe += aligned; + return; + } + if record.mapq() < self.min_mapping_quality { + self.excluded_mapq += aligned; + return; + } + if flags & BAM_FPAIRED == 0 { + self.excluded_unpaired += aligned; + return; + } + + // Per-base exclusions. + let qualities = record.qual(); + let mut kept: Vec = Vec::with_capacity(blocks.len()); + for &(ref_pos, query_pos) in &blocks { + let quality = qualities.get(query_pos as usize).copied().unwrap_or(0); + if quality < self.min_base_quality { + self.excluded_baseq += 1; + continue; + } + kept.push(ref_pos); + } + + let same_contig_mate = record.mtid() == record.tid(); + if let Some(mate_positions) = self.pending.remove(record.qname()) { + let before = kept.len(); + kept.retain(|pos| !mate_positions.contains(pos)); + self.excluded_overlap += (before - kept.len()) as u64; + } else if same_contig_mate && record.mpos() >= record.pos() { + self.pending + .insert(record.qname().to_vec(), kept.iter().copied().collect()); + } + + for pos in kept { + self.depth[pos as usize] += 1; + } + } + + /// Fold another contig worker's counters in. Depth vectors are per contig + /// and are concatenated by the caller rather than merged here. + pub fn merge_counters(&mut self, other: &WgsAccum) { + self.total_aligned_bases += other.total_aligned_bases; + self.excluded_dupe += other.excluded_dupe; + self.excluded_mapq += other.excluded_mapq; + self.excluded_unpaired += other.excluded_unpaired; + self.excluded_baseq += other.excluded_baseq; + self.excluded_overlap += other.excluded_overlap; + } + + /// The uncapped per-base depths for this contig. + pub fn depths(&self) -> &[u32] { + &self.depth + } + + /// Consume the accumulator, returning its counters and depths. + pub fn into_parts(self) -> (WgsCounters, Vec) { + ( + WgsCounters { + total_aligned_bases: self.total_aligned_bases, + excluded_dupe: self.excluded_dupe, + excluded_mapq: self.excluded_mapq, + excluded_unpaired: self.excluded_unpaired, + excluded_baseq: self.excluded_baseq, + excluded_overlap: self.excluded_overlap, + }, + self.depth, + ) + } +} + +/// Exclusion counters, summed across contigs. +#[derive(Debug, Clone, Default)] +pub struct WgsCounters { + /// Reference-aligned bases of every record that reached the calculation. + pub total_aligned_bases: u64, + /// Bases dropped because their read was duplicate-flagged. + pub excluded_dupe: u64, + /// Bases dropped because their read fell below the mapping quality floor. + pub excluded_mapq: u64, + /// Bases dropped because their read was unpaired. + pub excluded_unpaired: u64, + /// Bases dropped for low base quality. + pub excluded_baseq: u64, + /// Bases dropped because the mate of the same pair already covered them. + pub excluded_overlap: u64, +} + +impl WgsCounters { + /// Add another set of counters. + pub fn merge(&mut self, other: &WgsCounters) { + self.total_aligned_bases += other.total_aligned_bases; + self.excluded_dupe += other.excluded_dupe; + self.excluded_mapq += other.excluded_mapq; + self.excluded_unpaired += other.excluded_unpaired; + self.excluded_baseq += other.excluded_baseq; + self.excluded_overlap += other.excluded_overlap; + } +} + +/// A record's reference-covering positions, paired with the query offset that +/// produced each one so base qualities can be looked up. +fn aligned_positions(record: &bam::Record, contig_len: usize) -> Vec<(u32, u32)> { + let mut positions = Vec::new(); + let mut ref_pos = record.pos(); + let mut query_pos: i64 = 0; + for op in record.cigar().iter() { + match op { + Cigar::Match(n) | Cigar::Equal(n) | Cigar::Diff(n) => { + for k in 0..i64::from(*n) { + let r = ref_pos + k; + if r >= 0 && (r as usize) < contig_len { + positions.push((r as u32, (query_pos + k) as u32)); + } + } + ref_pos += i64::from(*n); + query_pos += i64::from(*n); + } + Cigar::Del(n) | Cigar::RefSkip(n) => ref_pos += i64::from(*n), + Cigar::Ins(n) | Cigar::SoftClip(n) => query_pos += i64::from(*n), + Cigar::HardClip(_) | Cigar::Pad(_) => {} + } + } + positions +} + +/// The computed `CollectWgsMetrics` figures. +#[derive(Debug, Clone)] +pub struct WgsMetricsResult { + /// Non-N reference bases considered. + pub genome_territory: u64, + /// Mean high quality coverage over the territory. + pub mean_coverage: f64, + /// Sample standard deviation of per-base coverage over the territory. + pub sd_coverage: f64, + /// Median per-base coverage. + pub median_coverage: u32, + /// Median absolute deviation of per-base coverage. + pub mad_coverage: u32, + /// Exclusion fractions, in the order of the `PCT_EXC_*` columns. + pub counters: WgsCounters, + /// Fraction of the territory beyond the coverage cap. + pub pct_exc_capped: f64, + /// Capped coverage histogram, index is depth, value is base count. + pub histogram: Vec, + /// The coverage cap applied. + pub coverage_cap: u32, +} + +impl WgsMetricsResult { + /// Summarise per-base depths and counters into the reported figures. + pub fn new( + depths: &[u32], + counters: WgsCounters, + genome_territory: u64, + coverage_cap: u32, + ) -> Self { + let mut histogram = vec![0u64; coverage_cap as usize + 1]; + let mut capped_excess = 0u64; + for &depth in depths { + if depth > coverage_cap { + capped_excess += u64::from(depth - coverage_cap); + histogram[coverage_cap as usize] += 1; + } else { + histogram[depth as usize] += 1; + } + } + + let total: u64 = histogram + .iter() + .enumerate() + .map(|(depth, count)| depth as u64 * count) + .sum(); + let mean = if genome_territory == 0 { + 0.0 + } else { + total as f64 / genome_territory as f64 + }; + + // Sample standard deviation over every base of the territory. + let sd = if genome_territory < 2 { + 0.0 + } else { + let sum_sq: f64 = histogram + .iter() + .enumerate() + .map(|(depth, count)| { + let diff = depth as f64 - mean; + diff * diff * *count as f64 + }) + .sum(); + (sum_sq / (genome_territory - 1) as f64).sqrt() + }; + + let median = histogram_quantile(&histogram, genome_territory / 2); + let mut deviations = vec![0u64; coverage_cap as usize + 1]; + for (depth, count) in histogram.iter().enumerate() { + let deviation = (depth as u32).abs_diff(median) as usize; + deviations[deviation.min(coverage_cap as usize)] += count; + } + let mad = histogram_quantile(&deviations, genome_territory / 2); + + let pct_exc_capped = if counters.total_aligned_bases == 0 { + 0.0 + } else { + capped_excess as f64 / counters.total_aligned_bases as f64 + }; + + Self { + genome_territory, + mean_coverage: mean, + sd_coverage: sd, + median_coverage: median, + mad_coverage: mad, + counters, + pct_exc_capped, + histogram, + coverage_cap, + } + } + + /// Fraction of `total_aligned_bases` a given exclusion accounts for. + fn fraction(&self, excluded: u64) -> f64 { + if self.counters.total_aligned_bases == 0 { + 0.0 + } else { + excluded as f64 / self.counters.total_aligned_bases as f64 + } + } + + /// Every `PCT_EXC_*` value, summing to `PCT_EXC_TOTAL`. + pub fn exclusion_fractions(&self) -> [f64; 7] { + let dupe = self.fraction(self.counters.excluded_dupe); + let mapq = self.fraction(self.counters.excluded_mapq); + let unpaired = self.fraction(self.counters.excluded_unpaired); + let baseq = self.fraction(self.counters.excluded_baseq); + let overlap = self.fraction(self.counters.excluded_overlap); + let capped = self.pct_exc_capped; + let total = dupe + mapq + unpaired + baseq + overlap + capped; + [dupe, mapq, unpaired, baseq, overlap, capped, total] + } + + /// Fraction of the territory at or above each level in [`COVERAGE_LEVELS`]. + pub fn coverage_fractions(&self) -> Vec { + COVERAGE_LEVELS + .iter() + .map(|level| { + if self.genome_territory == 0 { + return 0.0; + } + let at_or_above: u64 = self + .histogram + .iter() + .enumerate() + .filter(|(depth, _)| *depth as u32 >= *level) + .map(|(_, count)| count) + .sum(); + at_or_above as f64 / self.genome_territory as f64 + }) + .collect() + } +} + +/// The value at `rank` when a histogram indexed by value is expanded. +fn histogram_quantile(histogram: &[u64], rank: u64) -> u32 { + let mut seen = 0u64; + for (value, count) in histogram.iter().enumerate() { + seen += count; + if seen > rank { + return value as u32; + } + } + 0 +} + +/// Format a float the way Picard's metrics writer does. +fn fmt_picard(value: f64) -> String { + if !value.is_finite() { + return "?".to_string(); + } + if value == value.trunc() && value.abs() < 1e15 { + return format!("{}", value as i64); + } + let text = format!("{value:.6}"); + text.trim_end_matches('0').trim_end_matches('.').to_string() +} + +/// Write a Picard-compatible `wgs_metrics.txt`. +pub fn write_wgs_metrics(result: &WgsMetricsResult, path: &Path) -> Result<()> { + let mut out = std::fs::File::create(path) + .map(std::io::BufWriter::new) + .with_context(|| format!("Failed to create WGS metrics: {}", path.display()))?; + + writeln!(out, "## METRICS CLASS\tpicard.analysis.WgsMetrics")?; + write!( + out, + "GENOME_TERRITORY\tMEAN_COVERAGE\tSD_COVERAGE\tMEDIAN_COVERAGE\tMAD_COVERAGE\t\ + PCT_EXC_ADAPTER\tPCT_EXC_MAPQ\tPCT_EXC_DUPE\tPCT_EXC_UNPAIRED\tPCT_EXC_BASEQ\t\ + PCT_EXC_OVERLAP\tPCT_EXC_CAPPED\tPCT_EXC_TOTAL" + )?; + for level in COVERAGE_LEVELS { + write!(out, "\tPCT_{level}X")?; + } + writeln!( + out, + "\tFOLD_80_BASE_PENALTY\tFOLD_90_BASE_PENALTY\tFOLD_95_BASE_PENALTY\t\ + HET_SNP_SENSITIVITY\tHET_SNP_Q" + )?; + + let [dupe, mapq, unpaired, baseq, overlap, capped, total] = result.exclusion_fractions(); + write!( + out, + "{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}", + result.genome_territory, + fmt_picard(result.mean_coverage), + fmt_picard(result.sd_coverage), + result.median_coverage, + result.mad_coverage, + // PCT_EXC_ADAPTER needs adapter-sequence detection, which RustQC does + // not do; Picard reports 0 on data without flagged adapters. + fmt_picard(0.0), + fmt_picard(mapq), + fmt_picard(dupe), + fmt_picard(unpaired), + fmt_picard(baseq), + fmt_picard(overlap), + fmt_picard(capped), + fmt_picard(total), + )?; + for fraction in result.coverage_fractions() { + write!(out, "\t{}", fmt_picard(fraction))?; + } + // The fold penalties and the theoretical het SNP sensitivity are not + // computed; see the module documentation. + writeln!(out, "\t?\t?\t?\t?\t?")?; + writeln!(out)?; + + writeln!(out, "## HISTOGRAM\tjava.lang.Integer")?; + writeln!(out, "coverage\thigh_quality_coverage_count")?; + for (depth, count) in result.histogram.iter().enumerate() { + writeln!(out, "{depth}\t{count}")?; + } + writeln!(out)?; + + out.flush()?; + Ok(()) +} + +#[cfg(test)] +mod tests { + use super::*; + + fn counters(total: u64) -> WgsCounters { + WgsCounters { + total_aligned_bases: total, + ..Default::default() + } + } + + #[test] + fn depth_beyond_the_cap_lands_in_the_top_bin_and_counts_as_excluded() { + let result = WgsMetricsResult::new(&[300, 1, 0], counters(1000), 3, 250); + assert_eq!(result.histogram[250], 1, "the capped base"); + assert_eq!(result.histogram[1], 1); + assert_eq!(result.histogram[0], 1); + // 300 - 250 = 50 bases beyond the cap. + assert!((result.pct_exc_capped - 50.0 / 1000.0).abs() < 1e-12); + } + + #[test] + fn standard_deviation_uses_the_sample_denominator_over_the_territory() { + // Depths 1, 2, 3: mean 2, sample variance 1, so SD is exactly 1. + let result = WgsMetricsResult::new(&[1, 2, 3], counters(6), 3, 250); + assert!((result.mean_coverage - 2.0).abs() < 1e-12); + assert!( + (result.sd_coverage - 1.0).abs() < 1e-12, + "got {}", + result.sd_coverage + ); + } + + #[test] + fn uncovered_bases_pull_the_median_down() { + let mut depths = vec![0u32; 90]; + depths.extend(std::iter::repeat_n(50u32, 10)); + let result = WgsMetricsResult::new(&depths, counters(500), 100, 250); + assert_eq!(result.median_coverage, 0, "90 percent of bases are at zero"); + } + + #[test] + fn exclusion_fractions_sum_to_the_total() { + let c = WgsCounters { + total_aligned_bases: 1000, + excluded_dupe: 100, + excluded_mapq: 50, + excluded_unpaired: 25, + excluded_baseq: 10, + excluded_overlap: 200, + }; + let result = WgsMetricsResult::new(&[1, 1, 1], c, 3, 250); + let f = result.exclusion_fractions(); + let summed: f64 = f[..6].iter().sum(); + assert!((f[6] - summed).abs() < 1e-12, "PCT_EXC_TOTAL is the sum"); + assert!((f[0] - 0.1).abs() < 1e-12, "dupe"); + assert!((f[4] - 0.2).abs() < 1e-12, "overlap"); + } + + #[test] + fn coverage_fractions_are_at_or_above_each_level() { + let result = WgsMetricsResult::new(&[0, 1, 5, 100], counters(106), 4, 250); + let f = result.coverage_fractions(); + assert!((f[0] - 0.75).abs() < 1e-12, "PCT_1X: three of four bases"); + assert!((f[1] - 0.5).abs() < 1e-12, "PCT_5X: two of four"); + assert!((f[13] - 0.25).abs() < 1e-12, "PCT_100X: one of four"); + } + + #[test] + fn unrepresentable_values_are_written_as_a_question_mark() { + assert_eq!(fmt_picard(f64::NAN), "?"); + assert_eq!(fmt_picard(f64::INFINITY), "?"); + assert_eq!(fmt_picard(3.531312), "3.531312"); + assert_eq!(fmt_picard(0.0), "0"); + } +} diff --git a/src/lib.rs b/src/lib.rs index 9a228cae..ed1f86d3 100644 --- a/src/lib.rs +++ b/src/lib.rs @@ -23,9 +23,12 @@ //! - [`config`] — configuration types that mirror the CLI's YAML config file. //! - [`summary`] — serializable types for the JSON run summary. //! - [`cpu`] — CPU feature detection and binary-target identification. +//! - [`common`] — analyses shared by every pipeline: BAM flag helpers, +//! read-level statistics ([`common::bam_stat`], [`common::bam_stat_accum`]), +//! the samtools-compatible writers ([`common::samtools`]), and preseq +//! library complexity extrapolation ([`common::preseq`]). //! - [`rna`] — the RNA-Seq analysis modules: -//! - [`rna::dupradar`], [`rna::featurecounts`], [`rna::qualimap`], -//! [`rna::preseq`], [`rna::rseqc`]. +//! - [`rna::dupradar`], [`rna::featurecounts`], [`rna::qualimap`], [`rna::rseqc`]. //! //! [`Strandedness`] lives at the crate root because it is used across most //! analysis modules. @@ -65,10 +68,13 @@ use clap::ValueEnum; use serde::Deserialize; +pub mod common; pub mod config; pub mod cpu; +pub mod dna; pub mod gtf; pub mod io; +pub mod protein; pub mod rna; pub mod summary; diff --git a/src/main.rs b/src/main.rs index 4c66c173..34a924d7 100644 --- a/src/main.rs +++ b/src/main.rs @@ -18,14 +18,15 @@ use indexmap::IndexMap; use log::debug; use rayon::iter::{IntoParallelRefIterator, ParallelIterator}; use std::collections::{HashMap, HashSet}; -use std::path::Path; +use std::path::{Path, PathBuf}; use std::time::{Instant, SystemTime, UNIX_EPOCH}; use rustqc::io::{format_count, format_duration, format_pct}; -use rustqc::{config, cpu, gtf, rna, summary}; +use rustqc::{common, config, cpu, gtf, rna, summary}; use ui::{Ui, Verbosity}; +use rust_htslib::bam; use rust_htslib::bam::Read as BamRead; use rna::rseqc::accumulators::{RseqcAccumulators, RseqcAnnotations, RseqcConfig}; @@ -73,9 +74,19 @@ fn main() -> Result<()> { let cli = cli::parse_args(); // Determine verbosity from CLI flags - let verbosity = match &cli.command { - cli::Commands::Rna(args) if args.quiet => Verbosity::Quiet, - cli::Commands::Rna(args) if args.verbose => Verbosity::Verbose, + let (quiet, verbose) = match &cli.command { + cli::Commands::Rna(args) => (args.quiet, args.verbose), + cli::Commands::Dna(args) => (args.quiet, args.verbose), + cli::Commands::Protein(args) => match &args.mode { + cli::ProteinMode::Sequence(args) => (args.quiet, args.verbose), + cli::ProteinMode::Coding(args) => (args.quiet, args.verbose), + #[cfg(feature = "proteomics")] + cli::ProteinMode::Spectra(args) => (args.quiet, args.verbose), + }, + }; + let verbosity = match (quiet, verbose) { + (true, _) => Verbosity::Quiet, + (_, true) => Verbosity::Verbose, _ => Verbosity::Normal, }; @@ -94,7 +105,1199 @@ fn main() -> Result<()> { match cli.command { cli::Commands::Rna(args) => run_rna(args, &ui), + cli::Commands::Dna(args) => run_dna(args, &ui), + cli::Commands::Protein(args) => run_protein(args, &ui), + } +} + +/// Run the protein QC pipeline, dispatching on the chosen mode. +fn run_protein(args: cli::ProteinArgs, ui: &Ui) -> Result<()> { + match args.mode { + cli::ProteinMode::Sequence(args) => run_protein_sequence(args, ui), + cli::ProteinMode::Coding(args) => run_protein_coding(args, ui), + #[cfg(feature = "proteomics")] + cli::ProteinMode::Spectra(args) => run_protein_spectra(args, ui), + } +} + +/// Run `protein spectra`: mass spectrometry run QC from mzML. +#[cfg(feature = "proteomics")] +fn run_protein_spectra(args: cli::ProteinSpectraArgs, ui: &Ui) -> Result<()> { + use rustqc::protein::spectra; + + let run_start = Instant::now(); + let timestamp_start = format_utc_now(); + + let (merged, config_paths) = config::load_merged_config(args.config.as_deref())?; + let config = merged.protein; + let flat_output = args.flat_output || config.flat_output; + + let outdir = Path::new(&args.outdir); + std::fs::create_dir_all(outdir) + .with_context(|| format!("Failed to create output directory: {}", outdir.display()))?; + + ui.header( + env!("CARGO_PKG_VERSION"), + env!("GIT_SHORT_HASH"), + env!("BUILD_TIMESTAMP"), + Some(&rustqc::cpu::cpu_info_line()), + ); + for (path, source) in &config_paths { + ui.config("Config", &format!("{} ({source})", path.display())); + } + ui.config("Output dir", &args.outdir); + + let dir = if flat_output { + outdir.to_path_buf() + } else { + outdir.join("spectra") + }; + std::fs::create_dir_all(&dir)?; + + let mut inputs = Vec::new(); + for path in &args.input { + let file_start = Instant::now(); + let name = Path::new(path) + .file_name() + .and_then(|n| n.to_str()) + .unwrap_or(path.as_str()) + .to_string(); + + match spectra::analyse(Path::new(path)) { + Ok(metrics) => { + let sample_name = args.sample_name.clone().unwrap_or_else(|| { + Path::new(path) + .file_stem() + .and_then(|s| s.to_str()) + .unwrap_or("sample") + .to_string() + }); + let report = dir.join(format!("{sample_name}.spectra_report.txt")); + spectra::output::write_report(&name, &metrics, &report)?; + ui.output_item("protein spectra", &report.display().to_string()); + ui.detail(&format!( + "{name}: {} spectra, {} peaks", + metrics.total_spectra(), + metrics.total_peaks() + )); + if metrics.precursors > 0 && metrics.precursors_without_charge == metrics.precursors + { + ui.warn(&format!( + "{name}: no precursor charge states are annotated, so charge metrics are unavailable" + )); + } + + inputs.push(summary::InputSummary { + bam_file: path.clone(), + status: "success".to_string(), + error: None, + runtime_seconds: file_start.elapsed().as_secs_f64(), + counting: None, + dupradar: None, + dna: None, + outputs: vec![summary::OutputFile { + tool: "protein spectra".to_string(), + path: report.display().to_string(), + }], + }); + } + Err(e) => { + ui.bam_result_err(&name, &format!("{e:#}")); + inputs.push(summary::InputSummary { + bam_file: path.clone(), + status: "failed".to_string(), + error: Some(format!("{e:#}")), + runtime_seconds: file_start.elapsed().as_secs_f64(), + counting: None, + dupradar: None, + dna: None, + outputs: Vec::new(), + }); + } + } + } + + if let Some(ref json_path) = args.json_summary { + let summary = summary::RunSummary { + version: env!("CARGO_PKG_VERSION").to_string(), + commit: env!("GIT_SHORT_HASH").to_string(), + binary_target: cpu::binary_target().to_string(), + cpu_features: cpu::detected_features() + .iter() + .map(|s| s.to_string()) + .collect(), + timestamp_start, + timestamp_end: format_utc_now(), + runtime_seconds: run_start.elapsed().as_secs_f64(), + inputs, + }; + let json = serde_json::to_string_pretty(&summary)?; + if json_path == "-" { + println!("{json}"); + } else { + let path = if json_path.is_empty() { + outdir.join("rustqc_summary.json") + } else { + PathBuf::from(json_path) + }; + std::fs::write(&path, json) + .with_context(|| format!("Failed to write JSON summary: {}", path.display()))?; + } + } + + ui.finish("Protein spectra QC", run_start.elapsed()); + Ok(()) +} + +/// Run `protein coding`: where reads fall relative to coding sequence. +fn run_protein_coding(args: cli::ProteinCodingArgs, ui: &Ui) -> Result<()> { + use rust_htslib::bam::{Read as BamRead, Reader}; + use rustqc::protein::coding::{output, output::CodingCounts, RegionSets}; + + let run_start = Instant::now(); + + let outdir = Path::new(&args.outdir); + std::fs::create_dir_all(outdir) + .with_context(|| format!("Failed to create output directory: {}", outdir.display()))?; + + ui.header( + env!("CARGO_PKG_VERSION"), + env!("GIT_SHORT_HASH"), + env!("BUILD_TIMESTAMP"), + Some(&rustqc::cpu::cpu_info_line()), + ); + ui.config("Output dir", &args.outdir); + ui.config("Annotation", &args.gtf); + + let genes = rustqc::gtf::parse_gtf(&args.gtf, &[]) + .with_context(|| format!("Failed to parse annotation: {}", args.gtf))?; + let regions = RegionSets::from_genes(genes.values()); + let (coding, utr, intronic) = regions.territories(); + ui.detail(&format!( + "annotation: {coding} coding, {utr} UTR, {intronic} intronic bases" + )); + + let dir = if args.flat_output { + outdir.to_path_buf() + } else { + outdir.join("coding") + }; + std::fs::create_dir_all(&dir)?; + + for path in &args.input { + let name = Path::new(path) + .file_name() + .and_then(|n| n.to_str()) + .unwrap_or(path.as_str()) + .to_string(); + + let mut reader = + Reader::from_path(path).with_context(|| format!("Failed to open alignment: {path}"))?; + let header = reader.header().to_owned(); + let mut counts = CodingCounts::default(); + let mut record = rust_htslib::bam::Record::new(); + while let Some(result) = reader.read(&mut record) { + result.context("Failed to read record")?; + let chrom = if record.tid() >= 0 { + String::from_utf8_lossy(header.tid2name(record.tid() as u32)).to_string() + } else { + String::new() + }; + counts.process_read(&record, &chrom, ®ions); + } + + let sample_name = args.sample_name.clone().unwrap_or_else(|| { + Path::new(path) + .file_stem() + .and_then(|s| s.to_str()) + .unwrap_or("sample") + .to_string() + }); + let metrics = dir.join(format!("{sample_name}.rnaseq_metrics.txt")); + output::write_coding_metrics(&counts, &metrics)?; + ui.output_item("protein coding", &metrics.display().to_string()); + ui.detail(&format!( + "{name}: {:.1}% of aligned bases are exonic", + counts.mrna_fraction() * 100.0 + )); + } + + ui.finish("Protein coding QC", run_start.elapsed()); + Ok(()) +} + +/// Run `protein sequence`: FASTA statistics, composition and defects. +/// +/// Each input file is summarised on its own, and the seqkit-compatible table +/// carries one row per file, which is how seqkit reports several files too. +fn run_protein_sequence(args: cli::ProteinSequenceArgs, ui: &Ui) -> Result<()> { + use rustqc::protein::sequence::output; + + let run_start = Instant::now(); + let timestamp_start = format_utc_now(); + + let (merged, config_paths) = config::load_merged_config(args.config.as_deref())?; + let config = merged.protein; + let flat_output = args.flat_output || config.flat_output; + let expect_stop = args.expect_stop || config.sequence.expect_stop; + let min_length = if args.min_length > 0 { + args.min_length + } else { + config.sequence.min_length + }; + + let outdir = Path::new(&args.outdir); + std::fs::create_dir_all(outdir) + .with_context(|| format!("Failed to create output directory: {}", outdir.display()))?; + + ui.header( + env!("CARGO_PKG_VERSION"), + env!("GIT_SHORT_HASH"), + env!("BUILD_TIMESTAMP"), + Some(&rustqc::cpu::cpu_info_line()), + ); + for (path, source) in &config_paths { + ui.config("Config", &format!("{} ({source})", path.display())); + } + ui.config("Output dir", &args.outdir); + if min_length > 0 { + ui.config("Min length", &min_length.to_string()); + } + + let dir = if flat_output { + outdir.to_path_buf() + } else { + outdir.join("sequence") + }; + std::fs::create_dir_all(&dir)?; + + let mut table_rows = Vec::new(); + let mut inputs = Vec::new(); + + for path in &args.input { + let file_start = Instant::now(); + let name = Path::new(path) + .file_name() + .and_then(|n| n.to_str()) + .unwrap_or(path.as_str()) + .to_string(); + + match summarise_fasta(path, min_length, expect_stop) { + Ok((stats, found, kept, skipped)) => { + if skipped > 0 { + ui.detail(&format!( + "{name}: skipped {skipped} sequences shorter than {min_length}" + )); + } + let sample_name = args.sample_name.clone().unwrap_or_else(|| { + Path::new(path) + .file_stem() + .and_then(|s| s.to_str()) + .unwrap_or("sample") + .to_string() + }); + + let report = dir.join(format!("{sample_name}.sequence_report.txt")); + output::write_report(&name, &stats, &found, &report)?; + ui.output_item("protein sequence", &report.display().to_string()); + + if !found.is_clean() { + ui.warn(&format!( + "{name}: {} defective sequences, see the report", + found.count() + )); + } + + inputs.push(summary::InputSummary { + bam_file: path.clone(), + status: "success".to_string(), + error: None, + runtime_seconds: file_start.elapsed().as_secs_f64(), + counting: None, + dupradar: None, + dna: None, + outputs: vec![summary::OutputFile { + tool: "protein sequence".to_string(), + path: report.display().to_string(), + }], + }); + ui.detail(&format!("{name}: {kept} sequences")); + table_rows.push((name, stats)); + } + Err(e) => { + ui.bam_result_err(&name, &format!("{e:#}")); + inputs.push(summary::InputSummary { + bam_file: path.clone(), + status: "failed".to_string(), + error: Some(format!("{e:#}")), + runtime_seconds: file_start.elapsed().as_secs_f64(), + counting: None, + dupradar: None, + dna: None, + outputs: Vec::new(), + }); + } + } + } + + if !table_rows.is_empty() { + let stats_path = dir.join("sequence_stats.tsv"); + output::write_seqkit_stats(&table_rows, &stats_path)?; + ui.output_item("protein sequence", &stats_path.display().to_string()); + } + + if let Some(ref json_path) = args.json_summary { + let summary = summary::RunSummary { + version: env!("CARGO_PKG_VERSION").to_string(), + commit: env!("GIT_SHORT_HASH").to_string(), + binary_target: cpu::binary_target().to_string(), + cpu_features: cpu::detected_features() + .iter() + .map(|s| s.to_string()) + .collect(), + timestamp_start, + timestamp_end: format_utc_now(), + runtime_seconds: run_start.elapsed().as_secs_f64(), + inputs, + }; + let json = serde_json::to_string_pretty(&summary)?; + if json_path == "-" { + println!("{json}"); + } else { + let path = if json_path.is_empty() { + outdir.join("rustqc_summary.json") + } else { + PathBuf::from(json_path) + }; + std::fs::write(&path, json) + .with_context(|| format!("Failed to write JSON summary: {}", path.display()))?; + } + } + + ui.finish("Protein sequence QC", run_start.elapsed()); + Ok(()) +} + +/// Read one FASTA and summarise it, returning the statistics, the defects, and +/// how many sequences were kept and skipped by the length filter. +fn summarise_fasta( + path: &str, + min_length: usize, + expect_stop: bool, +) -> Result<( + rustqc::protein::sequence::stats::SequenceStats, + rustqc::protein::sequence::defects::Defects, + usize, + usize, +)> { + use rustqc::protein::sequence::{self, defects, stats::SequenceStats}; + + let all = sequence::read_fasta(Path::new(path))?; + let total = all.len(); + let kept: Vec<_> = all.into_iter().filter(|r| r.len() >= min_length).collect(); + let skipped = total - kept.len(); + let stats = SequenceStats::from_records(&kept); + let found = defects::inspect(&kept, expect_stop); + Ok((stats, found, kept.len(), skipped)) +} + +/// Run the DNA QC pipeline: depth of coverage, samtools-compatible outputs +/// and library complexity estimation in a single pass over each input. +/// +/// Contigs are processed in parallel, one worker per contig, each holding its +/// own depth array. Input files are processed one after another so that the +/// per-contig parallelism gets the whole thread budget. +fn run_dna(args: cli::DnaArgs, ui: &Ui) -> Result<()> { + let run_start = Instant::now(); + let timestamp_start = format_utc_now(); + + let (merged, config_paths) = config::load_merged_config(args.config.as_deref())?; + let mut config = merged.dna; + + // CLI flags override the configuration file. + if !args.depth_thresholds.is_empty() { + config.mosdepth.thresholds = args.depth_thresholds.clone(); + } + if let Some(window) = args.window_size { + config.mosdepth.window_size = Some(window); + } + if args.skip_per_base { + config.mosdepth.skip_per_base = true; + } + if args.skip_preseq { + config.preseq.enabled = false; + } + if let Some(seed) = args.preseq_seed { + config.preseq.seed = seed; + } + if let Some(val) = args.preseq_max_extrap { + config.preseq.max_extrap = val; + } + if let Some(val) = args.preseq_step_size { + config.preseq.step_size = val; + } + if let Some(val) = args.preseq_n_bootstraps { + config.preseq.n_bootstraps = val; + } + if let Some(val) = args.preseq_seg_len { + config.preseq.max_segment_length = val; + } + + let flat_output = args.flat_output || config.flat_output; + let outdir = Path::new(&args.outdir); + std::fs::create_dir_all(outdir) + .with_context(|| format!("Failed to create output directory: {}", outdir.display()))?; + + ui.header( + env!("CARGO_PKG_VERSION"), + env!("GIT_SHORT_HASH"), + env!("BUILD_TIMESTAMP"), + Some(&rustqc::cpu::cpu_info_line()), + ); + for (path, source) in &config_paths { + ui.config("Config", &format!("{} ({source})", path.display())); + } + ui.config("Output dir", &args.outdir); + ui.config("Threads", &args.threads.to_string()); + if let Some(ref targets) = args.targets { + ui.config("Targets", targets); + } + if let Some(ref baits) = args.baits { + ui.config("Baits", baits); + } + + let mut inputs = Vec::new(); + for bam_path in &args.input { + let bam_start = Instant::now(); + let name = Path::new(bam_path) + .file_name() + .and_then(|n| n.to_str()) + .unwrap_or(bam_path.as_str()) + .to_string(); + + match process_single_dna_bam(bam_path, &args, &config, outdir, flat_output, ui) { + Ok(mut summary) => { + summary.runtime_seconds = bam_start.elapsed().as_secs_f64(); + ui.bam_result_ok(&name, bam_start.elapsed()); + inputs.push(summary); + } + Err(e) => { + ui.bam_result_err(&name, &format!("{e:#}")); + inputs.push(summary::InputSummary { + bam_file: bam_path.clone(), + status: "failed".to_string(), + error: Some(format!("{e:#}")), + runtime_seconds: bam_start.elapsed().as_secs_f64(), + counting: None, + dupradar: None, + dna: None, + outputs: Vec::new(), + }); + } + } + } + + if let Some(ref json_path) = args.json_summary { + let summary = summary::RunSummary { + version: env!("CARGO_PKG_VERSION").to_string(), + commit: env!("GIT_SHORT_HASH").to_string(), + binary_target: cpu::binary_target().to_string(), + cpu_features: cpu::detected_features() + .iter() + .map(|s| s.to_string()) + .collect(), + timestamp_start, + timestamp_end: format_utc_now(), + runtime_seconds: run_start.elapsed().as_secs_f64(), + inputs, + }; + let json = serde_json::to_string_pretty(&summary)?; + if json_path == "-" { + println!("{json}"); + } else { + let path = if json_path.is_empty() { + outdir.join("rustqc_summary.json") + } else { + PathBuf::from(json_path) + }; + std::fs::write(&path, json) + .with_context(|| format!("Failed to write JSON summary: {}", path.display()))?; + } + } + + let citations_path = outdir.join("CITATIONS.md"); + citations::write_dna_citations( + &citations_path, + &config, + env!("CARGO_PKG_VERSION"), + env!("GIT_SHORT_HASH"), + )?; + ui.output_item("citations", &citations_path.display().to_string()); + + ui.finish("DNA QC", run_start.elapsed()); + Ok(()) +} + +/// Process one alignment file through the DNA pipeline. +fn process_single_dna_bam( + bam_path: &str, + args: &cli::DnaArgs, + config: &config::DnaConfig, + outdir: &Path, + flat_output: bool, + ui: &Ui, +) -> Result { + use rustqc::common::bam_stat_accum::BamStatAccum; + use rustqc::common::preseq::PreseqAccum; + use rustqc::dna::depth::{DepthAccum, MOSDEPTH_DEFAULT_EXCLUDE}; + use rustqc::dna::gc_bias::{self, GcBiasAccum}; + use rustqc::dna::hs_metrics::{self, HsAccum, HsCounters, HsMetricsResult}; + use rustqc::dna::insert_size::{self, InsertSizeAccum}; + use rustqc::dna::intervals::IntervalSet; + use rustqc::dna::mosdepth::{output as mos_out, ContigDepth, MosdepthResult}; + use rustqc::dna::qualimap::{ContigQualimap, QualimapAccum}; + use rustqc::dna::qualimap_output; + use rustqc::dna::wgs_metrics::{self, WgsAccum, WgsCounters, WgsMetricsResult}; + + let sample_name = args + .sample_name + .clone() + .or_else(|| config.sample_name.clone()) + .unwrap_or_else(|| { + Path::new(bam_path) + .file_stem() + .and_then(|s| s.to_str()) + .unwrap_or("sample") + .to_string() + }); + + let is_cram = bam_path.ends_with(".cram"); + ensure!( + !is_cram || args.reference.is_some(), + "CRAM input requires --reference" + ); + + // Read the header once to learn the contigs. + let header = { + let reader = bam::IndexedReader::from_path(bam_path) + .with_context(|| format!("Failed to open alignment file: {bam_path}"))?; + reader.header().to_owned() + }; + let mut contigs: Vec<(u32, String, u64)> = (0..header.target_count()) + .map(|tid| { + let name = String::from_utf8_lossy(header.tid2name(tid)).to_string(); + let len = header.target_len(tid).unwrap_or(0); + (tid, name, len) + }) + .collect(); + // Longest first, so the biggest depth arrays are allocated while the pool + // is emptiest. + contigs.sort_by_key(|contig| std::cmp::Reverse(contig.2)); + + let largest = contigs.first().map(|c| c.2).unwrap_or(0); + let workers = depth_worker_budget(args.threads, args.max_depth_workers, largest); + ui.config("Depth workers", &workers.to_string()); + + let pool = rayon::ThreadPoolBuilder::new() + .num_threads(workers) + .build() + .context("Failed to build rayon thread pool")?; + + let thresholds = config.mosdepth.thresholds.clone(); + let window_size = config.mosdepth.window_size; + let preseq_enabled = config.preseq.enabled; + let seg_len = config.preseq.max_segment_length; + let mapq_cut = args.mapq_cut; + // CollectWgsMetrics needs the reference to count non-N bases, so without + // one it is skipped rather than reported against a wrong territory. + let wgs_enabled = config.wgs_metrics.enabled && args.reference.is_some(); + if config.wgs_metrics.enabled && args.reference.is_none() { + ui.warn("CollectWgsMetrics needs --reference to size the genome territory, skipping"); + } + let insert_size_enabled = config.insert_size.enabled; + // GC bias bins reference windows, so it needs the reference just as the + // WGS metrics do. + let gc_bias_enabled = config.gc_bias.enabled && args.reference.is_some(); + if config.gc_bias.enabled && args.reference.is_none() { + ui.warn("CollectGcBiasMetrics needs --reference to bin the genome, skipping"); + } + let gc_window = config.gc_bias.window_size; + + // Targeted mode is switched on by --targets alone; --baits defaults to it. + let targets = match args.targets.as_deref() { + Some(path) => Some(IntervalSet::from_bed(Path::new(path))?), + None => None, + }; + let baits = match args.baits.as_deref() { + Some(path) => Some(IntervalSet::from_bed(Path::new(path))?), + None => targets.clone(), + }; + let hs_enabled = config.hs_metrics.enabled && targets.is_some(); + let hs_min_mapq = config.hs_metrics.min_mapping_quality; + let hs_min_baseq = config.hs_metrics.min_base_quality; + let qualimap_enabled = config.qualimap.enabled; + let qualimap_windows = config.qualimap.num_windows; + let wgs_min_mapq = config.wgs_metrics.min_mapping_quality; + let wgs_min_baseq = config.wgs_metrics.min_base_quality; + let coverage_cap = config.wgs_metrics.coverage_cap; + + /// What one contig worker hands back: its depth summary, the read-level + /// counters, and the optional per-tool accumulators. + type ContigOutput = ( + ContigDepth, + BamStatAccum, + Option, + Option<(WgsCounters, Vec)>, + Option, + Option, + Option<(HsCounters, Vec, Vec, String)>, + Option, + ); + + let results: Vec> = pool.install(|| { + contigs + .par_iter() + .map(|(tid, name, len)| -> Result { + let mut reader = bam::IndexedReader::from_path(bam_path) + .with_context(|| format!("Failed to open alignment file: {bam_path}"))?; + if let Some(reference) = args.reference.as_deref() { + reader + .set_reference(reference) + .with_context(|| format!("Failed to set reference: {reference}"))?; + } + reader + .fetch(*tid) + .with_context(|| format!("Failed to fetch contig {name}"))?; + + let mut depth = DepthAccum::new(*len, mapq_cut, MOSDEPTH_DEFAULT_EXCLUDE); + let mut bam_stat = BamStatAccum::default(); + let mut preseq = preseq_enabled.then(|| PreseqAccum::new(seg_len)); + // Picard filters differently from mosdepth, so its coverage + // needs its own accumulator rather than a correction applied + // to a shared one. + let mut wgs = wgs_enabled.then(|| WgsAccum::new(*len, wgs_min_mapq, wgs_min_baseq)); + let mut insert_sizes = insert_size_enabled.then(InsertSizeAccum::new); + let mut qualimap = + qualimap_enabled.then(|| QualimapAccum::new(name, *len, qualimap_windows)); + + // GC bias and the targeted metrics both need per-contig + // context, fetched once here rather than per record. + let reference_bases: Option> = if gc_bias_enabled { + let reader = rust_htslib::faidx::Reader::from_path( + args.reference.as_deref().unwrap_or_default(), + ) + .with_context(|| "Failed to open the reference FASTA index")?; + let length = reader.fetch_seq_len(name) as usize; + Some( + reader + .fetch_seq(name, 0, length.saturating_sub(1)) + .map(|s| s.to_vec()) + .with_context(|| format!("Failed to read reference for {name}"))?, + ) + } else { + None + }; + let mut gc = reference_bases + .as_ref() + .map(|bases| GcBiasAccum::new(bases, gc_window)); + let mut hs = hs_enabled.then(|| { + HsAccum::new( + name, + *len, + baits.as_ref().unwrap_or_else(|| targets.as_ref().unwrap()), + targets.as_ref().unwrap(), + hs_min_mapq, + hs_min_baseq, + ) + }); + + let mut record = bam::Record::new(); + while let Some(result) = reader.read(&mut record) { + result.context("Failed to read record")?; + depth.process_read(&record); + bam_stat.process_read(&record, mapq_cut); + if let Some(accum) = preseq.as_mut() { + accum.process_read(&record); + } + if let Some(accum) = wgs.as_mut() { + accum.process_read(&record); + } + if let Some(accum) = insert_sizes.as_mut() { + accum.process_read(&record); + } + if let (Some(accum), Some(bases)) = (gc.as_mut(), reference_bases.as_ref()) { + accum.process_read(&record, bases); + } + if let Some(accum) = hs.as_mut() { + accum.process_read(&record); + } + if let Some(accum) = qualimap.as_mut() { + accum.process_read(&record); + } + } + + let depths = depth.into_depths(); + let contig = ContigDepth::from_depths(name, &depths, window_size, &thresholds); + Ok(( + contig, + bam_stat, + preseq, + wgs.map(|accum| accum.into_parts()), + insert_sizes, + gc, + hs.map(|accum| { + let (counters, depths, mask) = accum.into_parts(); + (counters, depths, mask, name.clone()) + }), + qualimap.map(|accum| accum.into_result()), + )) + }) + .collect() + }); + + let mut per_contig = Vec::new(); + let mut bam_stat_total = BamStatAccum::default(); + let mut preseq_total: Option = None; + let mut wgs_counters = WgsCounters::default(); + let mut wgs_depths: Vec = Vec::new(); + let mut saw_wgs = false; + let mut insert_size_total: Option = None; + let mut gc_total: Option = None; + let mut hs_counters = HsCounters::default(); + let mut hs_target_depths: Vec = Vec::new(); + let mut hs_target_count = 0u64; + let mut hs_zero_targets = 0u64; + let mut qualimap_contigs: Vec = Vec::new(); + for result in results { + let (contig, bam_stat, preseq, wgs, insert_sizes, gc, hs, qualimap) = result?; + per_contig.push(contig); + bam_stat_total.merge(bam_stat); + match (preseq_total.as_mut(), preseq) { + (Some(total), Some(part)) => total.merge(part), + (None, part) => preseq_total = part, + _ => {} + } + if let Some((counters, depths)) = wgs { + saw_wgs = true; + wgs_counters.merge(&counters); + // Depths concatenate rather than merge: each worker owns a + // distinct contig and the metrics span all of them. + wgs_depths.extend(depths); + } + match (insert_size_total.as_mut(), insert_sizes) { + (Some(total), Some(part)) => total.merge(part), + (None, part) => insert_size_total = part, + _ => {} + } + match (gc_total.as_mut(), gc) { + (Some(total), Some(part)) => total.merge(&part), + (None, part) => gc_total = part, + _ => {} + } + if let Some(part) = qualimap { + qualimap_contigs.push(part); + } + if let Some((counters, depths, mask, contig_name)) = hs { + hs_counters.merge(&counters); + for (depth, on_target) in depths.iter().zip(mask.iter()) { + if *on_target { + hs_target_depths.push(*depth); + } + } + if let Some(set) = targets.as_ref() { + for interval in set.on(&contig_name) { + hs_target_count += 1; + if (interval.start..interval.end) + .all(|p| depths.get(p as usize).copied().unwrap_or(0) == 0) + { + hs_zero_targets += 1; + } + } + } + } + } + + // Unmapped records carry no contig, so they need their own pass; flagstat + // and idxstats both report them. + { + let mut reader = bam::IndexedReader::from_path(bam_path) + .with_context(|| format!("Failed to open alignment file: {bam_path}"))?; + if let Some(reference) = args.reference.as_deref() { + reader.set_reference(reference).ok(); + } + if reader.fetch(bam::FetchDefinition::Unmapped).is_ok() { + // Unmapped records reach no contig worker, yet they still count + // towards several metrics: flagstat and idxstats report them, HS + // metrics count them in TOTAL_READS and PF_BASES, and GC bias + // counts them as clusters. Both accumulators short-circuit on an + // unmapped record, so an empty contig is enough context here. + let mut hs_unmapped = hs_enabled.then(|| { + HsAccum::new( + "", + 0, + baits.as_ref().unwrap_or_else(|| targets.as_ref().unwrap()), + targets.as_ref().unwrap(), + hs_min_mapq, + hs_min_baseq, + ) + }); + let mut gc_unmapped = gc_bias_enabled.then(|| GcBiasAccum::new(&[], gc_window)); + let mut qualimap_unmapped = + qualimap_enabled.then(|| QualimapAccum::new("", 0, qualimap_windows)); + + let mut record = bam::Record::new(); + while let Some(result) = reader.read(&mut record) { + result.context("Failed to read unmapped record")?; + bam_stat_total.process_read(&record, mapq_cut); + if let Some(accum) = hs_unmapped.as_mut() { + accum.process_read(&record); + } + if let Some(accum) = gc_unmapped.as_mut() { + accum.process_read(&record, &[]); + } + if let Some(accum) = qualimap_unmapped.as_mut() { + accum.process_read(&record); + } + } + + if let Some(accum) = hs_unmapped { + let (counters, _, _) = accum.into_parts(); + hs_counters.merge(&counters); + } + if let (Some(total), Some(part)) = (gc_total.as_mut(), gc_unmapped) { + total.merge(&part); + } + if let (Some(first), Some(part)) = (qualimap_contigs.first_mut(), qualimap_unmapped) { + // The unmapped pass only moves read counters, so folding it + // into the first contig keeps the totals right without + // inventing a contig for reads that have none. + first.counters.merge(&part.into_result().counters); + } + } + } + + // Workers ran longest-contig-first; outputs go out in header order. + let order: Vec = (0..header.target_count()) + .map(|tid| String::from_utf8_lossy(header.tid2name(tid)).to_string()) + .collect(); + per_contig.sort_by_key(|contig| { + order + .iter() + .position(|name| name == &contig.name) + .unwrap_or(usize::MAX) + }); + + let result = MosdepthResult { + contigs: per_contig, + window_size, + thresholds: thresholds.clone(), + }; + + let bam_stat_result = bam_stat_total.into_result(); + ensure!( + args.skip_dup_check || bam_stat_result.duplicates > 0, + "No duplicate-flagged reads found in {bam_path}. RustQC expects \ + duplicate-marked (not removed) input. Pass --skip-dup-check to override." + ); + + let dir = |name: &str| -> PathBuf { + if flat_output { + outdir.to_path_buf() + } else { + outdir.join(name) + } + }; + let mut written: Vec = Vec::new(); + let mut record_output = |tool: &str, path: PathBuf| { + ui.output_item(tool, &path.display().to_string()); + written.push(summary::OutputFile { + tool: tool.to_string(), + path: path.display().to_string(), + }); + }; + + if config.mosdepth.enabled { + let mos_dir = dir("mosdepth"); + std::fs::create_dir_all(&mos_dir)?; + // Built with format! rather than with_extension: a sample name that + // contains a dot (test.dna, say) would otherwise lose its last segment. + let prefix = |suffix: &str| mos_dir.join(format!("{sample_name}.{suffix}")); + + let path = prefix("mosdepth.summary.txt"); + mos_out::write_summary(&result, &path)?; + record_output("mosdepth", path); + + let path = prefix("mosdepth.global.dist.txt"); + mos_out::write_global_dist(&result, &path)?; + record_output("mosdepth", path); + + if !config.mosdepth.skip_per_base { + let path = prefix("per-base.bed.gz"); + mos_out::write_per_base(&result, &path)?; + record_output("mosdepth", path); + } + + if window_size.is_some() { + let path = prefix("mosdepth.region.dist.txt"); + mos_out::write_region_dist(&result, &path)?; + record_output("mosdepth", path); + + let path = prefix("regions.bed.gz"); + mos_out::write_regions(&result, &path)?; + record_output("mosdepth", path); + + if !thresholds.is_empty() { + let path = prefix("thresholds.bed.gz"); + mos_out::write_thresholds(&result, &path)?; + record_output("mosdepth", path); + } + } + } + + if config.samtools.enabled { + let sam_dir = dir("samtools"); + std::fs::create_dir_all(&sam_dir)?; + + let path = sam_dir.join(format!("{sample_name}.stats.txt")); + common::samtools::stats::write_stats(&bam_stat_result, &path)?; + record_output("samtools stats", path); + + let path = sam_dir.join(format!("{sample_name}.flagstat.txt")); + common::samtools::flagstat::write_flagstat(&bam_stat_result, &path)?; + record_output("samtools flagstat", path); + + let refs: Vec<(String, u64)> = (0..header.target_count()) + .map(|tid| { + ( + String::from_utf8_lossy(header.tid2name(tid)).to_string(), + header.target_len(tid).unwrap_or(0), + ) + }) + .collect(); + let path = sam_dir.join(format!("{sample_name}.idxstats.txt")); + common::samtools::idxstats::write_idxstats(&bam_stat_result, &refs, &path)?; + record_output("samtools idxstats", path); + } + + if saw_wgs { + let territory = match args.reference.as_deref() { + Some(reference) => genome_territory(reference)?, + // Unreachable: saw_wgs implies a reference was given. + None => wgs_depths.len() as u64, + }; + let result = WgsMetricsResult::new(&wgs_depths, wgs_counters, territory, coverage_cap); + let dir_path = dir("picard").join("wgs_metrics"); + std::fs::create_dir_all(&dir_path)?; + let path = dir_path.join(format!("{sample_name}.wgs_metrics.txt")); + wgs_metrics::write_wgs_metrics(&result, &path)?; + record_output("picard CollectWgsMetrics", path); + } + + if let Some(accum) = insert_size_total { + let result = accum.into_result(config.insert_size.deviations); + if result.rows.is_empty() { + ui.warn("no paired records with a usable insert size, skipping insert size metrics"); + } else { + let dir_path = dir("picard").join("insert_size"); + std::fs::create_dir_all(&dir_path)?; + let path = dir_path.join(format!("{sample_name}.insert_size_metrics.txt")); + insert_size::write_insert_size_metrics(&result, &path)?; + record_output("picard CollectInsertSizeMetrics", path); + } + } + + if let Some(accum) = gc_total { + let result = accum.into_result(gc_window); + let dir_path = dir("picard").join("gc_bias"); + std::fs::create_dir_all(&dir_path)?; + let detail = dir_path.join(format!("{sample_name}.gc_bias.detail_metrics.txt")); + gc_bias::write_detail_metrics(&result, &detail)?; + record_output("picard CollectGcBiasMetrics", detail); + let summary = dir_path.join(format!("{sample_name}.gc_bias.summary_metrics.txt")); + gc_bias::write_summary_metrics(&result, &summary)?; + record_output("picard CollectGcBiasMetrics", summary); + } + + if hs_enabled { + let target_set = targets.as_ref().expect("hs_enabled implies --targets"); + let bait_set = baits.as_ref().unwrap_or(target_set); + let library_size = hs_metrics::estimate_library_size( + hs_counters.selected_pairs, + hs_counters.selected_unique_pairs, + ); + let result = HsMetricsResult { + bait_set: bait_set.name().to_string(), + bait_territory: bait_set.territory(), + target_territory: target_set.territory(), + genome_size: contigs.iter().map(|(_, _, len)| len).sum(), + counters: hs_counters, + target_depths: hs_target_depths, + zero_coverage_targets: hs_zero_targets, + target_count: hs_target_count, + library_size, + }; + let dir_path = dir("picard").join("hs_metrics"); + std::fs::create_dir_all(&dir_path)?; + let path = dir_path.join(format!("{sample_name}.hs_metrics.txt")); + hs_metrics::write_hs_metrics(&result, &path)?; + record_output("picard CollectHsMetrics", path); + } + + if !qualimap_contigs.is_empty() { + // Restore header order, since the workers ran longest contig first. + qualimap_contigs.sort_by_key(|c| { + order + .iter() + .position(|name| *name == c.name) + .unwrap_or(usize::MAX) + }); + let dir_path = dir("qualimap"); + std::fs::create_dir_all(&dir_path)?; + let results = dir_path.join("genome_results.txt"); + qualimap_output::write_genome_results(&qualimap_contigs, bam_path, &results)?; + record_output("qualimap", results); + qualimap_output::write_raw_data( + &qualimap_contigs, + &dir_path.join("raw_data_qualimapReport"), + )?; + record_output("qualimap", dir_path.join("raw_data_qualimapReport")); + let report = dir_path.join("qualimapReport.html"); + qualimap_output::write_html_report(&qualimap_contigs, &sample_name, &report)?; + record_output("qualimap", report); + } + + if let Some(mut accum) = preseq_total { + let preseq_dir = dir("preseq"); + std::fs::create_dir_all(&preseq_dir)?; + accum.finalize(); + let total_reads = accum.total_fragments; + let n_distinct = accum.n_distinct(); + let histogram = accum.into_histogram(); + match common::preseq::estimate_complexity( + &histogram, + total_reads, + n_distinct, + &config.preseq, + ) { + Ok(preseq_result) => { + let path = preseq_dir.join(format!("{sample_name}.lc_extrap.txt")); + common::preseq::write_output( + &preseq_result, + &path, + config.preseq.confidence_level, + )?; + record_output("preseq", path); + } + Err(e) => ui.warn(&format!("preseq: {e:#}")), + } + } + + Ok(summary::InputSummary { + bam_file: bam_path.to_string(), + status: "success".to_string(), + error: None, + runtime_seconds: 0.0, + counting: None, + dupradar: None, + dna: Some(dna_summary(&result, &bam_stat_result, &thresholds)), + outputs: written, + }) +} + +/// Build the JSON summary block for a `dna` run. +fn dna_summary( + result: &rustqc::dna::mosdepth::MosdepthResult, + bam_stat: &rustqc::common::bam_stat::BamStatResult, + thresholds: &[u32], +) -> summary::DnaSummary { + let genome_length = result.total_length(); + let histogram = + rustqc::dna::mosdepth::merge_histograms(result.contigs.iter().map(|c| &c.histogram)); + + let coverage_thresholds = thresholds + .iter() + .map(|threshold| { + let at_or_above: u64 = histogram + .iter() + .filter(|(depth, _)| *depth >= threshold) + .map(|(_, count)| count) + .sum(); + summary::CoverageThreshold { + threshold: *threshold, + pct_bases: if genome_length == 0 { + 0.0 + } else { + at_or_above as f64 * 100.0 / genome_length as f64 + }, + } + }) + .collect(); + + // Median: walk the depth histogram until half the reference is behind us. + let mut seen = 0u64; + let mut median = 0u32; + for (depth, count) in &histogram { + seen += count; + if seen * 2 >= genome_length { + median = *depth; + break; + } + } + + let duplicate_pct = if bam_stat.total_records == 0 { + 0.0 + } else { + bam_stat.duplicates as f64 * 100.0 / bam_stat.total_records as f64 + }; + + summary::DnaSummary { + genome_length, + covered_bases: result.total_bases(), + mean_coverage: result.mean(), + median_coverage: median, + max_coverage: result.max(), + coverage_thresholds, + total_reads: bam_stat.total_records, + duplicates: bam_stat.duplicates, + duplicate_pct, + } +} + +/// Count the reference's non-N bases, which is Picard's `GENOME_TERRITORY`. +/// +/// The whole reference is read once. Picard does the same, and the figure +/// cannot be taken from the alignment header, which records contig lengths +/// including their N runs. +fn genome_territory(reference: &str) -> Result { + use std::io::BufRead; + + let reader = rustqc::io::open_reader(reference) + .with_context(|| format!("Failed to open reference FASTA: {reference}"))?; + let mut territory = 0u64; + for line in reader.lines() { + let line = line.with_context(|| format!("Failed to read reference FASTA: {reference}"))?; + if line.starts_with('>') { + continue; + } + territory += line.bytes().filter(|b| !matches!(b, b'N' | b'n')).count() as u64; + } + Ok(territory) +} + +/// How many contig depth arrays may be live at once. +/// +/// Each worker holds four bytes per base of its contig, so the largest contig +/// sets the per-worker cost: about 1 GB for GRCh38 chr1. There is no portable +/// way to ask the operating system how much memory is free, so the budget is a +/// fixed 4 GB unless the user overrides it with `--max-depth-workers`. +fn depth_worker_budget(threads: usize, override_value: Option, largest: u64) -> usize { + const BUDGET_BYTES: u64 = 4 * 1024 * 1024 * 1024; + if let Some(value) = override_value { + return value.max(1); } + let per_worker = largest.saturating_mul(4).max(1); + let affordable = (BUDGET_BYTES / per_worker).max(1) as usize; + threads.min(affordable).max(1) } /// Reconstruct the command line for the featureCounts-compatible header comment. @@ -618,6 +1821,7 @@ fn run_rna(args: cli::RnaArgs, ui: &Ui) -> Result<()> { runtime_seconds: 0.0, counting: None, dupradar: None, + dna: None, outputs: vec![], }); } @@ -902,6 +2106,7 @@ impl BamResult { runtime_seconds: self.duration.as_secs_f64(), counting, dupradar, + dna: None, outputs: self .outputs .iter() @@ -1562,7 +2767,7 @@ fn write_rseqc_outputs( if params.config.flagstat.enabled { std::fs::create_dir_all(&samtools_dir)?; let flagstat_path = samtools_dir.join(format!("{}.flagstat", sample_name)); - rna::rseqc::flagstat::write_flagstat(result, &flagstat_path)?; + common::samtools::flagstat::write_flagstat(result, &flagstat_path)?; let p = flagstat_path.display().to_string(); ui.output_item("flagstat", &p); written.push(("flagstat".into(), p)); @@ -1572,7 +2777,7 @@ fn write_rseqc_outputs( if params.config.idxstats.enabled { std::fs::create_dir_all(&samtools_dir)?; let idxstats_path = samtools_dir.join(format!("{}.idxstats", sample_name)); - rna::rseqc::idxstats::write_idxstats(result, bam_header_refs, &idxstats_path)?; + common::samtools::idxstats::write_idxstats(result, bam_header_refs, &idxstats_path)?; let p = idxstats_path.display().to_string(); ui.output_item("idxstats", &p); written.push(("idxstats".into(), p)); @@ -1582,7 +2787,7 @@ fn write_rseqc_outputs( if params.config.samtools_stats.enabled { std::fs::create_dir_all(&samtools_dir)?; let stats_path = samtools_dir.join(format!("{}.stats", sample_name)); - rna::rseqc::stats::write_stats(result, &stats_path)?; + common::samtools::stats::write_stats(result, &stats_path)?; let p = stats_path.display().to_string(); ui.output_item("stats", &p); written.push(("samtools stats".into(), p)); diff --git a/src/protein/coding/mod.rs b/src/protein/coding/mod.rs new file mode 100644 index 00000000..9f4f3609 --- /dev/null +++ b/src/protein/coding/mod.rs @@ -0,0 +1,12 @@ +//! Coding-region quality control from an alignment and an annotation. +//! +//! Answers where reads fall relative to protein-coding sequence, which is the +//! question `rna` does not: that pipeline asks about transcripts, this one +//! about the coding part of them. +//! +//! Reproduces Picard `CollectRnaSeqMetrics`'s base assignment. + +pub mod output; +pub mod regions; + +pub use regions::{Region, RegionSets}; diff --git a/src/protein/coding/output.rs b/src/protein/coding/output.rs new file mode 100644 index 00000000..693c5ff3 --- /dev/null +++ b/src/protein/coding/output.rs @@ -0,0 +1,203 @@ +//! Base assignment and the `CollectRnaSeqMetrics`-compatible writer. + +use std::io::Write; +use std::path::Path; + +use anyhow::{Context, Result}; +use rust_htslib::bam; +use rust_htslib::bam::record::Cigar; + +use super::regions::{Region, RegionSets}; +use crate::common::bam_flags::*; + +/// Counts of aligned bases by the region they fall in. +#[derive(Debug, Clone, Default)] +pub struct CodingCounts { + /// Bases in an annotated coding sequence. + pub coding: u64, + /// Bases exonic but not coding. + pub utr: u64, + /// Bases within a transcript but not exonic. + pub intronic: u64, + /// Bases outside every transcript. + pub intergenic: u64, + /// Bases in reads before any assignment, which is the denominator. + pub aligned: u64, + /// Bases across every read, aligned or not, which Picard calls `PF_BASES`. + pub total: u64, +} + +impl CodingCounts { + /// Offer one record. + /// + /// Unmapped, secondary and supplementary records contribute nothing, which + /// is the same set `CollectWgsMetrics` excludes and gives the same + /// `PF_ALIGNED_BASES`. + pub fn process_read(&mut self, record: &bam::Record, chrom: &str, regions: &RegionSets) { + let flags = record.flags(); + // PF_BASES counts primary records only, so a secondary alignment does + // not have its sequence counted twice. An unmapped primary record + // still contributes its bases, which is why the two conditions differ. + if flags & (BAM_FSECONDARY | BAM_FSUPPLEMENTARY) == 0 { + self.total += record.seq_len() as u64; + } + if flags & (BAM_FUNMAP | BAM_FSECONDARY | BAM_FSUPPLEMENTARY) != 0 { + return; + } + + let mut reference = record.pos(); + for op in record.cigar().iter() { + match op { + Cigar::Match(n) | Cigar::Equal(n) | Cigar::Diff(n) => { + for k in 0..i64::from(*n) { + let position = reference + k; + if position < 0 { + continue; + } + self.aligned += 1; + match regions.classify(chrom, position as u64) { + Region::Coding => self.coding += 1, + Region::Utr => self.utr += 1, + Region::Intronic => self.intronic += 1, + Region::Intergenic => self.intergenic += 1, + } + } + reference += i64::from(*n); + } + Cigar::Del(n) | Cigar::RefSkip(n) => reference += i64::from(*n), + _ => {} + } + } + } + + /// Fold another set of counts in. + pub fn merge(&mut self, other: &CodingCounts) { + self.coding += other.coding; + self.utr += other.utr; + self.intronic += other.intronic; + self.intergenic += other.intergenic; + self.aligned += other.aligned; + self.total += other.total; + } + + /// Fraction of aligned bases in a class. + fn fraction(&self, part: u64) -> f64 { + if self.aligned == 0 { + 0.0 + } else { + part as f64 / self.aligned as f64 + } + } + + /// Fraction of aligned bases that are exonic, coding or otherwise. + pub fn mrna_fraction(&self) -> f64 { + self.fraction(self.coding + self.utr) + } +} + +/// Format a float the way Picard's metrics writer does. +fn fmt_picard(value: f64) -> String { + if !value.is_finite() { + return "?".to_string(); + } + if value == value.trunc() && value.abs() < 1e15 { + return format!("{}", value as i64); + } + let text = format!("{value:.6}"); + text.trim_end_matches('0').trim_end_matches('.').to_string() +} + +/// Write the `CollectRnaSeqMetrics`-compatible subset. +/// +/// Only the columns this analysis computes are written. The strand-specificity +/// and coverage-bias columns need a library protocol and a per-transcript +/// coverage pass that this mode does not do, and emitting them as zero would +/// read as a measurement rather than an absence. +pub fn write_coding_metrics(counts: &CodingCounts, path: &Path) -> Result<()> { + let mut out = std::fs::File::create(path) + .map(std::io::BufWriter::new) + .with_context(|| format!("Failed to create coding metrics: {}", path.display()))?; + + writeln!(out, "## METRICS CLASS\tpicard.analysis.RnaSeqMetrics")?; + writeln!( + out, + "PF_BASES\tPF_ALIGNED_BASES\tCODING_BASES\tUTR_BASES\tINTRONIC_BASES\t\ + INTERGENIC_BASES\tPCT_CODING_BASES\tPCT_UTR_BASES\tPCT_INTRONIC_BASES\t\ + PCT_INTERGENIC_BASES\tPCT_MRNA_BASES" + )?; + writeln!( + out, + "{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}", + counts.total, + counts.aligned, + counts.coding, + counts.utr, + counts.intronic, + counts.intergenic, + fmt_picard(counts.fraction(counts.coding)), + fmt_picard(counts.fraction(counts.utr)), + fmt_picard(counts.fraction(counts.intronic)), + fmt_picard(counts.fraction(counts.intergenic)), + fmt_picard(counts.mrna_fraction()), + )?; + writeln!(out)?; + out.flush()?; + Ok(()) +} + +#[cfg(test)] +mod tests { + use super::*; + + #[test] + fn the_four_classes_account_for_every_aligned_base() { + let counts = CodingCounts { + coding: 10, + utr: 20, + intronic: 30, + intergenic: 40, + aligned: 100, + total: 120, + }; + assert_eq!( + counts.coding + counts.utr + counts.intronic + counts.intergenic, + counts.aligned + ); + assert!((counts.mrna_fraction() - 0.3).abs() < 1e-12); + } + + #[test] + fn a_secondary_alignment_does_not_have_its_bases_counted_twice() { + use rust_htslib::bam::record::{Cigar, CigarString, Record}; + + let mut make = |flags: u16| { + let mut r = Record::new(); + let cigar = CigarString(vec![Cigar::Match(4)]); + r.set(b"q", Some(&cigar), b"ACGT", &[30u8; 4]); + r.set_flags(flags); + r.set_tid(0); + r.set_pos(0); + r + }; + let regions = RegionSets::default(); + let mut counts = CodingCounts::default(); + counts.process_read(&make(0), "chr1", ®ions); + counts.process_read(&make(BAM_FSECONDARY), "chr1", ®ions); + assert_eq!(counts.total, 4, "only the primary record's bases count"); + assert_eq!(counts.aligned, 4); + } + + #[test] + fn an_empty_run_does_not_divide_by_zero() { + let counts = CodingCounts::default(); + assert_eq!(counts.mrna_fraction(), 0.0); + assert_eq!(counts.fraction(0), 0.0); + } + + #[test] + fn picard_formatting_drops_trailing_zeros() { + assert_eq!(fmt_picard(0.482318), "0.482318"); + assert_eq!(fmt_picard(0.0), "0"); + assert_eq!(fmt_picard(1.0), "1"); + } +} diff --git a/src/protein/coding/regions.rs b/src/protein/coding/regions.rs new file mode 100644 index 00000000..86863a15 --- /dev/null +++ b/src/protein/coding/regions.rs @@ -0,0 +1,253 @@ +//! Genomic region sets built from a gene annotation. +//! +//! # Upstream semantics +//! +//! Follows Picard `CollectRnaSeqMetrics`. Every aligned base of a qualifying +//! read is assigned to exactly one class, and the classes are checked in a +//! fixed order because annotations overlap: a base inside a coding sequence of +//! one transcript and an intron of another counts as coding. +//! +//! "UTR" means exonic but not coding, which is what Picard reports under that +//! name. A transcript with no annotated CDS therefore contributes only UTR, +//! never coding, and on a non-coding annotation `CODING_BASES` is legitimately +//! zero rather than missing. + +use std::collections::HashMap; + +use crate::gtf::Gene; + +/// What a base was assigned to. +#[derive(Debug, Clone, Copy, PartialEq, Eq)] +pub enum Region { + /// Inside an annotated coding sequence. + Coding, + /// Exonic but outside any coding sequence. + Utr, + /// Within a transcript's span but not exonic. + Intronic, + /// Outside every transcript. + Intergenic, +} + +/// Merged, sorted interval lists per contig. +#[derive(Debug, Default)] +pub struct RegionSets { + coding: HashMap>, + exonic: HashMap>, + transcribed: HashMap>, +} + +/// Sort and merge overlapping half-open intervals. +fn merge(mut intervals: Vec<(u64, u64)>) -> Vec<(u64, u64)> { + if intervals.is_empty() { + return intervals; + } + intervals.sort_unstable(); + let mut merged: Vec<(u64, u64)> = Vec::with_capacity(intervals.len()); + for (start, end) in intervals { + match merged.last_mut() { + Some(last) if start <= last.1 => last.1 = last.1.max(end), + _ => merged.push((start, end)), + } + } + merged +} + +/// Whether a sorted, merged interval list contains a position. +fn contains(intervals: &[(u64, u64)], position: u64) -> bool { + intervals + .binary_search_by(|(start, end)| { + if position < *start { + std::cmp::Ordering::Greater + } else if position >= *end { + std::cmp::Ordering::Less + } else { + std::cmp::Ordering::Equal + } + }) + .is_ok() +} + +impl RegionSets { + /// Build the region sets from parsed genes. + /// + /// The GTF parser keeps coordinates one-based and inclusive, as the file + /// has them, while alignment positions are zero-based. Everything is + /// converted here to zero-based half-open, once, so the classifier + /// compares like with like. Skipping the conversion shifts every boundary + /// by one base, which leaves interior bases right and quietly misassigns + /// the edges: on the project fixture exactly one base moved from UTR to + /// intergenic. + pub fn from_genes<'a, I>(genes: I) -> Self + where + I: IntoIterator, + { + let mut coding: HashMap> = HashMap::new(); + let mut exonic: HashMap> = HashMap::new(); + let mut transcribed: HashMap> = HashMap::new(); + + for gene in genes { + for transcript in &gene.transcripts { + let chrom = transcript.chrom.clone(); + transcribed + .entry(chrom.clone()) + .or_default() + .push((transcript.start.saturating_sub(1), transcript.end)); + for (start, end) in &transcript.exons { + exonic + .entry(chrom.clone()) + .or_default() + .push((start.saturating_sub(1), *end)); + } + // A transcript without a CDS contributes no coding bases; its + // exons are all UTR, which is what Picard reports. + if let (Some(cds_start), Some(cds_end)) = (transcript.cds_start, transcript.cds_end) + { + if cds_end > cds_start { + let cds_start = cds_start.saturating_sub(1); + // Only the exonic part of the CDS span is coding; the + // span itself can cross introns. + for (start, end) in &transcript.exons { + let overlap_start = start.saturating_sub(1).max(cds_start); + let overlap_end = (*end).min(cds_end); + if overlap_end > overlap_start { + coding + .entry(chrom.clone()) + .or_default() + .push((overlap_start, overlap_end)); + } + } + } + } + } + // A gene whose exons were parsed without transcripts still + // contributes; otherwise a GTF with no transcript rows would look + // entirely intergenic. + if gene.transcripts.is_empty() { + transcribed + .entry(gene.chrom.clone()) + .or_default() + .push((gene.start.saturating_sub(1), gene.end)); + for exon in &gene.exons { + exonic + .entry(exon.chrom.clone()) + .or_default() + .push((exon.start.saturating_sub(1), exon.end)); + } + } + } + + Self { + coding: coding.into_iter().map(|(k, v)| (k, merge(v))).collect(), + exonic: exonic.into_iter().map(|(k, v)| (k, merge(v))).collect(), + transcribed: transcribed + .into_iter() + .map(|(k, v)| (k, merge(v))) + .collect(), + } + } + + /// Classify one zero-based position. + /// + /// The order is fixed and matters: annotations overlap, and a base that is + /// coding in one transcript and intronic in another is coding. + pub fn classify(&self, chrom: &str, position: u64) -> Region { + if self + .coding + .get(chrom) + .is_some_and(|set| contains(set, position)) + { + return Region::Coding; + } + if self + .exonic + .get(chrom) + .is_some_and(|set| contains(set, position)) + { + return Region::Utr; + } + if self + .transcribed + .get(chrom) + .is_some_and(|set| contains(set, position)) + { + return Region::Intronic; + } + Region::Intergenic + } + + /// Total bases of each class in the annotation itself, which is the + /// territory the metrics are reported against. + pub fn territories(&self) -> (u64, u64, u64) { + let span = |sets: &HashMap>| -> u64 { + sets.values() + .flat_map(|v| v.iter()) + .map(|(start, end)| end - start) + .sum() + }; + let coding = span(&self.coding); + let exonic = span(&self.exonic); + let transcribed = span(&self.transcribed); + ( + coding, + exonic.saturating_sub(coding), + transcribed.saturating_sub(exonic), + ) + } +} + +#[cfg(test)] +mod tests { + use super::*; + + #[test] + fn intervals_merge_when_they_touch_or_overlap() { + assert_eq!(merge(vec![(0, 10), (5, 20)]), vec![(0, 20)]); + assert_eq!(merge(vec![(0, 10), (10, 20)]), vec![(0, 20)], "touching"); + assert_eq!(merge(vec![(0, 10), (11, 20)]), vec![(0, 10), (11, 20)]); + assert_eq!( + merge(vec![(5, 20), (0, 10)]), + vec![(0, 20)], + "unsorted input" + ); + } + + #[test] + fn containment_is_half_open() { + let set = vec![(10, 20)]; + assert!(!contains(&set, 9)); + assert!(contains(&set, 10), "the start is inside"); + assert!(contains(&set, 19)); + assert!(!contains(&set, 20), "the end is not"); + } + + #[test] + fn classification_prefers_coding_then_utr_then_intronic() { + let mut sets = RegionSets::default(); + sets.coding.insert("chr1".into(), vec![(100, 200)]); + sets.exonic.insert("chr1".into(), vec![(50, 300)]); + sets.transcribed.insert("chr1".into(), vec![(0, 1000)]); + + assert_eq!(sets.classify("chr1", 150), Region::Coding); + assert_eq!(sets.classify("chr1", 60), Region::Utr, "exonic, not coding"); + assert_eq!(sets.classify("chr1", 500), Region::Intronic); + assert_eq!(sets.classify("chr1", 2000), Region::Intergenic); + assert_eq!( + sets.classify("chr2", 150), + Region::Intergenic, + "an unannotated contig is entirely intergenic" + ); + } + + #[test] + fn territories_subtract_the_nested_classes() { + let mut sets = RegionSets::default(); + sets.coding.insert("chr1".into(), vec![(100, 200)]); + sets.exonic.insert("chr1".into(), vec![(50, 300)]); + sets.transcribed.insert("chr1".into(), vec![(0, 1000)]); + let (coding, utr, intronic) = sets.territories(); + assert_eq!(coding, 100); + assert_eq!(utr, 150, "exonic 250 less the 100 coding"); + assert_eq!(intronic, 750, "transcribed 1000 less the 250 exonic"); + } +} diff --git a/src/protein/mod.rs b/src/protein/mod.rs new file mode 100644 index 00000000..49a445a0 --- /dev/null +++ b/src/protein/mod.rs @@ -0,0 +1,19 @@ +//! Protein quality control modules. +//! +//! Three modes that share an entry point and nothing else, because the +//! questions they answer take different inputs entirely: +//! +//! - [`sequence`] reads a protein FASTA and asks whether the proteome is +//! well-formed; +//! - `coding` reads an alignment and an annotation and asks where reads fall +//! relative to coding sequence; +//! - `spectra` reads mzML and asks whether a mass spectrometry run is healthy. + +pub mod coding; +pub mod sequence; + +/// Mass spectrometry analysis, available when built with the `proteomics` +/// feature. Without it the `spectra` mode is not offered at all, rather than +/// being offered and then failing. +#[cfg(feature = "proteomics")] +pub mod spectra; diff --git a/src/protein/sequence/defects.rs b/src/protein/sequence/defects.rs new file mode 100644 index 00000000..2b44e80e --- /dev/null +++ b/src/protein/sequence/defects.rs @@ -0,0 +1,202 @@ +//! Defects a protein FASTA can carry that seqkit does not report. +//! +//! These are the things that make a proteome unusable downstream: a residue +//! that is not an amino acid, a stop codon in the middle of a sequence, a +//! sequence that is a byte-for-byte duplicate of another, or an identifier +//! used twice for different sequences. + +use std::collections::HashMap; + +use super::Record; + +/// The twenty standard amino acids, plus the two that appear in translated +/// proteomes: `U` for selenocysteine and `O` for pyrrolysine. +pub const STANDARD_RESIDUES: &[u8] = b"ACDEFGHIKLMNPQRSTVWYUO"; + +/// Ambiguity codes that are valid in a protein FASTA but carry no identity: +/// `B` is D or N, `Z` is E or Q, `J` is I or L, and `X` is anything. +pub const AMBIGUOUS_RESIDUES: &[u8] = b"BZJX"; + +/// What is wrong with a proteome, if anything. +#[derive(Debug, Clone, Default)] +pub struct Defects { + /// Sequences holding a `*` anywhere but the final position. + pub internal_stops: Vec, + /// Sequences holding a residue that is neither standard, ambiguous, a gap + /// nor a terminal stop, with the offending residue. + pub non_standard: Vec<(String, u8)>, + /// Sequences whose residues are identical to an earlier one, paired with + /// the identifier they duplicate. + pub duplicates: Vec<(String, String)>, + /// Identifiers used more than once. + pub duplicate_ids: Vec, + /// Sequences with no residues at all. + pub empty: Vec, + /// Sequences not ending in `*`, reported only when asked for. + pub missing_terminal_stop: Vec, + /// Residues that are ambiguity codes. + pub ambiguous_residues: u64, +} + +impl Defects { + /// Whether anything at all was found. + pub fn is_clean(&self) -> bool { + self.internal_stops.is_empty() + && self.non_standard.is_empty() + && self.duplicates.is_empty() + && self.duplicate_ids.is_empty() + && self.empty.is_empty() + && self.missing_terminal_stop.is_empty() + } + + /// Total number of defective sequences, counting a sequence once per + /// category it falls into. + pub fn count(&self) -> usize { + self.internal_stops.len() + + self.non_standard.len() + + self.duplicates.len() + + self.duplicate_ids.len() + + self.empty.len() + + self.missing_terminal_stop.len() + } +} + +/// Inspect every record. +/// +/// `expect_stop` turns a missing terminal `*` into a reported defect. It is off +/// by default because most reference proteomes do not carry one. +pub fn inspect(records: &[Record], expect_stop: bool) -> Defects { + let mut defects = Defects::default(); + let mut seen_sequences: HashMap<&[u8], &str> = HashMap::new(); + let mut seen_ids: HashMap<&str, usize> = HashMap::new(); + + for record in records { + *seen_ids.entry(record.id.as_str()).or_insert(0) += 1; + + if record.is_empty() { + defects.empty.push(record.id.clone()); + continue; + } + + // A trailing stop is expected; anywhere else it truncates the protein. + let body = record + .residues + .strip_suffix(b"*") + .unwrap_or(&record.residues); + if body.contains(&b'*') { + defects.internal_stops.push(record.id.clone()); + } + if expect_stop && !record.residues.ends_with(b"*") { + defects.missing_terminal_stop.push(record.id.clone()); + } + + for residue in body { + if AMBIGUOUS_RESIDUES.contains(residue) { + defects.ambiguous_residues += 1; + } else if !STANDARD_RESIDUES.contains(residue) + && *residue != b'-' + && *residue != b'.' + && *residue != b'*' + { + defects.non_standard.push((record.id.clone(), *residue)); + break; + } + } + + match seen_sequences.get(record.residues.as_slice()) { + Some(first) => defects + .duplicates + .push((record.id.clone(), (*first).to_string())), + None => { + seen_sequences.insert(record.residues.as_slice(), record.id.as_str()); + } + } + } + + for (id, count) in seen_ids { + if count > 1 { + defects.duplicate_ids.push(id.to_string()); + } + } + defects.duplicate_ids.sort(); + + defects +} + +#[cfg(test)] +mod tests { + use super::*; + + fn record(id: &str, residues: &str) -> Record { + Record { + id: id.to_string(), + residues: residues.as_bytes().to_vec(), + } + } + + #[test] + fn a_terminal_stop_is_fine_and_an_internal_one_is_not() { + let defects = inspect(&[record("ok", "MKTAYI*"), record("bad", "MKT*AYI")], false); + assert_eq!(defects.internal_stops, vec!["bad".to_string()]); + } + + #[test] + fn ambiguity_codes_are_counted_rather_than_flagged() { + let defects = inspect(&[record("a", "MKTXBZJ")], false); + assert!(defects.non_standard.is_empty(), "X, B, Z and J are valid"); + assert_eq!(defects.ambiguous_residues, 4); + } + + #[test] + fn a_residue_that_is_not_an_amino_acid_is_flagged_once() { + let defects = inspect(&[record("a", "MK1T2AYI")], false); + assert_eq!(defects.non_standard.len(), 1, "one report per sequence"); + assert_eq!(defects.non_standard[0].1, b'1', "the first offender"); + } + + #[test] + fn selenocysteine_and_pyrrolysine_are_standard_enough() { + let defects = inspect(&[record("a", "MKUTAYIO")], false); + assert!(defects.non_standard.is_empty(), "U and O are real residues"); + } + + #[test] + fn identical_sequences_are_paired_with_the_first_that_carried_them() { + let defects = inspect( + &[ + record("first", "MKTAYI"), + record("second", "MSEQ"), + record("third", "MKTAYI"), + ], + false, + ); + assert_eq!( + defects.duplicates, + vec![("third".to_string(), "first".to_string())] + ); + } + + #[test] + fn a_repeated_identifier_is_reported_even_with_different_sequences() { + let defects = inspect(&[record("a", "MKT"), record("a", "MSEQ")], false); + assert_eq!(defects.duplicate_ids, vec!["a".to_string()]); + assert!(defects.duplicates.is_empty(), "the sequences differ"); + } + + #[test] + fn a_missing_terminal_stop_is_reported_only_when_asked_for() { + let records = [record("a", "MKTAYI")]; + assert!(inspect(&records, false).missing_terminal_stop.is_empty()); + assert_eq!( + inspect(&records, true).missing_terminal_stop, + vec!["a".to_string()] + ); + } + + #[test] + fn a_clean_proteome_reports_clean() { + let defects = inspect(&[record("a", "MKTAYI"), record("b", "MSEQ")], false); + assert!(defects.is_clean()); + assert_eq!(defects.count(), 0); + } +} diff --git a/src/protein/sequence/mod.rs b/src/protein/sequence/mod.rs new file mode 100644 index 00000000..88ebdccf --- /dev/null +++ b/src/protein/sequence/mod.rs @@ -0,0 +1,125 @@ +//! Protein FASTA quality control. +//! +//! Reproduces `seqkit stats -a -T` and adds what seqkit does not report: +//! amino acid composition, internal stop codons, non-standard residues and +//! duplicate sequences. + +pub mod defects; +pub mod output; +pub mod stats; + +use std::io::BufRead; +use std::path::Path; + +use anyhow::{Context, Result}; + +/// One sequence read from a FASTA file. +#[derive(Debug, Clone)] +pub struct Record { + /// Everything after `>` on the header line, up to the first whitespace. + pub id: String, + /// The residues, uppercased, with whitespace removed. + pub residues: Vec, +} + +impl Record { + /// Number of residues. + pub fn len(&self) -> usize { + self.residues.len() + } + + /// Whether the record holds no residues. + pub fn is_empty(&self) -> bool { + self.residues.is_empty() + } +} + +/// Read every record from a FASTA file, transparently handling gzip. +/// +/// Residues are uppercased on the way in, so downstream code never has to +/// think about case. Gap characters are kept, because seqkit counts them. +pub fn read_fasta(path: &Path) -> Result> { + let reader = crate::io::open_reader(path) + .with_context(|| format!("Failed to open FASTA: {}", path.display()))?; + + let mut records: Vec = Vec::new(); + let mut current: Option = None; + + for line in reader.lines() { + let line = line.with_context(|| format!("Failed to read FASTA: {}", path.display()))?; + let line = line.trim_end(); + if line.is_empty() { + continue; + } + if let Some(header) = line.strip_prefix('>') { + if let Some(record) = current.take() { + records.push(record); + } + let id = header.split_whitespace().next().unwrap_or("").to_string(); + current = Some(Record { + id, + residues: Vec::new(), + }); + } else { + match current.as_mut() { + Some(record) => record.residues.extend( + line.bytes() + .filter(|b| !b.is_ascii_whitespace()) + .map(|b| b.to_ascii_uppercase()), + ), + None => anyhow::bail!("{}: sequence data before any header line", path.display()), + } + } + } + if let Some(record) = current { + records.push(record); + } + Ok(records) +} + +#[cfg(test)] +mod tests { + use super::*; + + fn write(name: &str, contents: &str) -> std::path::PathBuf { + let dir = std::env::temp_dir().join("rustqc-protein-tests"); + std::fs::create_dir_all(&dir).unwrap(); + let path = dir.join(name); + std::fs::write(&path, contents).unwrap(); + path + } + + #[test] + fn records_are_split_on_header_lines() { + let path = write("two.fa", ">a desc here\nMKT\nAYI\n>b\nMSEQ\n"); + let records = read_fasta(&path).unwrap(); + assert_eq!(records.len(), 2); + assert_eq!(records[0].id, "a", "the id stops at the first space"); + assert_eq!(records[0].residues, b"MKTAYI", "wrapped lines are joined"); + assert_eq!(records[1].residues, b"MSEQ"); + } + + #[test] + fn residues_are_uppercased_and_stripped_of_whitespace() { + let path = write("case.fa", ">a\nmk t\tayi\n"); + let records = read_fasta(&path).unwrap(); + assert_eq!(records[0].residues, b"MKTAYI"); + } + + #[test] + fn blank_lines_are_ignored() { + let path = write("blank.fa", ">a\nMKT\n\n\nAYI\n\n"); + let records = read_fasta(&path).unwrap(); + assert_eq!(records.len(), 1); + assert_eq!(records[0].residues, b"MKTAYI"); + } + + #[test] + fn sequence_data_before_a_header_is_an_error() { + let path = write("headless.fa", "MKTAYI\n>a\nMSEQ\n"); + assert!( + read_fasta(&path).is_err(), + "data before a header must be rejected rather than silently dropped" + ); + } +} diff --git a/src/protein/sequence/output.rs b/src/protein/sequence/output.rs new file mode 100644 index 00000000..4d56334b --- /dev/null +++ b/src/protein/sequence/output.rs @@ -0,0 +1,197 @@ +//! Writers for the protein sequence QC outputs. +//! +//! Two files. The first reproduces `seqkit stats -a -T` so that anything +//! already parsing seqkit output keeps working. The second carries what seqkit +//! does not report: composition and defects. + +use std::io::Write; +use std::path::Path; + +use anyhow::{Context, Result}; + +use super::defects::Defects; +use super::stats::SequenceStats; + +/// Residues reported in the composition table, in the conventional order. +const REPORTED_RESIDUES: &[u8] = b"ACDEFGHIKLMNPQRSTVWY"; + +/// Write the `seqkit stats -a -T` compatible table. +/// +/// The four sequencing-only columns, `Q20(%)`, `Q30(%)`, `AvgQual` and +/// `sum_n`, are written as seqkit writes them for a protein FASTA: zero. +/// `GC(%)` likewise, since GC content is meaningless for residues. +pub fn write_seqkit_stats(entries: &[(String, SequenceStats)], path: &Path) -> Result<()> { + let mut out = std::fs::File::create(path) + .map(std::io::BufWriter::new) + .with_context(|| format!("Failed to create sequence stats: {}", path.display()))?; + + writeln!( + out, + "file\tformat\ttype\tnum_seqs\tsum_len\tmin_len\tavg_len\tmax_len\tQ1\tQ2\tQ3\t\ + sum_gap\tN50\tN50_num\tQ20(%)\tQ30(%)\tAvgQual\tGC(%)\tsum_n" + )?; + for (file, stats) in entries { + writeln!( + out, + "{}\tFASTA\tProtein\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t0\t0\t0.00\t0.00\t0", + file, + stats.count, + stats.total, + stats.min, + format_mean(stats.mean), + stats.max, + stats.q1, + stats.q2, + stats.q3, + stats.gaps, + stats.n50, + stats.n50_num, + )?; + } + out.flush()?; + Ok(()) +} + +/// Mean length as seqkit prints it: one decimal, and a trailing `.0` kept. +fn format_mean(value: f64) -> String { + format!("{value:.1}") +} + +/// Write the composition and defect report. +pub fn write_report( + file: &str, + stats: &SequenceStats, + defects: &Defects, + path: &Path, +) -> Result<()> { + let mut out = std::fs::File::create(path) + .map(std::io::BufWriter::new) + .with_context(|| format!("Failed to create sequence report: {}", path.display()))?; + + writeln!(out, "# RustQC protein sequence report")?; + writeln!(out, "# file\t{file}")?; + writeln!(out)?; + + writeln!(out, "## Composition")?; + writeln!(out, "residue\tcount\tfraction")?; + for residue in REPORTED_RESIDUES { + let count = stats.composition.get(residue).copied().unwrap_or(0); + writeln!( + out, + "{}\t{}\t{:.6}", + *residue as char, + count, + stats.fraction(*residue) + )?; + } + let other: u64 = stats + .composition + .iter() + .filter(|(residue, _)| !REPORTED_RESIDUES.contains(residue)) + .map(|(_, count)| count) + .sum(); + writeln!(out, "other\t{other}\t{:.6}", { + if stats.total == 0 { + 0.0 + } else { + other as f64 / stats.total as f64 + } + })?; + writeln!(out)?; + + writeln!(out, "## Defects")?; + writeln!(out, "category\tcount")?; + writeln!(out, "internal_stop\t{}", defects.internal_stops.len())?; + writeln!(out, "non_standard_residue\t{}", defects.non_standard.len())?; + writeln!(out, "duplicate_sequence\t{}", defects.duplicates.len())?; + writeln!(out, "duplicate_id\t{}", defects.duplicate_ids.len())?; + writeln!(out, "empty_sequence\t{}", defects.empty.len())?; + writeln!( + out, + "missing_terminal_stop\t{}", + defects.missing_terminal_stop.len() + )?; + writeln!(out, "ambiguous_residues\t{}", defects.ambiguous_residues)?; + writeln!(out)?; + + if !defects.is_clean() { + writeln!(out, "## Offending sequences")?; + writeln!(out, "category\tid\tdetail")?; + for id in &defects.internal_stops { + writeln!(out, "internal_stop\t{id}\t")?; + } + for (id, residue) in &defects.non_standard { + writeln!(out, "non_standard_residue\t{id}\t{}", *residue as char)?; + } + for (id, first) in &defects.duplicates { + writeln!(out, "duplicate_sequence\t{id}\t{first}")?; + } + for id in &defects.duplicate_ids { + writeln!(out, "duplicate_id\t{id}\t")?; + } + for id in &defects.empty { + writeln!(out, "empty_sequence\t{id}\t")?; + } + for id in &defects.missing_terminal_stop { + writeln!(out, "missing_terminal_stop\t{id}\t")?; + } + writeln!(out)?; + } + + out.flush()?; + Ok(()) +} + +#[cfg(test)] +mod tests { + use super::*; + use crate::protein::sequence::Record; + + fn scratch(name: &str) -> std::path::PathBuf { + let dir = std::env::temp_dir().join("rustqc-protein-output-tests"); + std::fs::create_dir_all(&dir).unwrap(); + dir.join(name) + } + + #[test] + fn the_mean_keeps_one_decimal_even_when_whole() { + assert_eq!(format_mean(328.4), "328.4"); + assert_eq!(format_mean(100.0), "100.0", "seqkit writes 100.0, not 100"); + } + + #[test] + fn the_stats_table_carries_seqkits_zero_columns() { + let records = vec![Record { + id: "a".into(), + residues: b"MKTAYI".to_vec(), + }]; + let stats = SequenceStats::from_records(&records); + let path = scratch("stats.tsv"); + write_seqkit_stats(&[("f.fa".to_string(), stats)], &path).unwrap(); + let text = std::fs::read_to_string(&path).unwrap(); + let row = text.lines().nth(1).unwrap(); + let fields: Vec<&str> = row.split('\t').collect(); + assert_eq!(fields[1], "FASTA"); + assert_eq!(fields[2], "Protein"); + assert_eq!(&fields[14..], &["0", "0", "0.00", "0.00", "0"]); + } + + #[test] + fn a_clean_report_omits_the_offenders_section() { + let records = vec![Record { + id: "a".into(), + residues: b"MKTAYI".to_vec(), + }]; + let stats = SequenceStats::from_records(&records); + let defects = crate::protein::sequence::defects::inspect(&records, false); + let path = scratch("clean.txt"); + write_report("f.fa", &stats, &defects, &path).unwrap(); + let text = std::fs::read_to_string(&path).unwrap(); + assert!(text.contains("## Composition")); + assert!(text.contains("## Defects")); + assert!( + !text.contains("## Offending sequences"), + "nothing to list, so the section is left out" + ); + } +} diff --git a/src/protein/sequence/stats.rs b/src/protein/sequence/stats.rs new file mode 100644 index 00000000..63f4061b --- /dev/null +++ b/src/protein/sequence/stats.rs @@ -0,0 +1,267 @@ +//! Length and composition statistics, reproducing `seqkit stats -a -T`. +//! +//! # Upstream semantics +//! +//! Derived by reproducing seqkit 2.13.0's output on the project fixtures. +//! Two of its conventions are worth stating, because neither is what a +//! statistics library gives you by default: +//! +//! **Quartiles are Tukey's halves.** `Q1` is the median of the lower half of +//! the sorted lengths, `Q3` the median of the upper half, with the overall +//! median excluded when the count is odd. On a ten-sequence file that gives +//! `Q1 = 157` where linear interpolation gives 165 and inverse-ECDF gives 157 +//! but then disagrees on `Q2`. +//! +//! **Halves are rounded half-to-even.** A median of 235.5 is reported as 236, +//! but 376.5 and 516.5 are reported as 376 and 516. Ordinary rounding gets the +//! first right and the other two wrong. +//! +//! `N50` is the largest length `L` such that sequences of at least `L` cover +//! half the total residues; `N50_num` is how many sequences that takes. + +use std::collections::BTreeMap; + +use super::Record; + +/// The statistics `seqkit stats -a` reports, plus composition. +#[derive(Debug, Clone)] +pub struct SequenceStats { + /// Number of sequences. + pub count: u64, + /// Total residues. + pub total: u64, + /// Shortest sequence. + pub min: u64, + /// Longest sequence. + pub max: u64, + /// Mean length. + pub mean: f64, + /// Lower quartile, Tukey's lower half median. + pub q1: u64, + /// Median. + pub q2: u64, + /// Upper quartile, Tukey's upper half median. + pub q3: u64, + /// Gap characters, `-` and `.`, across all sequences. + pub gaps: u64, + /// N50 length. + pub n50: u64, + /// Number of distinct lengths needed to reach N50, which is what seqkit + /// reports and is not the number of sequences. + pub n50_num: u64, + /// Residue counts, keyed by the residue character. + pub composition: BTreeMap, +} + +impl SequenceStats { + /// Summarise a set of records. + pub fn from_records(records: &[Record]) -> Self { + let mut lengths: Vec = records.iter().map(|r| r.len() as u64).collect(); + lengths.sort_unstable(); + + let count = lengths.len() as u64; + let total: u64 = lengths.iter().sum(); + let min = lengths.first().copied().unwrap_or(0); + let max = lengths.last().copied().unwrap_or(0); + let mean = if count == 0 { + 0.0 + } else { + total as f64 / count as f64 + }; + + let (q1, q2, q3) = tukey_quartiles(&lengths); + let (n50, n50_num) = n50(&lengths, total); + + let mut composition: BTreeMap = BTreeMap::new(); + let mut gaps = 0u64; + for record in records { + for residue in &record.residues { + if *residue == b'-' || *residue == b'.' { + gaps += 1; + } + *composition.entry(*residue).or_insert(0) += 1; + } + } + + Self { + count, + total, + min, + max, + mean, + q1, + q2, + q3, + gaps, + n50, + n50_num, + composition, + } + } + + /// Fraction of residues that are the given one. + pub fn fraction(&self, residue: u8) -> f64 { + if self.total == 0 { + 0.0 + } else { + self.composition.get(&residue).copied().unwrap_or(0) as f64 / self.total as f64 + } + } +} + +/// Median of an ascending slice, rounded half to even when the count is even. +fn median(sorted: &[u64]) -> u64 { + match sorted.len() { + 0 => 0, + n if n % 2 == 1 => sorted[n / 2], + n => round_half_to_even((sorted[n / 2 - 1] + sorted[n / 2]) as f64 / 2.0), + } +} + +/// Round to the nearest integer, ties going to the even one. +/// +/// seqkit reports 235.5 as 236 and 376.5 as 376, which only half-to-even +/// explains. +fn round_half_to_even(value: f64) -> u64 { + let floor = value.floor(); + let fraction = value - floor; + let rounded = if (fraction - 0.5).abs() < f64::EPSILON { + if (floor as i64) % 2 == 0 { + floor + } else { + floor + 1.0 + } + } else { + value.round() + }; + rounded.max(0.0) as u64 +} + +/// Tukey's quartiles: the medians of the two halves, the overall median +/// excluded from both when the count is odd. +fn tukey_quartiles(sorted: &[u64]) -> (u64, u64, u64) { + let n = sorted.len(); + if n == 0 { + return (0, 0, 0); + } + let half = n / 2; + let lower = &sorted[..half]; + let upper = &sorted[n - half..]; + (median(lower), median(sorted), median(upper)) +} + +/// N50 and the number of *distinct lengths* needed to reach it. +/// +/// The second figure is not the number of sequences, which is the obvious +/// reading and the wrong one. seqkit walks the distinct lengths from longest +/// down and counts how many it consumed: three sequences of lengths 10, 10 and +/// 3 give `N50_num` of 1, not 2, because the two tens are one length. A file +/// whose lengths are all distinct hides the difference entirely, which is why +/// the project's protein fixtures did not catch it. +fn n50(sorted_ascending: &[u64], total: u64) -> (u64, u64) { + let mut cumulative = 0u64; + let mut distinct = 0u64; + let mut previous: Option = None; + for length in sorted_ascending.iter().rev() { + if previous != Some(*length) { + distinct += 1; + previous = Some(*length); + } + cumulative += length; + if cumulative * 2 >= total { + return (*length, distinct); + } + } + (0, 0) +} + +#[cfg(test)] +mod tests { + use super::*; + + fn records(lengths: &[usize]) -> Vec { + lengths + .iter() + .enumerate() + .map(|(i, n)| Record { + id: format!("s{i}"), + residues: vec![b'A'; *n], + }) + .collect() + } + + #[test] + fn quartiles_are_tukeys_halves_not_interpolated() { + // The project's yeast fixture: linear interpolation gives 165 for Q1. + let stats = SequenceStats::from_records(&records(&[ + 81, 140, 157, 189, 198, 273, 381, 526, 584, 755, + ])); + assert_eq!(stats.q1, 157); + assert_eq!(stats.q2, 236); + assert_eq!(stats.q3, 526); + } + + #[test] + fn halves_round_to_even() { + assert_eq!(round_half_to_even(235.5), 236, "236 is even"); + assert_eq!(round_half_to_even(376.5), 376, "376 is even"); + assert_eq!(round_half_to_even(516.5), 516, "516 is even"); + assert_eq!(round_half_to_even(2.4), 2, "not a tie, ordinary rounding"); + assert_eq!(round_half_to_even(2.6), 3); + } + + #[test] + fn an_odd_count_excludes_the_median_from_both_halves() { + // Five values: halves are [1, 2] and [4, 5], so Q1 is 2 and Q3 is 4 + // after half-to-even rounding of 1.5 and 4.5. + let stats = SequenceStats::from_records(&records(&[1, 2, 3, 4, 5])); + assert_eq!(stats.q2, 3, "the median itself"); + assert_eq!(stats.q1, 2, "median of [1, 2] is 1.5, rounded to 2"); + assert_eq!(stats.q3, 4, "median of [4, 5] is 4.5, rounded to 4"); + } + + #[test] + fn n50_num_counts_distinct_lengths_not_sequences() { + // Two sequences of 10 and one of 3: two sequences are needed to cover + // half of 23, but they share a length, so seqkit reports 1. Every + // length in the project fixtures is distinct, so only a case like this + // separates the two definitions. + let stats = SequenceStats::from_records(&records(&[3, 10, 10])); + assert_eq!(stats.n50, 10); + assert_eq!(stats.n50_num, 1, "not 2"); + } + + #[test] + fn n50_is_the_length_covering_half_the_residues() { + // 755 + 584 + 526 = 1865, more than half of 3284. + let stats = SequenceStats::from_records(&records(&[ + 81, 140, 157, 189, 198, 273, 381, 526, 584, 755, + ])); + assert_eq!(stats.n50, 526); + assert_eq!(stats.n50_num, 3); + } + + #[test] + fn gaps_are_counted_but_still_appear_in_the_composition() { + let records = vec![Record { + id: "a".into(), + residues: b"MK-T.A".to_vec(), + }]; + let stats = SequenceStats::from_records(&records); + assert_eq!(stats.gaps, 2); + assert_eq!( + stats.total, 6, + "gaps count towards the length, as seqkit does" + ); + assert_eq!(stats.composition.get(&b'M'), Some(&1)); + } + + #[test] + fn an_empty_input_does_not_divide_by_zero() { + let stats = SequenceStats::from_records(&[]); + assert_eq!(stats.count, 0); + assert_eq!(stats.mean, 0.0); + assert_eq!(stats.n50, 0); + assert_eq!(stats.fraction(b'A'), 0.0); + } +} diff --git a/src/protein/spectra/metrics.rs b/src/protein/spectra/metrics.rs new file mode 100644 index 00000000..dd8d057a --- /dev/null +++ b/src/protein/spectra/metrics.rs @@ -0,0 +1,218 @@ +//! Per-run and per-level spectrum metrics. + +use std::collections::BTreeMap; + +/// Metrics for one MS level. +#[derive(Debug, Clone, Default)] +pub struct LevelMetrics { + /// Spectra acquired at this level. + pub spectra: u64, + /// Peaks across those spectra. + pub peaks: u64, + /// Summed total ion current. + pub total_ion_current: f64, + /// Fewest peaks in any one spectrum. + pub min_peaks: u64, + /// Most peaks in any one spectrum. + pub max_peaks: u64, +} + +impl LevelMetrics { + /// Mean peaks per spectrum. + pub fn mean_peaks(&self) -> f64 { + if self.spectra == 0 { + 0.0 + } else { + self.peaks as f64 / self.spectra as f64 + } + } +} + +/// Everything the `spectra` mode reports about one run. +#[derive(Debug, Clone, Default)] +pub struct SpectraMetrics { + /// Per-level metrics, keyed by MS level. + pub levels: BTreeMap, + /// Earliest retention time seen, in minutes. + pub rt_min: f64, + /// Latest retention time seen, in minutes. + pub rt_max: f64, + /// Charge state distribution of the precursors selected for fragmentation. + pub precursor_charges: BTreeMap, + /// Precursors whose charge the instrument did not assign. + pub precursors_without_charge: u64, + /// Lowest precursor m/z selected. + pub precursor_mz_min: f64, + /// Highest precursor m/z selected. + pub precursor_mz_max: f64, + /// Precursors selected in total. + pub precursors: u64, + /// Whether anything has been observed yet, so the ranges know to + /// initialise rather than compare against a default. + seen: bool, +} + +impl SpectraMetrics { + /// Fold one spectrum in. + pub fn observe( + &mut self, + level: u8, + peaks: u64, + total_ion_current: f64, + start_time: f64, + precursor: Option<(f64, Option)>, + ) { + let entry = self.levels.entry(level).or_default(); + entry.spectra += 1; + entry.peaks += peaks; + entry.total_ion_current += total_ion_current; + if entry.spectra == 1 { + entry.min_peaks = peaks; + entry.max_peaks = peaks; + } else { + entry.min_peaks = entry.min_peaks.min(peaks); + entry.max_peaks = entry.max_peaks.max(peaks); + } + + if !self.seen { + self.rt_min = start_time; + self.rt_max = start_time; + self.seen = true; + } else { + self.rt_min = self.rt_min.min(start_time); + self.rt_max = self.rt_max.max(start_time); + } + + if let Some((mz, charge)) = precursor { + if self.precursors == 0 { + self.precursor_mz_min = mz; + self.precursor_mz_max = mz; + } else { + self.precursor_mz_min = self.precursor_mz_min.min(mz); + self.precursor_mz_max = self.precursor_mz_max.max(mz); + } + self.precursors += 1; + match charge { + Some(z) => *self.precursor_charges.entry(z).or_insert(0) += 1, + None => self.precursors_without_charge += 1, + } + } + } + + /// Called once every spectrum has been offered. + pub fn finish(&mut self) { + if !self.seen { + self.rt_min = 0.0; + self.rt_max = 0.0; + } + } + + /// Spectra across every level. + pub fn total_spectra(&self) -> u64 { + self.levels.values().map(|l| l.spectra).sum() + } + + /// Peaks across every level. + pub fn total_peaks(&self) -> u64 { + self.levels.values().map(|l| l.peaks).sum() + } + + /// Retention time span, in minutes. + pub fn rt_span(&self) -> f64 { + self.rt_max - self.rt_min + } + + /// Ratio of fragmentation spectra to survey spectra, the usual measure of + /// how hard the instrument was working. + /// + /// `None` when there are no MS1 spectra to divide by, which is what an + /// MS2-only file gives. + pub fn ms2_per_ms1(&self) -> Option { + let ms1 = self.levels.get(&1)?.spectra; + if ms1 == 0 { + return None; + } + let ms2 = self.levels.get(&2).map(|l| l.spectra).unwrap_or(0); + Some(ms2 as f64 / ms1 as f64) + } +} + +#[cfg(test)] +mod tests { + use super::*; + + #[test] + fn levels_accumulate_separately() { + let mut m = SpectraMetrics::default(); + m.observe(1, 100, 10.0, 0.5, None); + m.observe(2, 20, 2.0, 0.6, Some((500.0, Some(2)))); + m.observe(1, 200, 20.0, 0.7, None); + m.finish(); + + assert_eq!(m.levels[&1].spectra, 2); + assert_eq!(m.levels[&1].peaks, 300); + assert_eq!(m.levels[&2].spectra, 1); + assert_eq!(m.total_spectra(), 3); + assert_eq!(m.total_peaks(), 320); + } + + #[test] + fn peak_bounds_start_from_the_first_spectrum_not_from_zero() { + let mut m = SpectraMetrics::default(); + m.observe(1, 50, 1.0, 0.0, None); + m.observe(1, 90, 1.0, 0.1, None); + m.finish(); + assert_eq!(m.levels[&1].min_peaks, 50, "not 0"); + assert_eq!(m.levels[&1].max_peaks, 90); + } + + #[test] + fn the_retention_time_range_spans_what_was_seen() { + let mut m = SpectraMetrics::default(); + m.observe(1, 1, 1.0, 5.0, None); + m.observe(1, 1, 1.0, 2.0, None); + m.observe(1, 1, 1.0, 9.0, None); + m.finish(); + assert_eq!(m.rt_min, 2.0, "not 0.0 from the default"); + assert_eq!(m.rt_max, 9.0); + assert_eq!(m.rt_span(), 7.0); + } + + #[test] + fn precursors_without_a_charge_are_counted_apart() { + let mut m = SpectraMetrics::default(); + m.observe(2, 1, 1.0, 0.0, Some((400.0, Some(2)))); + m.observe(2, 1, 1.0, 0.1, Some((600.0, None))); + m.observe(2, 1, 1.0, 0.2, Some((500.0, Some(2)))); + m.finish(); + assert_eq!(m.precursors, 3); + assert_eq!(m.precursor_charges[&2], 2); + assert_eq!(m.precursors_without_charge, 1); + assert_eq!(m.precursor_mz_min, 400.0); + assert_eq!(m.precursor_mz_max, 600.0); + } + + #[test] + fn the_fragmentation_ratio_needs_survey_spectra_to_divide_by() { + let mut m = SpectraMetrics::default(); + m.observe(2, 1, 1.0, 0.0, None); + m.finish(); + assert_eq!(m.ms2_per_ms1(), None, "an MS2-only file has no ratio"); + + let mut m = SpectraMetrics::default(); + m.observe(1, 1, 1.0, 0.0, None); + m.observe(2, 1, 1.0, 0.1, None); + m.observe(2, 1, 1.0, 0.2, None); + m.finish(); + assert_eq!(m.ms2_per_ms1(), Some(2.0)); + } + + #[test] + fn an_empty_run_reports_zeroes_rather_than_a_default_range() { + let mut m = SpectraMetrics::default(); + m.finish(); + assert_eq!(m.total_spectra(), 0); + assert_eq!(m.rt_span(), 0.0); + assert_eq!(m.ms2_per_ms1(), None); + } +} diff --git a/src/protein/spectra/mod.rs b/src/protein/spectra/mod.rs new file mode 100644 index 00000000..e861713b --- /dev/null +++ b/src/protein/spectra/mod.rs @@ -0,0 +1,52 @@ +//! Mass spectrometry run quality control from mzML. +//! +//! Answers the question a QC report on a raw run should answer: how many +//! spectra were acquired at each level, how much signal they carry, over what +//! retention time window, and what the precursors selected for fragmentation +//! looked like. +//! +//! Reading is delegated to [`mzdata`], which is why this module sits behind +//! the `proteomics` cargo feature. Building without it drops the `spectra` +//! mode rather than failing at run time. + +pub mod metrics; +pub mod output; + +use std::path::Path; + +use anyhow::{Context, Result}; +use mzdata::prelude::*; +use mzdata::MZReader; + +use metrics::SpectraMetrics; + +/// Read an mzML file and summarise it. +pub fn analyse(path: &Path) -> Result { + let reader = MZReader::open_path(path) + .with_context(|| format!("Failed to open spectra file: {}", path.display()))?; + + let mut metrics = SpectraMetrics::default(); + for spectrum in reader { + let level = spectrum.ms_level(); + let peaks = spectrum.peaks().len() as u64; + + // Total ion current as the sum of the peak intensities actually + // present, rather than whatever the instrument wrote in the header. + // The two usually agree; where they do not, the header can describe a + // profile spectrum that has since been centroided, and the peaks are + // the honest answer for a file as it stands. + let tic = f64::from(spectrum.peaks().tic()); + + let start_time = spectrum.start_time(); + + let precursor = spectrum + .precursor() + .and_then(|p| p.ion()) + .map(|ion| (ion.mz, ion.charge)); + + metrics.observe(level, peaks, tic, start_time, precursor); + } + + metrics.finish(); + Ok(metrics) +} diff --git a/src/protein/spectra/output.rs b/src/protein/spectra/output.rs new file mode 100644 index 00000000..30e860fd --- /dev/null +++ b/src/protein/spectra/output.rs @@ -0,0 +1,117 @@ +//! Writer for the mass spectrometry run report. + +use std::io::Write; +use std::path::Path; + +use anyhow::{Context, Result}; + +use super::metrics::SpectraMetrics; + +/// Write the run report. +pub fn write_report(file: &str, metrics: &SpectraMetrics, path: &Path) -> Result<()> { + let mut out = std::fs::File::create(path) + .map(std::io::BufWriter::new) + .with_context(|| format!("Failed to create spectra report: {}", path.display()))?; + + writeln!(out, "# RustQC protein spectra report")?; + writeln!(out, "# file\t{file}")?; + writeln!(out)?; + + writeln!(out, "## Run")?; + writeln!(out, "metric\tvalue")?; + writeln!(out, "spectra\t{}", metrics.total_spectra())?; + writeln!(out, "peaks\t{}", metrics.total_peaks())?; + writeln!(out, "rt_min\t{:.6}", metrics.rt_min)?; + writeln!(out, "rt_max\t{:.6}", metrics.rt_max)?; + writeln!(out, "rt_span\t{:.6}", metrics.rt_span())?; + match metrics.ms2_per_ms1() { + Some(ratio) => writeln!(out, "ms2_per_ms1\t{ratio:.6}")?, + // Written as NA rather than 0, which would read as "no fragmentation" + // when the truth is "no survey scans to divide by". + None => writeln!(out, "ms2_per_ms1\tNA")?, + } + writeln!(out)?; + + writeln!(out, "## Levels")?; + writeln!( + out, + "ms_level\tspectra\tpeaks\tmean_peaks\tmin_peaks\tmax_peaks\ttotal_ion_current" + )?; + for (level, m) in &metrics.levels { + writeln!( + out, + "{level}\t{}\t{}\t{:.4}\t{}\t{}\t{:.4}", + m.spectra, + m.peaks, + m.mean_peaks(), + m.min_peaks, + m.max_peaks, + m.total_ion_current, + )?; + } + writeln!(out)?; + + writeln!(out, "## Precursors")?; + writeln!(out, "metric\tvalue")?; + writeln!(out, "selected\t{}", metrics.precursors)?; + writeln!(out, "without_charge\t{}", metrics.precursors_without_charge)?; + if metrics.precursors > 0 { + writeln!(out, "mz_min\t{:.4}", metrics.precursor_mz_min)?; + writeln!(out, "mz_max\t{:.4}", metrics.precursor_mz_max)?; + } + writeln!(out)?; + + if !metrics.precursor_charges.is_empty() { + writeln!(out, "## Charge states")?; + writeln!(out, "charge\tcount\tfraction")?; + let assigned: u64 = metrics.precursor_charges.values().sum(); + for (charge, count) in &metrics.precursor_charges { + writeln!( + out, + "{charge}\t{count}\t{:.6}", + *count as f64 / assigned as f64 + )?; + } + writeln!(out)?; + } + + out.flush()?; + Ok(()) +} + +#[cfg(test)] +mod tests { + use super::*; + + fn scratch(name: &str) -> std::path::PathBuf { + let dir = std::env::temp_dir().join("rustqc-spectra-output-tests"); + std::fs::create_dir_all(&dir).unwrap(); + dir.join(name) + } + + #[test] + fn a_run_without_survey_scans_reports_na_rather_than_zero() { + let mut metrics = SpectraMetrics::default(); + metrics.observe(2, 10, 1.0, 0.0, None); + metrics.finish(); + let path = scratch("ms2only.txt"); + write_report("f.mzML", &metrics, &path).unwrap(); + let text = std::fs::read_to_string(&path).unwrap(); + assert!( + text.contains("ms2_per_ms1\tNA"), + "0 would read as no fragmentation, which is the opposite of the truth" + ); + } + + #[test] + fn the_charge_section_is_omitted_when_nothing_was_assigned() { + let mut metrics = SpectraMetrics::default(); + metrics.observe(2, 10, 1.0, 0.0, Some((500.0, None))); + metrics.finish(); + let path = scratch("nocharge.txt"); + write_report("f.mzML", &metrics, &path).unwrap(); + let text = std::fs::read_to_string(&path).unwrap(); + assert!(text.contains("without_charge\t1")); + assert!(!text.contains("## Charge states")); + } +} diff --git a/src/rna/mod.rs b/src/rna/mod.rs index 7dbcc094..29b50b64 100644 --- a/src/rna/mod.rs +++ b/src/rna/mod.rs @@ -3,10 +3,12 @@ //! Contains dupRadar duplication rate analysis, featureCounts-compatible output, //! and RSeQC tool reimplementations. -pub mod bam_flags; -pub mod cpp_rng; pub mod dupradar; pub mod featurecounts; -pub mod preseq; pub mod qualimap; pub mod rseqc; + +// These analyses are not RNA-specific and now live in `crate::common`. +// Re-exported here so existing `crate::rna::...` paths and the published +// 0.2.x library surface keep resolving. Drop the shims at 1.0. +pub use crate::common::{bam_flags, cpp_rng, preseq}; diff --git a/src/rna/rseqc/accumulators.rs b/src/rna/rseqc/accumulators.rs index b91a409e..20f2b964 100644 --- a/src/rna/rseqc/accumulators.rs +++ b/src/rna/rseqc/accumulators.rs @@ -11,10 +11,8 @@ use anyhow::Result; use indexmap::IndexMap; use rust_htslib::bam; -use super::bam_stat::{BamStatResult, GcDepthBin}; - -/// Default GC-depth bin size in base pairs (matches upstream samtools default). -const GCD_BIN_SIZE: u64 = 20_000; +// BamStatAccum is read-level and assay-agnostic; it lives in `crate::common` +// and is shared with the dna pipeline. Re-exported so existing paths resolve. use super::common::{self, KnownJunctionSet, ReferenceJunctions}; use super::infer_experiment::{GeneModel, InferExperimentResult}; use super::inner_distance::{ @@ -25,6 +23,7 @@ use super::junction_saturation::SaturationResult; use super::read_distribution::{ChromIntervals, ReadDistributionResult, RegionSets}; use super::read_duplication::ReadDuplicationResult; use super::tin::TinAccum; +pub use crate::common::bam_stat_accum::BamStatAccum; use crate::rna::preseq::PreseqAccum; use crate::rna::bam_flags::*; @@ -116,1200 +115,6 @@ pub struct RseqcConfig { // Per-tool accumulators // =================================================================== -/// bam_stat accumulator — simple flag/MAPQ counting. -/// -/// Also collects the additional counters needed for samtools-compatible -/// flagstat, idxstats, and stats output. -#[derive(Debug)] -pub struct BamStatAccum { - // --- RSeQC bam_stat fields (original) --- - /// Total BAM records seen (primary + secondary + supplementary + unmapped). - pub total_records: u64, - /// Records with QC-fail flag (0x200). - pub qc_failed: u64, - /// Records with duplicate flag (0x400). - pub duplicates: u64, - /// Secondary alignment records (0x100). RSeQC calls these "non-primary". - pub non_primary: u64, - /// Unmapped reads (0x4). - pub unmapped: u64, - /// Mapped reads with MAPQ < cutoff. - pub non_unique: u64, - /// Mapped reads with MAPQ >= cutoff (uniquely mapped). - pub unique: u64, - /// Among unique reads: read1 in a pair. - pub read_1: u64, - /// Among unique reads: read2 in a pair. - pub read_2: u64, - /// Among unique reads: forward strand. - pub forward: u64, - /// Among unique reads: reverse strand. - pub reverse: u64, - /// Among unique reads: has splice junction (CIGAR N). - pub splice: u64, - /// Among unique reads: no splice junctions. - pub non_splice: u64, - /// Among unique reads: in proper pairs (0x2). - pub proper_pairs: u64, - /// Among proper-paired unique reads: mates on different chromosomes. - pub proper_pair_diff_chrom: u64, - - // --- samtools flagstat additional fields --- - /// Secondary alignments (0x100) — counted independently of QC/dup. - pub secondary: u64, - /// Supplementary alignments (0x800) — counted independently of QC/dup. - pub supplementary: u64, - /// All mapped records (not 0x4), regardless of QC/dup. - pub mapped: u64, - /// Paired reads (0x1), regardless of QC/dup. - pub paired_flagstat: u64, - /// Read1 in pair (0x40), regardless of QC/dup — for flagstat. - pub read1_flagstat: u64, - /// Read2 in pair (0x80), regardless of QC/dup — for flagstat. - pub read2_flagstat: u64, - /// First fragments for samtools stats: primary reads that are not "last fragments". - pub first_fragments: u64, - /// Last fragments for samtools stats: primary reads with 0x80 flag. - pub last_fragments: u64, - /// Properly paired reads (0x1 + 0x2), regardless of QC/dup. - pub properly_paired: u64, - /// Both mates mapped (paired + both !unmapped). - pub both_mapped: u64, - /// Singletons (paired, this mapped, mate unmapped). - pub singletons: u64, - /// Paired, both mapped, different reference. - pub mate_diff_chr: u64, - /// Paired, both mapped, different reference, MAPQ >= 5. - pub mate_diff_chr_mapq5: u64, - - // --- samtools idxstats additional fields --- - /// Per-reference (tid) mapped and unmapped counts. - pub chrom_counts: HashMap, - /// Unmapped reads with no reference (tid < 0). - pub unplaced_unmapped: u64, - - // --- samtools stats SN additional fields --- - /// Sum of query sequence lengths for all primary reads (non-secondary, non-supplementary). - pub total_len: u64, - /// Sum of first fragment (read1 or unpaired) sequence lengths. - pub total_first_fragment_len: u64, - /// Sum of last fragment (read2) sequence lengths. - pub total_last_fragment_len: u64, - /// Sum of query lengths for mapped primary reads. - pub bases_mapped: u64, - /// Sum of M/=/X CIGAR operations for mapped primary reads. - pub bases_mapped_cigar: u64, - /// Sum of query lengths for duplicate-flagged primary reads. - pub bases_duplicated: u64, - /// Maximum query sequence length (among primary reads). - pub max_len: u64, - /// Maximum first-fragment sequence length. - pub max_first_fragment_len: u64, - /// Maximum last-fragment sequence length. - pub max_last_fragment_len: u64, - /// Sum of average per-read base qualities (for average-of-averages). - pub quality_sum: f64, - /// Number of reads contributing to quality_sum (primary, non-QC-fail). - pub quality_count: u64, - /// Sum of NM tag values across mapped primary reads. - pub mismatches: u64, - /// Insert size with orientation: abs_tlen → [total, inward, outward, other]. - /// Only one mate per pair contributes (upstream mate), capped at 8000. - pub is_hist: HashMap, - /// Inward-oriented pairs (FR). - pub inward_pairs: u64, - /// Outward-oriented pairs (RF). - pub outward_pairs: u64, - /// Other orientation pairs (FF, RR). - pub other_orientation: u64, - /// Total primary reads (non-secondary, non-supplementary). - pub primary_count: u64, - /// Primary mapped reads count (non-secondary, non-supplementary, !unmapped). - pub primary_mapped: u64, - /// Primary duplicate reads. - pub primary_duplicates: u64, - /// Primary mapped reads with MAPQ = 0 (matching upstream samtools stats). - pub reads_mq0: u64, - /// Primary non-QC-fail mapped paired reads where mate is also mapped. - pub reads_mapped_and_paired: u64, - - // --- samtools stats histogram/distribution fields --- - /// Read length histogram (all primary reads): length → count. - pub rl_hist: HashMap, - /// First fragment read length histogram: length → count. - pub frl_hist: HashMap, - /// Last fragment read length histogram: length → count. - pub lrl_hist: HashMap, - /// MAPQ histogram: primary, mapped, !qcfail, !dup (quality 0-255). - pub mapq_hist: [u64; 256], - /// Per-cycle quality for first fragments (primary, mapped, !qcfail, !dup). - /// Outer: cycle index. Inner: quality value → count (64 buckets covers Q0-Q63). - pub ffq: Vec<[u64; 64]>, - /// Per-cycle quality for last fragments. - pub lfq: Vec<[u64; 64]>, - /// GC content step-function for first fragments, 200 bins (matching samtools ngc=200). - /// Each bin i stores the number of reads with gc_count * 199 / seq_len <= i. - pub gcf: [u64; 200], - /// GC content step-function for last fragments, 200 bins. - pub gcl: [u64; 200], - /// Per-cycle base composition for first fragments (primary, mapped, !qcfail, !dup). - /// [A, C, G, T, N, Other] per cycle. - pub fbc: Vec<[u64; 6]>, - /// Per-cycle base composition for last fragments. - pub lbc: Vec<[u64; 6]>, - /// Per-cycle base composition (read-oriented) for first fragments. - /// Reverse strand reads contribute in reversed cycle order. - pub fbc_ro: Vec<[u64; 6]>, - /// Per-cycle base composition (read-oriented) for last fragments. - pub lbc_ro: Vec<[u64; 6]>, - /// Per-cycle base composition (reverse-complemented for reverse-strand reads, - /// combined first+last fragments). Used for GCT output. [A, C, G, T] only. - pub gcc_rc: Vec<[u64; 4]>, - /// Total base counters for first fragments: [A, C, G, T, N]. - pub ftc: [u64; 5], - /// Total base counters for last fragments: [A, C, G, T, N]. - pub ltc: [u64; 5], - /// Indel distribution by size: length → [insertions, deletions]. - pub id_hist: HashMap, - /// Indels per cycle: cycle → [ins_fwd, ins_rev, del_fwd, del_rev]. - pub ic: Vec<[u64; 4]>, - /// CRC32 checksum sums: [names, sequences, qualities]. - /// Each is the wrapping u32 sum of per-read CRC32 values. - pub chk: [u32; 3], - /// Coverage distribution: depth → number of reference positions at that depth. - /// Populated from a round buffer pileup during sorted BAM processing. - pub cov_hist: HashMap, - /// Circular buffer for coverage pileup, matching upstream samtools design. - /// `cov_buf[cov_buf_idx]` corresponds to reference position `cov_buf_pos`. - /// The buffer grows dynamically to accommodate `max_read_length * 5`. - cov_buf: Vec, - /// Index into `cov_buf` corresponding to `cov_buf_pos`. - cov_buf_idx: usize, - /// Reference position of the element at `cov_buf[cov_buf_idx]`. - cov_buf_pos: i64, - /// Current chromosome tid for round buffer tracking. - cov_buf_tid: i32, - - // --- GC-depth (GCD section) fields --- - /// Accumulated GC-depth bins (one per `GCD_BIN_SIZE`-bp genomic window). - gcd_bins: Vec, - /// Start position of the current GCD bin. - gcd_pos: i64, - /// Chromosome tid of the current GCD bin. - gcd_tid: i32, -} - -impl Default for BamStatAccum { - fn default() -> Self { - Self { - total_records: 0, - qc_failed: 0, - duplicates: 0, - non_primary: 0, - unmapped: 0, - non_unique: 0, - unique: 0, - read_1: 0, - read_2: 0, - forward: 0, - reverse: 0, - splice: 0, - non_splice: 0, - proper_pairs: 0, - proper_pair_diff_chrom: 0, - secondary: 0, - supplementary: 0, - mapped: 0, - paired_flagstat: 0, - read1_flagstat: 0, - read2_flagstat: 0, - first_fragments: 0, - last_fragments: 0, - properly_paired: 0, - both_mapped: 0, - singletons: 0, - mate_diff_chr: 0, - mate_diff_chr_mapq5: 0, - chrom_counts: HashMap::new(), - unplaced_unmapped: 0, - total_len: 0, - total_first_fragment_len: 0, - total_last_fragment_len: 0, - bases_mapped: 0, - bases_mapped_cigar: 0, - bases_duplicated: 0, - max_len: 0, - max_first_fragment_len: 0, - max_last_fragment_len: 0, - quality_sum: 0.0, - quality_count: 0, - mismatches: 0, - is_hist: HashMap::new(), - inward_pairs: 0, - outward_pairs: 0, - other_orientation: 0, - primary_count: 0, - primary_mapped: 0, - primary_duplicates: 0, - reads_mq0: 0, - reads_mapped_and_paired: 0, - rl_hist: HashMap::new(), - frl_hist: HashMap::new(), - lrl_hist: HashMap::new(), - mapq_hist: [0u64; 256], - ffq: Vec::new(), - lfq: Vec::new(), - gcf: [0u64; 200], - gcl: [0u64; 200], - fbc: Vec::new(), - lbc: Vec::new(), - fbc_ro: Vec::new(), - lbc_ro: Vec::new(), - gcc_rc: Vec::new(), - ftc: [0u64; 5], - ltc: [0u64; 5], - id_hist: HashMap::new(), - ic: Vec::new(), - chk: [0u32; 3], - cov_hist: HashMap::new(), - cov_buf: vec![0u32; 1500], // matches upstream samtools: nbases * 5 = 300 * 5 - cov_buf_idx: 0, - cov_buf_pos: 0, - cov_buf_tid: -1, - gcd_bins: Vec::new(), - gcd_pos: -1, - gcd_tid: -1, - } - } -} - -impl BamStatAccum { - /// Process a single BAM record. Called for EVERY record (before counting filters). - /// - /// Collects counters for: - /// - RSeQC bam_stat (original cascade with early returns) - /// - samtools flagstat (counts all records independently) - /// - samtools idxstats (per-reference mapped/unmapped counts) - /// - samtools stats SN section (sequence lengths, quality, insert size, etc.) - pub fn process_read(&mut self, record: &bam::Record, mapq_cut: u8) { - let flags = record.flags(); - self.total_records += 1; - - let is_secondary = flags & BAM_FSECONDARY != 0; - let is_supplementary = flags & BAM_FSUPPLEMENTARY != 0; - let is_unmapped = flags & BAM_FUNMAP != 0; - let is_paired = flags & BAM_FPAIRED != 0; - let is_dup = flags & BAM_FDUP != 0; - let is_qcfail = flags & BAM_FQCFAIL != 0; - let is_primary = !is_secondary && !is_supplementary; - let is_mapped = !is_unmapped; - let tid = record.tid(); - let mapq = record.mapq(); - - // ================================================================= - // samtools flagstat counters (count ALL records, no early returns) - // ================================================================= - if is_secondary { - self.secondary += 1; - } - if is_supplementary { - self.supplementary += 1; - } - if is_mapped { - self.mapped += 1; - } - // samtools stats: "1st fragments" / "last fragments" count primary reads only - // For paired reads: read2 flag -> last, everything else -> 1st - // For SE reads (no PAIRED flag): all counted as 1st fragments - if is_primary { - if flags & BAM_FREAD2 != 0 { - self.last_fragments += 1; - } else { - self.first_fragments += 1; - } - } - // samtools flagstat: paired-read metrics count PRIMARY reads only - // (secondary/supplementary are excluded from paired/read1/read2/properly-paired counts) - if is_paired && is_primary { - self.paired_flagstat += 1; - if flags & BAM_FREAD1 != 0 { - self.read1_flagstat += 1; - } - if flags & BAM_FREAD2 != 0 { - self.read2_flagstat += 1; - } - if flags & BAM_FPROPER_PAIR != 0 { - self.properly_paired += 1; - } - let mate_unmapped = flags & BAM_FMUNMAP != 0; - if is_mapped && !mate_unmapped { - self.both_mapped += 1; - if tid != record.mtid() { - self.mate_diff_chr += 1; - if mapq >= 5 { - self.mate_diff_chr_mapq5 += 1; - } - } - } - if is_mapped && mate_unmapped { - self.singletons += 1; - } - } - - // ================================================================= - // samtools idxstats counters (per-reference) - // ================================================================= - if is_unmapped { - if tid >= 0 { - // Unmapped read placed on a reference (has tid) - self.chrom_counts.entry(tid).or_insert((0, 0)).1 += 1; - } else { - self.unplaced_unmapped += 1; - } - } else if tid >= 0 { - // Mapped read - self.chrom_counts.entry(tid).or_insert((0, 0)).0 += 1; - } - - // ================================================================= - // CHK checksums: computed on ALL reads (including secondary and - // supplementary). Matches samtools stats.c update_checksum() which - // is called before the secondary-read early return. - // ================================================================= - { - let qname = record.qname(); - let name_crc = crc32fast::hash(qname); - self.chk[0] = self.chk[0].wrapping_add(name_crc); - - let seq_len = record.seq_len(); - if seq_len > 0 { - // SAFETY: We access the raw BAM record data to compute CRC32 - // checksums matching samtools' approach. The pointer arithmetic - // replicates htslib's bam_get_seq() macro: - // data + l_qname + (n_cigar << 2) - // The seq_len > 0 guard above ensures sequence data exists. - // The slice length seq_len.div_ceil(2) matches the BAM spec's - // 4-bit encoded sequence format: (seq_len+1)/2 bytes. - let seq_bytes = unsafe { - let inner = record.inner(); - let data = inner.data; - let seq_offset = - inner.core.l_qname as isize + ((inner.core.n_cigar as isize) << 2); - let seq_nbytes = seq_len.div_ceil(2); - std::slice::from_raw_parts(data.offset(seq_offset), seq_nbytes) - }; - let seq_crc = crc32fast::hash(seq_bytes); - self.chk[1] = self.chk[1].wrapping_add(seq_crc); - - let qual = record.qual(); - let qual_crc = crc32fast::hash(qual); - self.chk[2] = self.chk[2].wrapping_add(qual_crc); - } - } - - // Track gc_count from the primary-read per-cycle loop so the GCD - // section below can reuse it without re-scanning the sequence. - let mut primary_gc_count: u64 = 0; - - // ================================================================= - // samtools stats SN counters (primary reads only) - // ================================================================= - if is_primary { - self.primary_count += 1; - let seq_len = record.seq_len() as u64; - let mate_unmapped = flags & BAM_FMUNMAP != 0; - - self.total_len += seq_len; - let is_last_fragment = is_paired && flags & BAM_FREAD2 != 0; - if is_last_fragment { - self.total_last_fragment_len += seq_len; - if seq_len > self.max_last_fragment_len { - self.max_last_fragment_len = seq_len; - } - } else { - self.total_first_fragment_len += seq_len; - if seq_len > self.max_first_fragment_len { - self.max_first_fragment_len = seq_len; - } - } - if seq_len > self.max_len { - self.max_len = seq_len; - } - - // RL/FRL/LRL: read length histograms (all primary reads) - *self.rl_hist.entry(seq_len).or_insert(0) += 1; - if is_last_fragment { - *self.lrl_hist.entry(seq_len).or_insert(0) += 1; - } else { - *self.frl_hist.entry(seq_len).or_insert(0) += 1; - } - - if is_dup { - self.primary_duplicates += 1; - self.bases_duplicated += seq_len; - } - // "reads mapped and paired" for samtools stats: primary, non-QC-fail, - // mapped, paired, mate also mapped - if is_mapped && is_paired && !is_qcfail && !mate_unmapped { - self.reads_mapped_and_paired += 1; - } - if is_mapped { - self.primary_mapped += 1; - self.bases_mapped += seq_len; - - // samtools stats: reads MQ0 counts primary mapped reads with MAPQ=0 - // (upstream stats.c: MQ0 is counted inside collect_orig_read_stats, - // which is only called for IS_ORIGINAL reads = non-secondary, non-supplementary) - if record.mapq() == 0 { - self.reads_mq0 += 1; - } - - // NOTE: bases_mapped_cigar is now computed in the IC/ID CIGAR - // loop below (for all mapped non-secondary reads) to avoid a - // separate full CIGAR traversal here. - - // NM tag (edit distance) - if let Ok(rust_htslib::bam::record::Aux::U8(nm)) = record.aux(b"NM") { - self.mismatches += u64::from(nm); - } else if let Ok(rust_htslib::bam::record::Aux::U16(nm)) = record.aux(b"NM") { - self.mismatches += u64::from(nm); - } else if let Ok(rust_htslib::bam::record::Aux::U32(nm)) = record.aux(b"NM") { - self.mismatches += u64::from(nm); - } else if let Ok(rust_htslib::bam::record::Aux::I8(nm)) = record.aux(b"NM") { - if nm > 0 { - self.mismatches += nm as u64; - } - } else if let Ok(rust_htslib::bam::record::Aux::I16(nm)) = record.aux(b"NM") { - if nm > 0 { - self.mismatches += nm as u64; - } - } else if let Ok(rust_htslib::bam::record::Aux::I32(nm)) = record.aux(b"NM") { - if nm > 0 { - self.mismatches += nm as u64; - } - } - - // Insert size + orientation for paired primary reads where both - // mates are mapped. Matches samtools stats gate: - // IS_PAIRED_AND_MAPPED && IS_ORIGINAL - // if (isize > 0 || tid == mtid) - // Both mates contribute; samtools divides by 2 at output. - // We do the same in write_insert_size() and the SN section. - if is_paired && !mate_unmapped { - let tid = record.tid(); - let mtid = record.mtid(); - let tlen = record.insert_size(); - let abs_tlen = tlen.unsigned_abs(); - - if abs_tlen > 0 || tid == mtid { - let pos = record.pos(); - let mpos = record.mpos(); - - // Compute orientation (only meaningful for same-chromosome) - let pos_fst = mpos - pos; - let is_fst: i64 = if flags & BAM_FREAD1 != 0 { 1 } else { -1 }; - let is_fwd: i64 = if flags & BAM_FREVERSE != 0 { -1 } else { 1 }; - let is_mfwd: i64 = if flags & BAM_FMREVERSE != 0 { -1 } else { 1 }; - - // orientation_idx: 1=inward, 2=outward, 3=other - let orientation_idx = if is_fwd * is_mfwd > 0 { - self.other_orientation += 1; - 3usize - } else if is_fst * pos_fst > 0 { - if is_fst * is_fwd > 0 { - self.inward_pairs += 1; - 1usize - } else { - self.outward_pairs += 1; - 2usize - } - } else if is_fst * pos_fst < 0 { - if is_fst * is_fwd > 0 { - self.outward_pairs += 1; - 2usize - } else { - self.inward_pairs += 1; - 1usize - } - } else { - self.inward_pairs += 1; - 1usize - }; - - if abs_tlen > 0 { - // Cap at MAX_INSERT_SIZE (8000), matching - // samtools stats which accumulates overflow - // into the cap bucket. - let capped = abs_tlen.min(8000); - let entry = self.is_hist.entry(capped).or_insert([0; 4]); - entry[0] += 1; // total - entry[orientation_idx] += 1; - } - } - } - } - - // Average quality for primary non-QC-fail reads. - // Upstream samtools stats computes per-BASE quality average: - // sum of all individual base qualities / total bases. - // (Not a per-read average of averages.) - if !is_qcfail { - let quals = record.qual(); - if !quals.is_empty() { - let base_qual_sum: f64 = quals.iter().map(|&q| f64::from(q)).sum::(); - self.quality_sum += base_qual_sum; - self.quality_count += quals.len() as u64; - } - } - - // ============================================================= - // MAPQ histogram: primary + mapped + !qcfail + !dup - // (matches samtools stats.c:1239 five-flag exclusion) - // ============================================================= - if is_mapped && !is_qcfail && !is_dup { - self.mapq_hist[mapq as usize] += 1; - } - - // ============================================================= - // Per-cycle quality & base composition histograms: - // FFQ/LFQ, FBC/LBC, GCF/GCL, FTC/LTC, FBC_RO/LBC_RO - // - // Upstream samtools stats includes duplicates, unmapped, and - // qcfail reads in these histograms (collect_orig_read_stats - // has no such checks). Only secondary+supplementary are - // excluded (via IS_ORIGINAL), which is already handled by - // the outer is_primary guard. - // ============================================================= - { - let is_reverse = flags & BAM_FREVERSE != 0; - - let seq = record.seq(); - let quals = record.qual(); - let read_len = seq.len(); - - // Determine which arrays to use (first vs last fragment) - // If paired: read2 = last, read1 = first. If SE: all = first. - let (qual_arr, base_arr, base_ro_arr, gc_arr, tc_arr) = if is_last_fragment { - ( - &mut self.lfq, - &mut self.lbc, - &mut self.lbc_ro, - &mut self.gcl, - &mut self.ltc, - ) - } else { - ( - &mut self.ffq, - &mut self.fbc, - &mut self.fbc_ro, - &mut self.gcf, - &mut self.ftc, - ) - }; - - // Ensure per-cycle arrays are large enough - if read_len > qual_arr.len() { - qual_arr.resize(read_len, [0u64; 64]); - } - if read_len > base_arr.len() { - base_arr.resize(read_len, [0u64; 6]); - } - if read_len > base_ro_arr.len() { - base_ro_arr.resize(read_len, [0u64; 6]); - } - if read_len > self.gcc_rc.len() { - self.gcc_rc.resize(read_len, [0u64; 4]); - } - - let mut gc_count: u64 = 0; - - // Pre-built lookup tables for the per-cycle inner loop, - // avoiding branches and match overhead on every base. - // - // BAM 4-bit encoding: A=1, C=2, G=4, T=8, N=15, others=0,3,5..14 - // BASE_IDX[nibble] → 0=A, 1=C, 2=G, 3=T, 4=N, 5=Other - const BASE_IDX: [u8; 16] = [5, 0, 1, 5, 2, 5, 5, 5, 3, 5, 5, 5, 5, 5, 5, 4]; - // RC_IDX[base_idx] → reverse-complement base_idx (A↔T, C↔G) - // Only meaningful for base_idx 0-3 (ACGT). Index 4/5 not used. - const RC_IDX: [u8; 6] = [3, 2, 1, 0, 4, 5]; // A→T, C→G, G→C, T→A - - // Hoist the is_reverse branch outside the inner loop so the - // compiler can version the loop and potentially auto-vectorize - // each variant independently. - if !is_reverse { - for i in 0..read_len { - let q = quals[i] as usize; - qual_arr[i][q.min(63)] += 1; - - let base_idx = BASE_IDX[seq.encoded_base(i) as usize] as usize; - base_arr[i][base_idx] += 1; - base_ro_arr[i][base_idx] += 1; - if base_idx < 4 { - self.gcc_rc[i][base_idx] += 1; - } - if base_idx == 1 || base_idx == 2 { - gc_count += 1; - } - if base_idx < 5 { - tc_arr[base_idx] += 1; - } - } - } else { - for i in 0..read_len { - let ro_cycle = read_len - 1 - i; - let q = quals[i] as usize; - qual_arr[ro_cycle][q.min(63)] += 1; - - let base_idx = BASE_IDX[seq.encoded_base(i) as usize] as usize; - base_arr[i][base_idx] += 1; - base_ro_arr[ro_cycle][base_idx] += 1; - if base_idx < 4 { - self.gcc_rc[ro_cycle][RC_IDX[base_idx] as usize] += 1; - } - if base_idx == 1 || base_idx == 2 { - gc_count += 1; - } - if base_idx < 5 { - tc_arr[base_idx] += 1; - } - } - } - - // Save gc_count for GCD section below (avoids re-scanning the sequence). - primary_gc_count = gc_count; - - // GC content: cumulative step function with ngc=200 bins. - // Matches samtools stats.c:925-941. For a read with gc_count G/C - // bases out of read_len total, increment bins gc_idx_min..gc_idx_max. - let ngc: usize = 200; - if let (Some(gc_idx_min), Some(gc_idx_max)) = ( - (gc_count as usize * (ngc - 1)).checked_div(read_len), - ((gc_count as usize + 1) * (ngc - 1)).checked_div(read_len), - ) { - let gc_idx_max = gc_idx_max.min(ngc - 1); - for item in gc_arr.iter_mut().take(gc_idx_max).skip(gc_idx_min) { - *item += 1; - } - } - } - } // if is_primary - - // ============================================================= - // Indel distribution (ID) and indels per cycle (IC) from CIGAR. - // - // Upstream samtools stats calls count_indels() AFTER the - // secondary-read early return (line 1206-1210) and the - // IS_UNMAPPED return (line 1255), but OUTSIDE IS_ORIGINAL(). - // This means: all mapped, non-secondary reads are included - // (supplementary, duplicate, qcfail all contribute). - // - // IC uses first-fragment/last-fragment read order (not - // forward/reverse strand) and read-oriented cycle indices, - // matching upstream count_indels(). - // ============================================================= - // ============================================================= - // Combined single-CIGAR-pass block for IC/ID (indel distribution), - // bases_mapped_cigar, and COV (coverage ring-buffer pileup). - // - // Both IC/ID and COV apply to the same read set (mapped, - // non-secondary). Merging them into one CIGAR traversal - // eliminates two redundant record.cigar() calls per read. - // - // IC/ID: Upstream samtools stats calls count_indels() outside - // IS_ORIGINAL() — supplementary/dup/qcfail all contribute. - // IC uses first/last-fragment order and read-oriented cycles. - // - // COV: Circular-buffer pileup; buffer flushed up to read start - // before CIGAR walk; M/=/X blocks inserted as ranges. - // Buffer grown to max_read_len * 5 as needed. - // ============================================================= - if is_mapped && !is_secondary { - use rust_htslib::bam::record::Cigar as C; - let is_reverse = flags & BAM_FREVERSE != 0; - let read_len = record.seq_len(); - let tid = record.tid(); - let pos = record.pos(); // 0-based - - // Upstream order: paired ? (read1?FIRST:0)+(read2?LAST:0) : FIRST - let order: u32 = if is_paired { - (if flags & BAM_FREAD1 != 0 { 1 } else { 0 }) - + (if flags & BAM_FREAD2 != 0 { 2 } else { 0 }) - } else { - 1 // unpaired → FIRST - }; - - // COV buffer setup (must happen before CIGAR walk). - // Skip reads with no sequence (upstream samtools early-return). - let do_cov = read_len > 0; - let buf_size = if do_cov { - // Grow buffer to max_read_len * 5 if needed. - // When growing, linearise the circular data just like - // upstream samtools: copy [idx..old_size] then [0..idx] - // into a fresh buffer, and reset idx to 0. - let need = read_len * 5; - if need > self.cov_buf.len() { - let old_size = self.cov_buf.len(); - let mut new_buf = vec![0u32; need]; - let head = old_size - self.cov_buf_idx; - new_buf[..head].copy_from_slice(&self.cov_buf[self.cov_buf_idx..]); - new_buf[head..head + self.cov_buf_idx] - .copy_from_slice(&self.cov_buf[..self.cov_buf_idx]); - self.cov_buf = new_buf; - self.cov_buf_idx = 0; - } - let bs = self.cov_buf.len(); - // Flush entire buffer on chromosome change - if tid != self.cov_buf_tid { - self.flush_cov_buf_all(); - self.cov_buf_tid = tid; - self.cov_buf_pos = pos; - self.cov_buf_idx = 0; - } - // Flush positions from cov_buf_pos up to read start - self.cov_buf_flush_to(pos, bs); - bs - } else { - 0 - }; - - // Single CIGAR traversal serving IC/ID + bases_mapped_cigar + COV - let cigar = record.cigar(); - let mut icycle: usize = 0; - let mut cigar_mapped: u64 = 0; - let mut ref_pos = pos; - - for op in cigar.iter() { - match op { - C::Ins(n) => { - let ncig = *n as usize; - let len = *n as u64; - cigar_mapped += len; // I counts toward bases_mapped_cigar - - // ID: indel size distribution - let id_entry = self.id_hist.entry(len).or_insert([0; 2]); - id_entry[0] += 1; // insertions - - // IC: indels per cycle (read-oriented index) - let idx = if is_reverse { - read_len.saturating_sub(icycle + ncig) - } else { - icycle - }; - if idx >= self.ic.len() { - self.ic.resize(idx + 1, [0u64; 4]); - } - if order == 1 { - self.ic[idx][0] += 1; // ins_1st - } - if order == 2 { - self.ic[idx][1] += 1; // ins_2nd - } - - icycle += ncig; // I advances query cycle; ref unchanged - // COV: I consumes no reference positions - } - C::Del(n) => { - let len = *n as u64; - // ID: indel size distribution - let id_entry = self.id_hist.entry(len).or_insert([0; 2]); - id_entry[1] += 1; // deletions - - // IC: indels per cycle (read-oriented index) - let idx = if is_reverse { - if icycle == 0 { - // Discard meaningless deletions at cycle 0 - // (upstream: "if (idx<0) continue;") - ref_pos += *n as i64; // still advance ref for COV - continue; - } - read_len.saturating_sub(icycle + 1) - } else { - if icycle == 0 { - ref_pos += *n as i64; - continue; - } - icycle - 1 - }; - if idx >= self.ic.len() { - self.ic.resize(idx + 1, [0u64; 4]); - } - if order == 1 { - self.ic[idx][2] += 1; // del_1st - } - if order == 2 { - self.ic[idx][3] += 1; // del_2nd - } - // D does NOT advance query cycle; does advance ref - ref_pos += *n as i64; - } - C::Match(n) | C::Equal(n) | C::Diff(n) => { - let len = *n as u64; - cigar_mapped += len; // M/=/X count toward bases_mapped_cigar - icycle += *n as usize; - // COV: M/=/X consumes reference positions - if do_cov { - let end = ref_pos + *n as i64; - self.cov_buf_insert(ref_pos, end, buf_size); - ref_pos = end; - } else { - ref_pos += *n as i64; - } - } - C::RefSkip(n) => { - ref_pos += *n as i64; // N advances ref (COV skips it) - } - C::SoftClip(n) => { - icycle += *n as usize; // S advances query cycle - // COV: S consumes no reference positions - } - C::HardClip(_) | C::Pad(_) => {} - } - } - self.bases_mapped_cigar += cigar_mapped; - } // if is_mapped && !is_secondary (IC/ID + COV combined) - - // ============================================================= - // GCD: GC-depth accumulation (no-reference path). - // - // Matches upstream samtools stats without --ref-seq: bins of - // GCD_BIN_SIZE bp, depth incremented for each read, GC fraction - // accumulated from the read's sequence. - // - // Included reads: mapped, non-secondary (same as COV). - // - // NOTE: gc_count_for_gcd is set from the primary-read per-cycle - // loop above (when is_primary is true), or computed here only for - // non-primary mapped reads, avoiding a redundant full sequence scan. - // ============================================================= - if is_mapped && !is_secondary { - let tid = record.tid(); - let pos = record.pos(); - let seq_len = record.seq_len(); - - if seq_len > 0 { - // Start a new bin on: first read, chromosome change, or - // read beyond current bin boundary. - let new_bin = self.gcd_pos < 0 - || tid != self.gcd_tid - || pos - self.gcd_pos > GCD_BIN_SIZE as i64; - - if new_bin { - self.gcd_bins.push(GcDepthBin { gc: 0.0, depth: 0 }); - self.gcd_pos = pos; - self.gcd_tid = tid; - } - - // Increment depth and accumulate GC fraction from read seq. - if let Some(bin) = self.gcd_bins.last_mut() { - bin.depth += 1; - // For primary reads, gc_count was already computed in the - // per-cycle base loop above. For non-primary mapped reads - // (supplementary, etc.) compute it here from the sequence. - let gc_count: u32 = if is_primary { - primary_gc_count as u32 - } else { - let seq = record.seq(); - let mut count: u32 = 0; - for i in 0..seq_len { - let base = seq.encoded_base(i); - if base == 2 || base == 4 { - count += 1; - } - } - count - }; - bin.gc += gc_count as f32 / seq_len as f32; - } - } - } // if is_mapped && !is_secondary (GCD) - - // ================================================================= - // RSeQC bam_stat cascade (original logic, with early returns) - // ================================================================= - - // 1. QC-failed - if is_qcfail { - self.qc_failed += 1; - return; - } - - // 2. Duplicate - if is_dup { - self.duplicates += 1; - return; - } - - // 3. Secondary (non-primary) — NOT supplementary - if is_secondary { - self.non_primary += 1; - return; - } - - // 4. Unmapped - if is_unmapped { - self.unmapped += 1; - return; - } - - // 5. MAPQ classification - if mapq < mapq_cut { - self.non_unique += 1; - return; - } - - // Uniquely mapped - self.unique += 1; - - if flags & BAM_FREAD1 != 0 { - self.read_1 += 1; - } - if flags & BAM_FREAD2 != 0 { - self.read_2 += 1; - } - if flags & BAM_FREVERSE != 0 { - self.reverse += 1; - } else { - self.forward += 1; - } - - // Splice detection: CIGAR N operation - let has_splice = record - .cigar() - .iter() - .any(|op| matches!(op, rust_htslib::bam::record::Cigar::RefSkip(_))); - if has_splice { - self.splice += 1; - } else { - self.non_splice += 1; - } - - // Proper pair analysis - if is_paired && flags & BAM_FPROPER_PAIR != 0 { - self.proper_pairs += 1; - if tid != record.mtid() { - self.proper_pair_diff_chrom += 1; - } - } - } - - /// Flush all remaining positions in the coverage round buffer into cov_hist. - /// Must be called after processing all reads (or when switching chromosomes). - /// Flush the circular buffer from `cov_buf_pos` up to (but not including) `pos`. - /// Each slot's depth is recorded in `cov_hist` and the slot is zeroed. - /// Matches upstream `round_buffer_flush` logic from samtools stats.c. - fn cov_buf_flush_to(&mut self, pos: i64, buf_size: usize) { - if pos - self.cov_buf_pos >= buf_size as i64 { - // Gap exceeds buffer size. Match upstream samtools exactly: - // flush `size - 1` positions (from cov_buf_pos to - // cov_buf_pos + size - 2), leaving the LAST slot untouched. - // Then advance idx by `size - 1` and jump pos. - // - // Upstream (stats.c round_buffer_flush lines 334-366): - // pos = rbuf.pos + size - 1; // cap at last slot - // ito = lidx2ridx(start, size, rbuf.pos, pos-1); - // // flush from start to ito (size-1 slots) - // rbuf.start = lidx2ridx(start, size, rbuf.pos, pos); - // rbuf.pos = new_pos; - let flush_count = buf_size - 1; // flush all but the last slot - for _ in 0..flush_count { - let depth = self.cov_buf[self.cov_buf_idx]; - if depth > 0 { - *self.cov_hist.entry(depth).or_insert(0) += 1; - self.cov_buf[self.cov_buf_idx] = 0; - } - self.cov_buf_idx += 1; - if self.cov_buf_idx >= buf_size { - self.cov_buf_idx = 0; - } - } - // idx now points to the ONE unflushed slot (the last position - // in the old window). Jump pos to the new read position. - self.cov_buf_pos = pos; - } else { - // Normal case: flush slot by slot. - while self.cov_buf_pos < pos { - let depth = self.cov_buf[self.cov_buf_idx]; - if depth > 0 { - *self.cov_hist.entry(depth).or_insert(0) += 1; - self.cov_buf[self.cov_buf_idx] = 0; - } - self.cov_buf_idx += 1; - if self.cov_buf_idx >= buf_size { - self.cov_buf_idx = 0; - } - self.cov_buf_pos += 1; - } - } - } - - /// Insert a contiguous reference range `[from, to)` into the circular buffer, - /// incrementing depth for each position. The range must fit within `buf_size`. - fn cov_buf_insert(&mut self, from: i64, to: i64, buf_size: usize) { - for ref_pos in from..to { - // Map ref_pos to buffer index: offset from cov_buf_idx by (ref_pos - cov_buf_pos) - let offset = (ref_pos - self.cov_buf_pos) as usize; - let idx = (self.cov_buf_idx + offset) % buf_size; - self.cov_buf[idx] += 1; - } - } - - /// Flush the entire circular buffer and reset tracking state. - pub fn flush_cov_buf_all(&mut self) { - for slot in self.cov_buf.iter_mut() { - if *slot > 0 { - *self.cov_hist.entry(*slot).or_insert(0) += 1; - *slot = 0; - } - } - self.cov_buf_idx = 0; - self.cov_buf_pos = 0; - self.cov_buf_tid = -1; - } - - /// Merge another accumulator into this one. - pub fn merge(&mut self, mut other: BamStatAccum) { - // Flush any remaining positions in the other's round buffer into its - // cov_hist before merging. Without this, positions still in the - // round buffer would be silently lost during parallel merges. - other.flush_cov_buf_all(); - - // RSeQC bam_stat fields - self.total_records += other.total_records; - self.qc_failed += other.qc_failed; - self.duplicates += other.duplicates; - self.non_primary += other.non_primary; - self.unmapped += other.unmapped; - self.non_unique += other.non_unique; - self.unique += other.unique; - self.read_1 += other.read_1; - self.read_2 += other.read_2; - self.forward += other.forward; - self.reverse += other.reverse; - self.splice += other.splice; - self.non_splice += other.non_splice; - self.proper_pairs += other.proper_pairs; - self.proper_pair_diff_chrom += other.proper_pair_diff_chrom; - - // samtools flagstat fields - self.secondary += other.secondary; - self.supplementary += other.supplementary; - self.mapped += other.mapped; - self.paired_flagstat += other.paired_flagstat; - self.read1_flagstat += other.read1_flagstat; - self.read2_flagstat += other.read2_flagstat; - self.first_fragments += other.first_fragments; - self.last_fragments += other.last_fragments; - self.properly_paired += other.properly_paired; - self.both_mapped += other.both_mapped; - self.singletons += other.singletons; - self.mate_diff_chr += other.mate_diff_chr; - self.mate_diff_chr_mapq5 += other.mate_diff_chr_mapq5; - - // samtools idxstats fields - for (tid, (m, u)) in other.chrom_counts { - let entry = self.chrom_counts.entry(tid).or_insert((0, 0)); - entry.0 += m; - entry.1 += u; - } - self.unplaced_unmapped += other.unplaced_unmapped; - - // samtools stats SN fields - self.total_len += other.total_len; - self.total_first_fragment_len += other.total_first_fragment_len; - self.total_last_fragment_len += other.total_last_fragment_len; - self.bases_mapped += other.bases_mapped; - self.bases_mapped_cigar += other.bases_mapped_cigar; - self.bases_duplicated += other.bases_duplicated; - if other.max_len > self.max_len { - self.max_len = other.max_len; - } - if other.max_first_fragment_len > self.max_first_fragment_len { - self.max_first_fragment_len = other.max_first_fragment_len; - } - if other.max_last_fragment_len > self.max_last_fragment_len { - self.max_last_fragment_len = other.max_last_fragment_len; - } - self.quality_sum += other.quality_sum; - self.quality_count += other.quality_count; - self.mismatches += other.mismatches; - for (isize_val, counts) in other.is_hist { - let entry = self.is_hist.entry(isize_val).or_insert([0; 4]); - for i in 0..4 { - entry[i] += counts[i]; - } - } - self.inward_pairs += other.inward_pairs; - self.outward_pairs += other.outward_pairs; - self.other_orientation += other.other_orientation; - self.primary_count += other.primary_count; - self.primary_mapped += other.primary_mapped; - self.primary_duplicates += other.primary_duplicates; - self.reads_mq0 += other.reads_mq0; - self.reads_mapped_and_paired += other.reads_mapped_and_paired; - - // Histogram/distribution fields - for (len, count) in other.rl_hist { - *self.rl_hist.entry(len).or_insert(0) += count; - } - for (len, count) in other.frl_hist { - *self.frl_hist.entry(len).or_insert(0) += count; - } - for (len, count) in other.lrl_hist { - *self.lrl_hist.entry(len).or_insert(0) += count; - } - for i in 0..256 { - self.mapq_hist[i] += other.mapq_hist[i]; - } - - // Per-cycle quality arrays (FFQ/LFQ) - merge_vec_arrays(&mut self.ffq, other.ffq); - merge_vec_arrays(&mut self.lfq, other.lfq); - - // GC content distributions (200 bins) - for i in 0..200 { - self.gcf[i] += other.gcf[i]; - self.gcl[i] += other.gcl[i]; - } - - // Per-cycle base composition (FBC/LBC and read-oriented) - merge_vec_arrays(&mut self.fbc, other.fbc); - merge_vec_arrays(&mut self.lbc, other.lbc); - merge_vec_arrays(&mut self.fbc_ro, other.fbc_ro); - merge_vec_arrays(&mut self.lbc_ro, other.lbc_ro); - merge_vec_arrays(&mut self.gcc_rc, other.gcc_rc); - - // Total base counters - for i in 0..5 { - self.ftc[i] += other.ftc[i]; - self.ltc[i] += other.ltc[i]; - } - - // Indel distribution - for (len, counts) in other.id_hist { - let entry = self.id_hist.entry(len).or_insert([0; 2]); - entry[0] += counts[0]; - entry[1] += counts[1]; - } - - // Indels per cycle - merge_vec_arrays(&mut self.ic, other.ic); - - // CHK checksums (wrapping u32 addition) - for i in 0..3 { - self.chk[i] = self.chk[i].wrapping_add(other.chk[i]); - } - - // COV histogram (additive merge) - for (depth, count) in other.cov_hist { - *self.cov_hist.entry(depth).or_insert(0) += count; - } - - // GCD bins (concatenate — bins from different chromosome workers - // are independent and will be sorted during output). - self.gcd_bins.append(&mut other.gcd_bins); - } -} - // ------------------------------------------------------------------- // infer_experiment accumulator // ------------------------------------------------------------------- @@ -2327,112 +1132,10 @@ fn point_in(region_map: &HashMap, chrom: &str, point: u6 region_map.get(chrom).is_some_and(|ci| ci.contains(point)) } -// =================================================================== -// Merge helpers for Vec<[u64; N]> per-cycle arrays -// =================================================================== - -/// Merge two `Vec<[u64; N]>` arrays element-wise, extending target if shorter. -fn merge_vec_arrays(target: &mut Vec<[u64; N]>, source: Vec<[u64; N]>) { - if source.len() > target.len() { - target.resize(source.len(), [0u64; N]); - } - for (i, arr) in source.into_iter().enumerate() { - for j in 0..N { - target[i][j] += arr[j]; - } - } -} - // =================================================================== // Converter methods: accumulator → result types for output functions // =================================================================== -impl BamStatAccum { - /// Convert accumulated counters into a `BamStatResult` for output. - pub fn into_result(mut self) -> BamStatResult { - // Flush remaining positions in the coverage round buffer - self.flush_cov_buf_all(); - BamStatResult { - // RSeQC bam_stat fields - total_records: self.total_records, - qc_failed: self.qc_failed, - duplicates: self.duplicates, - non_primary: self.non_primary, - unmapped: self.unmapped, - non_unique: self.non_unique, - unique: self.unique, - read_1: self.read_1, - read_2: self.read_2, - forward: self.forward, - reverse: self.reverse, - splice: self.splice, - non_splice: self.non_splice, - proper_pairs: self.proper_pairs, - proper_pair_diff_chrom: self.proper_pair_diff_chrom, - // samtools flagstat fields - secondary: self.secondary, - supplementary: self.supplementary, - mapped: self.mapped, - paired_flagstat: self.paired_flagstat, - read1_flagstat: self.read1_flagstat, - read2_flagstat: self.read2_flagstat, - first_fragments: self.first_fragments, - last_fragments: self.last_fragments, - properly_paired: self.properly_paired, - both_mapped: self.both_mapped, - singletons: self.singletons, - mate_diff_chr: self.mate_diff_chr, - mate_diff_chr_mapq5: self.mate_diff_chr_mapq5, - // samtools idxstats fields - chrom_counts: self.chrom_counts, - unplaced_unmapped: self.unplaced_unmapped, - // samtools stats SN fields - total_len: self.total_len, - total_first_fragment_len: self.total_first_fragment_len, - total_last_fragment_len: self.total_last_fragment_len, - bases_mapped: self.bases_mapped, - bases_mapped_cigar: self.bases_mapped_cigar, - bases_duplicated: self.bases_duplicated, - max_len: self.max_len, - max_first_fragment_len: self.max_first_fragment_len, - max_last_fragment_len: self.max_last_fragment_len, - quality_sum: self.quality_sum, - quality_count: self.quality_count, - mismatches: self.mismatches, - is_hist: self.is_hist, - inward_pairs: self.inward_pairs, - outward_pairs: self.outward_pairs, - other_orientation: self.other_orientation, - primary_count: self.primary_count, - primary_mapped: self.primary_mapped, - primary_duplicates: self.primary_duplicates, - reads_mq0: self.reads_mq0, - reads_mapped_and_paired: self.reads_mapped_and_paired, - // Histogram/distribution fields - rl_hist: self.rl_hist, - frl_hist: self.frl_hist, - lrl_hist: self.lrl_hist, - mapq_hist: self.mapq_hist, - ffq: self.ffq, - lfq: self.lfq, - gcf: self.gcf, - gcl: self.gcl, - fbc: self.fbc, - lbc: self.lbc, - fbc_ro: self.fbc_ro, - lbc_ro: self.lbc_ro, - gcc_rc: self.gcc_rc, - ftc: self.ftc, - ltc: self.ltc, - id_hist: self.id_hist, - ic: self.ic, - chk: self.chk, - cov_hist: self.cov_hist, - gcd_bins: self.gcd_bins, - } - } -} - impl InferExpAccum { /// Convert accumulated strand counts into an `InferExperimentResult`. pub fn into_result(self) -> InferExperimentResult { diff --git a/src/rna/rseqc/mod.rs b/src/rna/rseqc/mod.rs index a4730ccb..6f38ecca 100644 --- a/src/rna/rseqc/mod.rs +++ b/src/rna/rseqc/mod.rs @@ -7,14 +7,34 @@ pub mod accumulators; pub mod common; pub mod plots; -pub mod bam_stat; -pub mod flagstat; -pub mod idxstats; pub mod infer_experiment; pub mod inner_distance; pub mod junction_annotation; pub mod junction_saturation; pub mod read_distribution; pub mod read_duplication; -pub mod stats; pub mod tin; + +// bam_stat and the samtools writers are read-level and assay-agnostic; they +// now live in `crate::common`. Re-exported so existing +// `crate::rna::rseqc::...` paths and the published 0.2.x library surface +// keep resolving. Drop the shims at 1.0. +pub use crate::common::bam_stat; +pub use crate::common::samtools::{flagstat, idxstats, stats}; + +#[cfg(test)] +mod compat_tests { + //! Guards the re-export shims that keep the published 0.2.x paths alive. + //! These are compile-time assertions; there is nothing to observe at runtime. + + #[test] + fn moved_modules_are_still_reachable_from_their_old_paths() { + let _: fn( + &crate::rna::rseqc::bam_stat::BamStatResult, + &std::path::Path, + ) -> anyhow::Result<()> = crate::rna::rseqc::flagstat::write_flagstat; + let _ = crate::rna::rseqc::accumulators::BamStatAccum::default(); + let _: u16 = crate::rna::bam_flags::BAM_FDUP; + let _: Option<&crate::rna::preseq::PreseqAccum> = None; + } +} diff --git a/src/summary.rs b/src/summary.rs index 2f1320a6..05f18254 100644 --- a/src/summary.rs +++ b/src/summary.rs @@ -44,6 +44,11 @@ pub struct InputSummary { /// dupRadar summary (if successful and enabled). #[serde(skip_serializing_if = "Option::is_none")] pub dupradar: Option, + /// DNA depth-of-coverage summary (if this was a `dna` run). + /// + /// An input carries either the RNA fields above or this one, never both. + #[serde(skip_serializing_if = "Option::is_none")] + pub dna: Option, /// List of output files written. pub outputs: Vec, } @@ -90,6 +95,39 @@ pub struct DupradarSummary { pub slope: Option, } +/// Depth of coverage summary for a single alignment file. +#[derive(Debug, Serialize)] +pub struct DnaSummary { + /// Total reference bases across all contigs. + pub genome_length: u64, + /// Sum of per-base depth, that is total bases covered. + pub covered_bases: u64, + /// Mean depth across the reference. + pub mean_coverage: f64, + /// Median per-base depth. + pub median_coverage: u32, + /// Highest per-base depth seen. + pub max_coverage: u32, + /// Percentage of reference bases at or above each requested threshold, + /// in the order the thresholds were requested. + pub coverage_thresholds: Vec, + /// Total records seen. + pub total_reads: u64, + /// Duplicate-flagged records. + pub duplicates: u64, + /// Duplicate rate as a percentage of total records. + pub duplicate_pct: f64, +} + +/// Percentage of the reference covered at or above one depth threshold. +#[derive(Debug, Serialize)] +pub struct CoverageThreshold { + /// The threshold itself, in reads (for example 10 for 10X). + pub threshold: u32, + /// Percentage of reference bases at or above it. + pub pct_bases: f64, +} + /// A single output file written during processing. #[derive(Debug, Serialize)] pub struct OutputFile { diff --git a/tests/create_dna_test_data.sh b/tests/create_dna_test_data.sh new file mode 100755 index 00000000..d9762868 --- /dev/null +++ b/tests/create_dna_test_data.sh @@ -0,0 +1,141 @@ +#!/usr/bin/env bash +# Regenerate the DNA test inputs and the reference outputs they are compared against. +# +# Inputs come from nf-core/test-datasets (a real human chr22 slice, 40 kb). +# The upstream BAM is not duplicate-marked, so this script marks duplicates +# with samtools; RustQC requires duplicate-marked input. +# +# The reference outputs are produced by the pinned tool versions recorded in +# tests/expected/dna/VERSIONS.txt. Regenerating with a different version will +# make the parity tests fail, which is the intended behaviour: fixtures and +# tool versions travel together. +set -euo pipefail + +MOSDEPTH_VERSION="0.3.14" +PICARD_VERSION="3.4.0" +QUALIMAP_VERSION="2.3" +SAMTOOLS_VERSION="1.24" + +here="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +data="$here/data/dna" +expected="$here/expected/dna" +base="https://raw.githubusercontent.com/nf-core/test-datasets/modules/data/genomics/homo_sapiens" + +have() { command -v "$1" >/dev/null || { echo "missing tool: $1" >&2; exit 1; }; } +have samtools; have mosdepth; have curl; have java; have unzip + +check_version() { + local tool="$1" want="$2" got + got="$($tool --version 2>&1 | head -1 | grep -oE '[0-9]+\.[0-9]+(\.[0-9]+)?' | head -1)" + if [[ "$got" != "$want" ]]; then + echo "$tool version $got does not match the pinned $want" >&2 + echo "Install the pinned version, or update VERSIONS.txt and the fixtures together." >&2 + exit 1 + fi +} +check_version samtools "$SAMTOOLS_VERSION" +check_version mosdepth "$MOSDEPTH_VERSION" + +mkdir -p "$data" "$expected" +tmp="$(mktemp -d)" +trap 'rm -rf "$tmp"' EXIT + +curl -sSfL -o "$tmp/upstream.bam" "$base/illumina/bam/test.paired_end.sorted.bam" +curl -sSfL -o "$data/genome.fasta" "$base/genome/genome.fasta" +curl -sSfL -o "$data/genome.fasta.fai" "$base/genome/genome.fasta.fai" +curl -sSfL -o "$data/targets.bed" "$base/genome/genome.multi_intervals.bed" + +# Mark duplicates: name-sort, add mate tags, coordinate-sort, then markdup. +samtools sort -n -o "$tmp/ns.bam" "$tmp/upstream.bam" +samtools fixmate -m "$tmp/ns.bam" "$tmp/fm.bam" +samtools sort -o "$tmp/cs.bam" "$tmp/fm.bam" +samtools markdup -S "$tmp/cs.bam" "$data/test.dna.bam" +samtools index "$data/test.dna.bam" + +mosdepth --by 500 --thresholds 1,5,10,15,20,30,50 "$expected/test" "$data/test.dna.bam" + +# Picard is a jar rather than a command, so it is fetched by version instead of +# version-checked. The JVM locale is pinned: a French default locale writes +# "3,531312" where an English one writes "3.531312", which would make the +# fixtures depend on the machine that produced them. +picard_jar="$tmp/picard-$PICARD_VERSION.jar" +curl -sSfL -o "$picard_jar" \ + "https://github.com/broadinstitute/picard/releases/download/$PICARD_VERSION/picard.jar" +picard() { + java -Duser.language=en -Duser.country=US -jar "$picard_jar" "$@" 2>/dev/null +} + +picard CollectWgsMetrics \ + -I "$data/test.dna.bam" \ + -O "$expected/test.wgs_metrics.txt" \ + -R "$data/genome.fasta" + +picard CollectInsertSizeMetrics \ + -I "$data/test.dna.bam" \ + -O "$expected/test.insert_size_metrics.txt" \ + -H "$tmp/insert_size_histogram.pdf" + +# The chart output needs R, so it goes to the scratch directory and is not +# compared against; only the two metrics tables are fixtures. +# Picard consumes interval lists rather than BED, so the targets are converted +# with Picard's own tool. The two conventions differ: BED is zero-based +# half-open, an interval list one-based inclusive. +picard CreateSequenceDictionary -R "$data/genome.fasta" -O "$tmp/genome.dict" +picard BedToIntervalList \ + -I "$data/targets.bed" \ + -O "$tmp/targets.interval_list" \ + -SD "$tmp/genome.dict" + +picard CollectHsMetrics \ + -I "$data/test.dna.bam" \ + -O "$expected/test.hs_metrics.txt" \ + -R "$data/genome.fasta" \ + -BI "$tmp/targets.interval_list" \ + -TI "$tmp/targets.interval_list" + +picard CollectGcBiasMetrics \ + -I "$data/test.dna.bam" \ + -O "$expected/test.gc_bias.detail_metrics.txt" \ + -S "$expected/test.gc_bias.summary_metrics.txt" \ + -CHART "$tmp/gc_bias.pdf" \ + -R "$data/genome.fasta" + +# Picard stamps a start time and the full command line, absolute paths and all, +# into the first four lines of every metrics file. Those are dropped: they would +# change on every regeneration and say nothing about the numbers. The +# "## METRICS CLASS" and "## HISTOGRAM" markers further down are part of the +# format and are kept. +for f in "$expected/test.wgs_metrics.txt" "$expected/test.insert_size_metrics.txt" \ + "$expected/test.gc_bias.detail_metrics.txt" "$expected/test.gc_bias.summary_metrics.txt" \ + "$expected/test.hs_metrics.txt"; do + sed -e '/^## htsjdk\.samtools\.metrics\.StringHeader$/d' -e '/^# /d' "$f" \ + | sed -e '/./,$!d' > "$f.tmp" && mv "$f.tmp" "$f" +done +samtools stats "$data/test.dna.bam" > "$expected/test.stats.txt" +samtools flagstat "$data/test.dna.bam" > "$expected/test.flagstat.txt" +samtools idxstats "$data/test.dna.bam" > "$expected/test.idxstats.txt" + +# Qualimap ships as a zip rather than a single jar, and its launcher passes +# -XX:MaxPermSize, which modern JVMs reject, so the main class is invoked +# directly. The locale is pinned for the same reason as Picard's. +curl -sSfL -o "$tmp/qualimap.zip" \ + "https://bitbucket.org/kokonech/qualimap/downloads/qualimap_v$QUALIMAP_VERSION.zip" +unzip -q -o "$tmp/qualimap.zip" -d "$tmp" +qm_dir="$tmp/qualimap_v$QUALIMAP_VERSION" +java -Duser.language=en -Duser.country=US -Xmx2G \ + -cp "$qm_dir/qualimap.jar:$qm_dir/lib/*" \ + org.bioinfo.ngs.qc.qualimap.main.NgsSmartMain bamqc \ + -bam "$data/test.dna.bam" -outdir "$tmp/qualimap" -nt 1 >/dev/null 2>&1 + +mkdir -p "$expected/qualimap" +# The Input section records the absolute paths it was run with, which would +# make the fixture depend on the machine that produced it. +grep -v "bam file =\|outfile =" "$tmp/qualimap/genome_results.txt" \ + > "$expected/qualimap/genome_results.txt" +cp -R "$tmp/qualimap/raw_data_qualimapReport" "$expected/qualimap/" + +printf 'mosdepth\t%s\nsamtools\t%s\npicard\t%s\nqualimap\t%s\n' \ + "$MOSDEPTH_VERSION" "$SAMTOOLS_VERSION" "$PICARD_VERSION" "$QUALIMAP_VERSION" \ + > "$expected/VERSIONS.txt" + +echo "Regenerated $(find "$data" "$expected" -type f | wc -l | tr -d ' ') files." diff --git a/tests/create_protein_test_data.sh b/tests/create_protein_test_data.sh new file mode 100755 index 00000000..359d762d --- /dev/null +++ b/tests/create_protein_test_data.sh @@ -0,0 +1,111 @@ +#!/usr/bin/env bash +# Regenerate the protein test inputs and the reference outputs they are +# compared against. +# +# Inputs come from nf-core/test-datasets: two small real protein FASTA files. +# The reference statistics come from seqkit, pinned to the version recorded in +# tests/expected/protein/VERSIONS.txt. Regenerating with a different version +# will make the parity tests fail, which is the intended behaviour. +set -euo pipefail + +SEQKIT_VERSION="2.13.0" +PYTEOMICS_VERSION="5.0.1" + +here="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +data="$here/data/protein" +expected="$here/expected/protein" +base="https://raw.githubusercontent.com/nf-core/test-datasets/modules/data/proteomics/database" + +have() { command -v "$1" >/dev/null || { echo "missing tool: $1" >&2; exit 1; }; } +have seqkit; have curl; have python3 + +got="$(seqkit version 2>&1 | grep -oE '[0-9]+\.[0-9]+\.[0-9]+' | head -1)" +if [[ "$got" != "$SEQKIT_VERSION" ]]; then + echo "seqkit version $got does not match the pinned $SEQKIT_VERSION" >&2 + echo "Install the pinned version, or update VERSIONS.txt and the fixtures together." >&2 + exit 1 +fi + +mkdir -p "$data" "$expected" +tmp="$(mktemp -d)" +trap 'rm -rf "$tmp"' EXIT +curl -sSfL -o "$data/yeast_UPS_mini.fasta" "$base/yeast_UPS_mini.fasta" +curl -sSfL -o "$data/protein_mini_with_cazymes.faa" "$base/protein_mini_with_cazymes.faa" + +# -T gives tab-separated output, which is what RustQC reproduces. The file +# column holds the path it was run with, so it is rewritten to the bare +# basename to keep the fixture independent of where it was generated. +for f in yeast_UPS_mini.fasta protein_mini_with_cazymes.faa; do + seqkit stats -a -T "$data/$f" \ + | awk -v n="$f" 'BEGIN{FS=OFS="\t"} NR==1{print; next} {$1=n; print}' \ + > "$expected/${f%.*}.seqkit.tsv" +done + +# The mzML fixture comes from mzdata's own test data: 48 spectra, 14 MS1 and +# 34 MS2, which is enough to exercise every metric. nf-core's proteomics +# fixtures are either a single profile scan or far past the size budget. +curl -sSfL -o "$data/small.mzML" \ + "https://raw.githubusercontent.com/mobiusklein/mzdata/main/test/data/small.mzML" + +# The reference figures come from pyteomics, installed into a throwaway +# environment so the host's Python is left alone. Total ion current is summed +# from the peak intensities rather than read from the header, matching what +# RustQC reports: the two differ when a profile spectrum has been centroided. +python3 -m venv "$tmp/venv" +"$tmp/venv/bin/pip" install --quiet "pyteomics==$PYTEOMICS_VERSION" numpy psims lxml +"$tmp/venv/bin/python" - "$data/small.mzML" "$expected/small.pyteomics.tsv" <<'PYEOF' +import collections, sys +from pyteomics import mzml + +source, destination = sys.argv[1], sys.argv[2] +levels = collections.Counter(); peaks = collections.Counter() +tic = collections.Counter(); lo = {}; hi = {} +rts = []; charges = collections.Counter(); nocharge = 0; nprecursors = 0; mzs = [] + +with mzml.read(source) as reader: + for spectrum in reader: + level = int(spectrum["ms level"]) + intensity = spectrum.get("intensity array") + n = len(intensity) if intensity is not None else 0 + levels[level] += 1 + peaks[level] += n + tic[level] += float(intensity.sum()) if n else 0.0 + lo[level] = n if level not in lo else min(lo[level], n) + hi[level] = n if level not in hi else max(hi[level], n) + start = spectrum.get("scanList", {}).get("scan", [{}])[0].get("scan start time") + if start is not None: + rts.append(float(start)) + for precursor in spectrum.get("precursorList", {}).get("precursor", []): + for ion in precursor.get("selectedIonList", {}).get("selectedIon", []): + nprecursors += 1 + charge = ion.get("charge state") + if charge is None: + nocharge += 1 + else: + charges[int(charge)] += 1 + mz = ion.get("selected ion m/z") + if mz is not None: + mzs.append(float(mz)) + +rows = ["metric\tvalue"] +rows.append(f"spectra\t{sum(levels.values())}") +rows.append(f"peaks\t{sum(peaks.values())}") +rows.append(f"rt_min\t{min(rts):.6f}") +rows.append(f"rt_max\t{max(rts):.6f}") +rows.append(f"precursors\t{nprecursors}") +rows.append(f"precursors_without_charge\t{nocharge}") +if mzs: + rows.append(f"precursor_mz_min\t{min(mzs):.4f}") + rows.append(f"precursor_mz_max\t{max(mzs):.4f}") +for level in sorted(levels): + rows.append(f"ms{level}_spectra\t{levels[level]}") + rows.append(f"ms{level}_peaks\t{peaks[level]}") + rows.append(f"ms{level}_min_peaks\t{lo[level]}") + rows.append(f"ms{level}_max_peaks\t{hi[level]}") + rows.append(f"ms{level}_total_ion_current\t{tic[level]:.4f}") +open(destination, "w").write("\n".join(rows) + "\n") +PYEOF + +printf 'seqkit\t%s\npyteomics\t%s\n' "$SEQKIT_VERSION" "$PYTEOMICS_VERSION" > "$expected/VERSIONS.txt" + +echo "Regenerated $(find "$data" "$expected" -type f | wc -l | tr -d ' ') files." diff --git a/tests/data/dna/genome.fasta b/tests/data/dna/genome.fasta new file mode 100644 index 00000000..b0ea69be --- /dev/null +++ b/tests/data/dna/genome.fasta @@ -0,0 +1,668 @@ +>chr22 +ACTCAAGATAATGATGAGTAAAGAATATATTTCTAACAACAAAAAGGAAATTTGATAGTA +TTTCTAAAGACAAAAAGGAAATTTGTATTCACATTCAGTTAGTCATTCCACCAGAATGAC +TTCATCACACAATATTTTGTGACAAGAACCTGAACAGCCTCATGTTTTACAATATTCTTT +TCATCTTTTATTATATGCACCAAAATTTTCTTTTTTAAATTTTCTTGAACCTCTAAATCT +ACTTTAAAAATTTACCTGATACACTTTTTAAATGGACAAATGCTGAAGGTAGCTGTGTAT +ACAAATGTGACTAGAAGGAAAAAGATGATGTAGAAATACAATAACTCCTTGAGTTGATCA +TTCTGATTGGCATTTATAGAGTAGAAATGTTTTGTAATTACAGAGGAAAAAAGATGGCCT +TTCCTTCAACAGTTATGAGCCGTCAGAATTTTCAAAAATATTGCATTTTGACAATGTAGT +TTCTAGTTTGACAATGATATATTTATCTTCAAAACCAGGAAAATGTAGATAAGGATTTGG +TTTTATAATATTTAAATTCTTATTAAAATGTATAATAAAATTGTTTTCCCCATCACTTTA +TTCTTCTGTAAGTTATTTTACGTTTAAAATGTAAACAAATAAAAATAAGTAAATAAACAG +TAGCAGCTTCTTTTCCTGGTGAATCGAGGATTGAGTATGTATTATATCTTTCCTGGACTA +TTGGAATAACCTCTCCCTCCTTCCACAGAGAAGCCATAATAATCTTTATGAAATACAAAT +CAAATCATGGTATTCATTCTTTAAATAGTTATCAATAAAAATAAAATCCCAACTTTATAC +CCTGTTCTGCAAATTTTAACGTGGTCTGAATTCAGCTTACATTTCTTCTTTCCCTTGTCT +ATTGCCCATCAGGCTCACTGGCCTTATTCCTTCACACCAAACTAGTTATTTCCGGGGTGG +GAGGAAGGCTTGCAGTGTTTTCTCCATCTGCAATAGTCTTTCCCAAATCTTAGTGTGGAT +AAAGTTTCCTTCTTGTTACTTGAATCACAAATACTATGTTCTTAGTCATTCTCTGTTACA +TCATCCAGAGTACATTATATCAATTTTCCAATATTTTTATTTATTTGATTTCCCACTATA +ACAGAGGCTCTGTTAGTGCAGGGTCTTTTACTCTTTTGTAATCCCAACAGCAAGAACAAA +ACAAGGTACATAGTACATATTTAATAAATACCTGTTGAACAAATATGTGCCAGTAATATT +TCTTCATGCTGCTGAATAAGTTAACAGCATATAAACACATACAAACCAAGTGGCATGGAT +GTCTGCTTTGATTTTTAGCCATTTAAAAATATACGTAACCCATCCTAAGGGGTTTATATT +TGTTTTGCATAATACATTAATATGTACTCATTATTCATTACACAGTTAATATATCTATAT +TTGCAGGGAATATACATTGCTTGGAATTATACAAAAAAATATTATTTTTCGTTTTCTAAT +ATTCAGGATACAGTGTTTTAATGGGGGTGTTTCTTCATTCTTTTTTTCTTACTGGTTTTT +ACTTTTTAAATTTGAAAGCCTTGCAGTGATCATAAGGATCTGTTCAGGCAAAGAACATGA +AAGAGTTTAAATTTTTATCATTTTAGTGTTTCTTATTCTCTATATCAAAAACATTCACAG +GTAAGTTAACAAGATCCTCATCAGGAGGAAAAGTAAATTGTTCACTACCATCCTCTAGTA +TCCTAATCTGGTCTTGTTGTTGGCTAACTTCAGCAGTTACTATTCTGTGATTGGTGTAAT +ATTAACCAAATAAATTACTGGATTTGTTCCACAAATATTATATCTTAGATTGGTTCTTTC +CTGTCTCTGAAAATAAAGTCTTGCAATGAGAATAAATTATTTTACAACAGTTAATTAGCA +ATGTAAAGTTTATTGAAAATGTATTTGCTTTTTTTGTAAATCATCTGTGAATCCAGAGGG +GAAAAATATGACAAAGAAAGCTATATAAGATATTATTTTATTTTACAGAGTAACAGACTA +GCTAGAGACAATGAATTAAGGGAAAATGACAAAGAACAGCTCAAAGCAATTTCTACACGA +GATCCTCTCTCTGAAATCACTGCGCAGGAGAAAGATTTTCTATGGACCACAGGTAAGTGC +TAAAATGGAGATTCTCTGTTTCTTTTTCTTTATTACAGAAAAAATAACTGACTTTGGCTG +ATCTCAGCATGTTTTTACCATACCTATTAGAATAAATGAAGCAGAATTTACATGATTTTT +AAACTATAAACATTGCCTTTTTAAAAACAATGGCTGTAAATTGATATTTGTAGAAAATCA +TACTACATTTGTAGTTGGCACATTAAATGCTTTTTCTTACTCTGAATTCCTGATATGACT +TTCTTTAGGATTGTTTAAAATATTCTAGTAGTTTTAGGTCAATTTAGATGTGATTTAGTT +GCTCTAGATATTATAATTTTTAGGGGTTCCCTTTCATTTTTTTCTTACGTTTCTTCAAAT +AGTATAATGCCTTATTTTCATTTATGAAGAAATTACCCTGCTGTTGGTGATACGGGTATA +TTTAAATAAACCAGTTGCAGTGCATTTTTGCAGAAAGTCCATTAAGACATAAATTTTGTC +CAGTAACCACAGTAGAAGTGGTGACTCTATGATTCATTCATGTTGCATAAGTAGGTGAAA +AATATGAGCTATATTCTGTCTGTTAAATGGAATTCTAGAGATGAAGTAGCCCAGGTAAAT +GTATGTTTGAGATTACTAGATAACTGTTGTACAAATTGGTATGTCACTTAAATTGTTTTC +TCTCAGAAAGTCCACATAAATAAATGAAATAGACTAATAATAGTAATATGGTGTAGAAAA +AACTCCCTTAACATTATTTCCATAGATAAAACTAATTAGAACTGTAAATTCTAAGGAGAT +TATTTATCTAAACTAATTTTAAAATCAGAAGTTAAGGCAGTGTTTTAGATGGCTCATTCA +CAACTATCTTTCCCCTTTAAATATGATTTATTGTCTTTCTCATACACAGATGTATTGCTT +GGTAAAAGATTGGCCTCCAATCAAACCTGAACAGGCTATGGAACTTCTGGACTGTAATTA +CCCAGATCCTATGGTTCGAAGTTTTGCTGTTCAGTGCTTGGAAAAATATTTAACAGATAA +CAAACTTTCTCAGTATTTAATTCAGCTAGTACAGGTAAAATAATGTAAAATAGTGAATAA +TGTTTAATTACAATAATAATTTATTTTAGATCCATACAACTTCCTTTTAAAAAACCTACT +GCACTAACTAGTTTTATGCTTAAAAAAAATTATTACCAGTAATATCCACTTTCTTTCTGA +AAAAATTTTCTTTAGATCGGCCATGCAGAAACTGAACCTGATTTGTTTTTTTTGAATCAC +CTAGGTCCTAAAATATGAACAATATTTGGATAACTTGCTTGTGAGATTTTTACTGAAGAA +AGCATTGACTAATCAAAGGATTGGGCACTTTTTCTTTTGGCATTTAAAGTAAGTCTAATT +ATTTTCCCATTAAATTCTTAAGGTACATATTACTTGCTTTCTTAATAGATTTATAAATAT +GTATTACTTATATACTTTTGTTTATGTTTGGCTGGAAGAGTTTTCCATACTAAAACTATT +TTGTACCAGTGATGAGCTTCTCAACTTTTGCTCTTTGAAATTTAAAAAGTAATAAATTCA +AAACTAAATTTCAGTCATGAATGAGAGCTTAAATATTTTTAAAGATTTTTGTTCTACTTA +AGTAAAATTTTCTAGGTCCAGATGAATATTGCTGTAGGTTTCACTGTGTGTATGGATTAA +AATATCCCCAAAAAAAGAAAAAAAATGTTTTACCTTGAGATTCAGAACAATAATGTCAAA +CTCCCGTGGTTCTTACTGAAAAACAAGCTAATTAAGAATAAAAAATGTTTTGTAGAATGT +GATATATGCAGTACTCAAAAGTTACAGGTCATAAACCATATAACTTTTCATAAATTTAGA +AACAGATTTATATCTAATATGATATTTTAAGTGTTAAAATTTAAAAATGGAACCCAGAAG +TTAAGTTGAAAACAAGAAGCGTAGACGTGTGTCAGAAGAGTCAAACAGCATTCACTGAGC +GCTTTGTTCCCTCCCTCTTCATTTGATTATTTTTGTGCTCAATTTCCTTTTTTCATGCTT +TTATATCTTGTACTGAGATTAGTCAATGAAAACTAGTTGAAATAAACCTAAAAACTAGAT +GTTTATTTAATCACATATTCAGGAACTACCTGAAACTCATGGTGGTTTTGCTTCTAAATT +ACAGGTTTTGAATAATGTTATTATTAGTATGATTGTAACATTTATTGGATTTCAAAAATG +AGTGTTTAAATTGTTTAGCAAAGATTATTTGTATACTGATTTAAGACTATATATATATTT +TTCTAATTTTGCATGATTCTTTTAGATCTGAGATGCACAATAAAACACTTAGCCAGAGGT +TTGGCCTGCTTTTGGAGTCCTATTGTCGTGCATGTGGGATGTATTTGAAGCACCTGAATA +GGCAAGTCGAGGCAATGGAAAAGCTCATTAACTTAACTGACATTGTCAAACAGGAGAAGA +AGGATGAAACACAAAAGTTGTGTGACTCTAGTCTGTGTTTGAGACTCTTTTCACTGCAGT +GGGGCAGAGTTGTTTAGAAGCCCAGTGTATATACAGATCATGGTCCTTGGAATCAAGCAG +ATTAGGATTTGGAACCAAGTTCCACTGCCTCTCATCTGTGTAGTGTTAGACACGTTATGC +AGGCTCTCAAGACTCATTTTCTTTGTCTGTAAAATGGGAATAATACCTGCTTCGTAAGGC +CATTGTGAGAATTAAATTACATGAGATATGCAAAGAACCTATCACAATCCTTGGAACACA +GAAGGTGCCCAATAAATGTTAGATCCCTTTACTTTCCCTTCCTTTCTCTTATTCAGGTCC +CTAAGTATTTACAGTGATTATTTCCTTATTCTGTCATTTATTATCTCTCAGTAATGACCC +TGAAAATGAGTGGAAAGAAGTTAGTTTTTACATTTCCAAGTTTAAAATGGATTTCGAGTC +ACTCAGTAAATATATCACACCCTCTAGTCATCTGCTGTCTAGCTTAGTGTAACTAAGAGT +AGGAAATACAATGTAAACTTTTTTTTTTGAGACAGGGTCTGGCTCTTTTGCCCGGCCTGG +AATGCAGTGGTGCAATTTCGGCTCACTGCAGCCTTGACCTCCTGGGTTCAAGCCATCCTC +CCACCTCAGCCTCCTGAGTAGCTAGGACTATAGGAGCATGCCACCACTCCCAGCTAATTT +TTGTATTTTTAGTAGAGACAGTGTTCTATTCTGCTTTATATTAAAAGCCCCTTAGAAAAT +GGGAACCTGGTGAATATATAATGAATTGTAAAATATTTTAATGTGTAACTTTTTCAACTG +TGAAACTGACTACTGATTTTTTGATGAAAACAGCTGCTGATAAAGTATTTTGTGTAAAGT +GTAGTTCTTATTAATCAGGAAAATGATGACTTGATTAGACTGTATATGCCCTCTTGGATT +TTATTTTAAATGGATTGGTGACTTTCACATAGGTAAAACACAGTCCATCTGTATTCTTTT +TTCCATCAAAAAGCGAGTGATTTAGAATTATAAAAAAATTTGTGAGCAGCCTATTTGAAA +GGCATCATGGAAATTTCACAGCACAATAACATGGATTTGTTTTTTTCTTAATGATGTAAA +TCCGTTTAATTCATATTTTGATCAATAGCCCATGCTTGCCAACTCTGAAGAAATTTAATT +TCCAGCAGTATTTTAAAGCTAGCCTGTTAACTTTTTCTGAATATTTAAAGTTCCTCTTTT +TTCTATGTCTGCACAAACTGCAGACCTGGGCTGGACCCACATACTCAAGAGTCCACCTTA +AGAAATTATTTTGATGTCCAAGACATCACTAAAATATTTCAGTTTAAAGATAACATGTGG +TGTTAATAGATTGTGGTGCTTTTACTATTTAAAGACAACTTTCATACTTCAGATGTTTTT +GAGAAGAGGGGAATGTGAGGGGAGGGGGCAGAACAGGGAGGAGTTTGAATGAATTACATT +CTTTATATCCATCCTGCTCATTTGGGGCATGTCTTTAAGAGAAGGCTGAAAGTTGTGAGA +GTATATTGTATACCGTAAGAGAATCAACTCTTCATCATGGATGGGATTGTGAAGGCTGAA +CTGTAAAAGTCAGCATTGACAGCATCCTCAATTAATAATTCTTGGTGACAGAATAATACA +GCTGGGCTGTTTTATAAATATAAACAATACCATTTTTAATTATTACATTAAAAATTTTAA +ATATATCTATGTGCCATGGCCTGGGAAGCCTGTTTTCTATTTTCATAAAAATTATTTTTA +CTGTATGAAAAGATTATGGGGTTTAGCTCAAAATATCTGTGGTCCTGATAAAATTGGATT +GGTAACTCTACCTCAGAAGGAAAATGGGAAAAAAAAATAGATGAGTCACAATTCAATACT +TCAAGCTCAGAAACTGTGCAGATCACTGAATTTTAGATTTATAAAGTCAGAGTTGGCATG +CGTTGTTTTTAATGATATGGAAGACCTTAAGAAAAAAACTTGGCTGAAGTTTAATCGTTG +GTCCAGCCATTTGAAAAAGGCAATAGTTCGAGGAGGTTTCCGAATTCGGCATTTGAAATT +CATTTTGTTCTCTCTTCTTCATTATTAGTGCATTTGGTGTGTGTATACTTGCACACAATT +CTGTTTGTGTACACACTGCTTGCTAAGCCCTAGTCAAGAGGCATCTTTTATAAAAGGTGT +AAAGAAATATCAAGGTTCTAAAATTCGGAAGAGTTTAGAATTTATTAGGAGTTTCCCAAG +TTGGGATGTTAGTCTTTAAATAAACTTCATGCACCTATTCCACTTAAGGTTTTGCACCTC +CTTTTTATTAGTGCAGTGCCATTTCTTCTGCTTGATTTTAGGTATGTTAATATTCCAGCC +TTGCTAGTTAGCATAAAGTGACAGGTGTGAGCCATGAGGAAATTTTCTGACTTAATTTTT +ATACAACTACATATGAGTTTTAGTGGAGAAAAAAAATTAGTCCCTTGTGCATATATAGTA +GTTAGGTAAATGATTTTTCTACCAACAGTGTACTCCATTCCTCATGTAGGTAAGTACAGA +AAAGGTTTTTAAATGTATTTTGTTAGCCAGTTAAAGTCTATGAATCTATCTGCAACCTTA +TTTAATCTGTCACTACAATAATTTTGTGGTTATGCTAAGAACCATGTATACTTTTAGGTA +TTCTTATTTTTGTCAATTTTTCTAGGTTAGCAAGGAGGCAGAAAAGCTTCACTGTTTCAT +ATTAAAATATAATTAGACTAAACTTAATTCTAGTATGAATTTCCAAAATCATTATCTATT +TATTTCATTTTTATTTAATTTTGTTTTTAGTTCATTTTTAAAAGTCCCTTGTTCAATTTA +ATTTATGTTCCTAAGAGTGGTTGGAGAACTTGGCCTTCATCTGATTTCAAAAACATTTTG +AGTTTCAAATGAAGTTAATGGTTTCAGTGTGATTCAGTCCTCAGACCTAATTGGGTTGAA +TAAAATCTAAAAGAATATACCCTTTTGGAGCATAACATTTTAATACCTTGAGGAATGTGG +CACTACCAAAAGAAGACTACTAACACGTCAGATGTTCACCTGGAAGCTTTAACAAGAAAT +TCGAACCACCCTTTTGGCCCCATTAATTGTAGCAAGTTTATTTCTCTATATTTTGTCATT +CAGTGAATTGAAGTCCTGTGGTATACTGCATTCATTAGAAGAAAAACGTTTTTAATGTCC +TTTTAATGATGGCCCAGAAAGCATTTGACACAGCAAGATGCATGTATTATTATATTGAGA +ATACAGAATAATAACAGTATCACTAAATTTAAGACCTCTTCCCAGTCTTGCTGTTCCTAG +CAAGAAGTTTGGCCCGTGACTGCACTTACTGTTTATGCTCATCAGAAACTGTCAATGTCT +GCTTTTCTTTAACTCTGCAGTCTGTAACATCATGCTGTTTATTAAAAAAAAAGAAAAATT +ACTTTGACTTGTGTCCAAACAATCCTTAGTGTACTACATAAGCAAAAAACTGTGATAATT +CTCTTTTGCCATTCCTTTTGAAAAGCAAGCCAGTGTTGCTAAAATCAAAATTTAGCTGAA +TTTGAGTTCTTTTCAGTAATGACTAAGAATACTTGATTGAAAATCTGAAACTATTATACC +TTAAAAGCCAATTTTTCTGCCCCAGTAAAGTGATGAATATTAAAGAAATGTATGTTTAAA +TATTTACTTCCTTTAAGCATAAAGAATTATATGCTTGTATTTTAAGAAATATATGTATGT +ATACATACATATGAATGTATGTATATGCAATAGGTAAGTGGACTTTTTTCCAAGTCATTT +GAAGATCAGAACCTAGAAATGAAGTTAGGCTACAAGCAAACTGGTTTTGCTTTCAGTTCT +CATAAACATTGCAAAAGGTAAGTGTGGGCTTTTCTTTGACCATTAATGCACATAGGCATT +AACAACTTAGTATTTCTGAGCAATTAAGCAAATAATTACTTACATTTTATTTATTTGCCA +AATGGTTTAAATAATTTTGAATTGACTTTGCTCTCCAGGGATAATATCTCTCTTTGCTGG +AATGATTCAGGTAGCTCCTATCTAAATGGAAAACTGTGGTAATTGAAACACACACTTTAC +ATTTTAAATTAGCAGTTTTGAATTTGTTAGGGAAAAAAATCCCAGCAATTGCATATTGTT +AGGTAGAAGTCAAATTTACAAAGAAACGGAATAGAGATGTGCCCTTGAGAAAAGTGTAGA +ATCTCAATGTGCAGATGATTTAAAATGTGCGTGCATATAAAATGTTCATGTGTACTTACA +TACTTTATTACAGAGAAGTCTTTGGTATACAAAATAGTTTACCACAACCTTTTAAACAGC +AGGTTCTGGGCCTTAAATGCGTATCACATTTAGCCAAGAGAACTCGGGTAGGGGCATGGA +AAATGAACTGCAGCTCCCTATCCCTAGCCTCTATACCAGCTGTTCAATGAAAAGTACCAA +GGCTCACTGAATGTTATAACCTAGCAGATTGTTACATAAATGATCTAACATTTTTGAGCA +CCGCTACTGGATGCTAGAAGCTAAGCTAAAGTGTTTCACATGCCCTACTTTGCTTATTCT +ATAAAATAACTGCGTGAAAGAACAGGTTATCCCCATTTTATAGATGAGAAAAGAAAGGTT +TACACAGGTTAGCTTATTTGCCCAAAGTTGTGATTATGGCCTACAAAGTCAAATAAATCC +TACTCTGAGACACATGTTCTTTCCACCATTGCACACTAGAAAGGAAAACACCAAGATTAT +TCATTACTGATCAAGTCAATATTGCTGTATTCAGCTAATTTAGTAATATGTGTCTTGAAA +TTAATTGCTAAAAGGGATTAAACTGACTTAGAATCAGTTTTTTGTTTGATTACATCTACA +TACAAAAGTAGCTTCAAATGTCTCATTCTACTGTCCATAATTTAAGATTTTTGAGTATAA +TACAATTTTAAAGATACTTTGAGGCACTTTGGAAAATCAGACCAAAATCTCTTTTCCACT +CACAGATTCGGCTTAATCAATCTGGAAAGCATTTGTTGAGAGCCTTATGACATCATTTAA +TAACCACGGTTGATTCATTAATTAAAGTACAGACAATTGTTGACTATCCATGTGGGACTT +TTCTATTAGGTTGACGCAAAAATAATTGCGGTTTTTCGCCATTAAAGGTTAACAGCGAAA +ACTGGAATTACTTTTGCACCAGCCTAATACGATGTGGATCATCTGAGATGAATGTTGAAA +TCCAGTATAGCTTCTTCATATTTCTGGCCCATTTTTCCCACCAGAAAGTGCACAAAGTGA +AATGAGCTTATGAAAAGCTTAATTAACTAGAAAAATGTTACTGAAAGAAAAATTACATGG +TACATGACAAGGCTAAATACTAGTAACTCTAAACTTAGTGAATTTTCTAGGCAGCAGCTT +TCCTCTGCTGTCTAGACTGGTAAAGAACAAACTAAGGCCAGGCGCAGTGGCTCATGCCTG +TAATCCCAGCACTTTGGGAGGCTGAGGCGGCCAAATCACCTGAGGTCAGGAGTTCAAGAC +CAGCCTGATCAACATGGTGAAACCCTGTCTACACTAAAAATATAAAAATTAGCTGGGCGT +GGTGGTGCACACCTGTAATCCCAGCTACTTAGGAAGCTGAAGCAGGAGAATTGCTTGAAC +CCAGGAGGCAGAGGTTGCAGTGAGCCAAGATCACGCCACTGTGCTCCAGCCTGGGCTACA +AGAGCAAAACTCCATCTCAAAAAGGAAAAAAAAGAAAAAAACTATAATAAATATGTTAGG +TCCATGTTTTCTTAAGTTTTCTACCGGATTTTTATCTTCGTATAGTGAACGAACTGTTAA +GAACTTTTTTATGAGAAATATTTTAGTATGACTATATTGCATAGAGTTAGGCTGATGGTT +CAGTGTTCAGTAGGTTAGATACCCTCATTGTTTATTTCCATATTGACTGGTTCTAGCTAG +AGCTGAAATTAGGCAAAGAATATCTTGAACTCATTTTGCTATACAGGAAAAAAGTGCTTC +CTTAGCTCATTTGGAAAGAGATTGAGATTAGAAAAGATGGTTAATTTGTATGTATTTATA +GAAATAAATAGAATACAAAATGAGGCTTTTAAATTTTTTCCCACATGAAAATATGATACT +TTAATCATTACGTTTTACATTGTTAGTTTGCAGACAGGCATAATTAGGTCCTCAGTTGCA +GAAATCACAGACATCTGAAGGCCAGCCCTTTAATTTGGCCACCGTCTTAAGATTTCTCTG +CTCCTTCCTTTGCTCCTCCTCCTACTGCACAGTTTGAACTGATGCTGTTCTATATAAGGT +ACTTTTCCACCTACCTCATCTCTGACTACAGTGCTATATTTTTCACACAGTAAGGACAGG +TGTTGTGTTAATCTCACCATGCCAACAATCAGGGCACCACCTAGCAGAGTCAGTGAAGGC +CAAAATAAACAGTGGAAGATAGCCATTTGGTCATACTTTTTTATAAGAATGACATCTTCA +GATTGGCTGGCTGGACTGTAGAAGCATGAAAAGGGGGTTCCATTTTTGTGATCGAAGAAT +TCTTTTATGTCCAGAGCACTGTTGAGCAAATCATTTCTATCTTGGTGGCACTTAGGTGTG +TAAAAGCACTAGGAATATGGAAGAGGGAAAAAGATAAAGGCACTGTCACCAATACCAAAT +ACTTAACAGTTTCTAATTATGAAATAGCTTCAGGCTGAAGTTATTAGTGGGCAGTTTCAA +TCTTAGAAGGTGGTAAAATATTACATAGCTCATGGGAAAGGGTTGATTGGAGGGCCACAG +TGAAATGGCCATTTCCAGTCATTAAGCAAGGATGTGGAAGAGAATTCTTAGTTTATATGA +CATTGCAGGAGAGTCAGTGACCAATTTCATAAGGAATATGACTCCTCCCTACATGCAGGT +TCTTGGACTCTTGGACAGTATGAATCCGTTTGTCCATTGAACAAAAATGTATTGAGCCTT +ACTATGAGCTTTCAACACCTAGTAATGCCTCTGTGGTCTCTGTCTTGATCTCCTGTAGCA +AAATATTACCCTGAAGAAAAGCACGTTGAGGCTTTTGCTCTAGACTCACAGACAGGGAGC +CCCACCTGGACTTTGGTTCCTGGGAGACAGAACCAGTGGAGAAGGGAGCTCTGTCAGCTG +GTGACTTTTTTCAAAAAAGCTTGAGGTTTATTACCATATCCATTAGGTACTTGAGGTACT +GTGCTAAAGGCCTACAAACTGTTTGAAATCTTAAAAATCATTGCATCCAAAATAGAAAAC +AAAAGTCATCAGATTGAAATTGATGCTTAAAGACAATAAAGTGTAACATGTCAACTAATC +TAACACAACTCAACTTTTATAGTTAGGTATAAATATAAATTTTAAATCATATGAAAGACT +ATACTTTCAGGGATCATTTCTATAATTCGTTAAATCATATGAACCCATTGTGTAACTTAT +TAAAATAAAAATAATCTTTACATTTATTTGATAAGAAAAAATTACTCGCTTGATTCAAGG +GAGACTGTGGTACACTGTAGCATATGTTATATGGCGCGGAGTGGAATCTCCAAAAGAAAG +ACTCCCCACAAATGACTACTCATTGGCTCAGCCTATAAATTCCAGACACCAAGTTGTGAA +ATTGGAATAATTTCTCTCCTTTCTATATACCCCATTTCTCCACCAAGAAGAAAGCTTCAT +TTATCCTGATTTGATCACTATAAAAATGTTCACTCCAAAAAAATAGATTTATCCCTAAAG +ACAGCCCTGGGTTATTTATGTACCCTGCTAGGGACAGTCTGGCAGGGAAAGGTTGCTGTC +ATAAGAACTCTTTAAACTTTACAATACCTTGGGATTTATCTGGACAGCCTCTTCATTATA +ATGTAGGAGAGCTTTCTGAGCTGAATGGGTGAGGTTCACAAACACCCGAAGACACGAGTA +CTTCCCGTGACCACGGCAGTGCACACCACAGGTGAAGGCACAGTCCAGCCAGTCGTCCAT +GATATCTGTGTGGATGGCAGTGCAGGTTGATTCTTCTCTCCGAATGCTTCAATTTGAAAA +AAAAAAAAATGTTCTTCACTTACTAGAAAATTTCGTTCTACATTTTGGTGCGGTTATGAG +CTTATGTACACAATTAGCTGGGATTACAGGCGCTCAGCTGCCATGTCCAGCTAATTTTTG +TATTTTTAGTAGAGACAGGGTGTTGGCCAGGCTCGTCTCCAACTCCTGACCTCAAGTGAT +CCACCCACCTTGGCCTCCCAAAGTGCTGGGATTACAGGCATGAGCCACTGCACCTGGCCC +AAATACTATGTTTTATCAATTCTAAAGTGCACTTTAGTATTTACATTTTAATATAACTAA +AATCAATATGTATTTTGCAATCAATGGCATCTTGCTATTATTTGAAAACATTTCTTTAAT +AGTCTGTAAAATAATGGAACATGCCCAGATGCAGTGGCTTATGCCTGTAATCCCAGCACT +TTGAAGGGTCAAGATAGGAGGATCGCTTGAGCCCAGGAGCTGGAGACCAGCCTGGCCAAT +ATAGTGACAGAATAAATAAATAAGTAAATAAAATAATGGAAAATCTCACAAATGGTGATG +TTTTAGGTTCGACAAAATACATTAACTAGCCCATTTAGTTTTCTGAAATTATTTTGATGT +TATTGCTTACAATATTTGTTCTGTGGTACACAACCATAGGATTAATAATATTGATGAAAA +TAATAAAAGAATAATAAGCATGTATTGAGCTCTTCCTGTGTGAAGTTCTGGACAAATCCT +CATAAAGCCTTAAAAGGCAGATACTAGGCTGGGCACGGTGGCTCATGCCTGTAATCCCAG +CACTTTGGGAGGCCGAGGCAGGCAGATCACGCGGTCAGGAGATTGAGACCATCCTGGCTA +ACATGATGAAACACGGTCTCTACTAAAAATACAAAAAATTAGCCAGGCATGGTGGCACGT +GCCTGTAGTCCCAGCTACTCGGGAGGCTGAGGCAGGAAAATCGCTTGAACCTGGGAGGCT +GAGGTTGCAGTGAGCCAAGATCGCACCACTGCTCTCCAGCCTGGGCGACAGAGCAAGACT +CTGTCTTAAAAAAAAAAAAAAAAAAAAGAAAGAAACAGGCAGATACTAGCCCAGGCACGG +TGGCTCATGCCTGTAATCCCACACCTTCGAAGGCCCAGGCGGGTGGATTATCTGAGGTCA +GGAGTTTGAGACCAGCCTGACCAACATTGTGAAACCCTGTCTCTACTAAAAATACAAAAA +TATTAGCCAGGTGTGGTGACAGGTGCCTGTAATTCCAGCTACTCAGGAGGCTAAGGCAGG +AGAATCGCTTGAACCCGGGAGGCGGAGGTTGCAGTGAGCTGAGATTGTGCCACTTTACTC +CAGCCTAGGTGACAGAGGAAGACTCTGTCTCAAAAAAAACAAACAAACAACAACAACAAC +ATCAAAAAGAAACCTATAGTAATAAAATTGAAATAGAAGGAGGTTTGCAATCAAAATGAC +TGACTAGGAATGAAATAGGAAACATAATATTTTGCATCTGCATAGGGAAGTCTGAGATTG +GCTGATCTTGTTCTCTTCTGTAGGGGAAATACTAGTCCAGAACTTGGGGTGCCTGCCAAG +AGGGGAGCAGCCACAGTAGGAAAGGGGGACTCTGGAATGCTAGGGTTCTGGGGTCTGTGG +ACACAGGAGGCAGAGGACATGTGTTAAGATGTTTTAAGAAATGAATGTTGAACTGGATAT +GAAAATATTTTTCAGCCGGGCGCAGTGGCTCACGTCTGTAATCCCAGTACTTTGGGAGGC +TGAGGCGGGTGGATCATGAGGTCAGGAGATCGAGACCATCCTGGCTAACACGGTGAAACC +CCGTCCGTCTCTACTGAAAATACAAAAAGTTAGCCAGGCGTGGTGGCGGAGGCCTGTAAT +CCCAGTTACTCTGGCGGCTGAAGCAGGAGAATGGCGTGAACCTGGGAGACGGAGCTTGCA +GTGAGCCGAGATTGCACCAGTGCACTCTAGCCTGGGCGACAGAGGGAGACTCCATCTAAA +AAAAAAAAAAAAAGAAAGAAAATATTTTTCACTATAGAGAGGCATATGTCCCCTGAACTT +GCCGGGATCCACCTTTCCTGCTGGTGCATTCTGTGAGTTAGAAGAAAACTTCCAAAGAGC +CATTTTTTCCACCCTGTCTACTGTATAAAATTGCTTCTCAAACATGTGCTGCATTGCAGA +GGATTACCATTGTTTTGCTAACCAGCGTCTGGTCTTTCTTATGTGGCGCTGCAATTACTA +GTGTCAAACCCTGTTGGTAATACCCAGAGGACGGTGTCTGAAGTCTTTACTCAATATTCA +CATTTGGCCGGGTGTGGTGGCTCACACCTGTAATCCCAGCACTTTCGGAAGCAGAGGCAG +GCGGATCACTTGAGGTCAGGAGTTCAAGACCAGCCTGGCCAACATGGTGAAACTCCATCT +CTACTAAAAATACAAAAATTAGCCGGGTATGGTGGCGGGTGCCTGTAATCTCAGCTACTA +GGGAGGCTGAGACAGGAGAATCACTTGAACCCAGGAGGTGGAGGTTACAGTGAGCCAAGA +TTGTGCCACTGTACTCCAGCCTGGGGGAAAATTCACATTTGTAGAGAGTTTAAATTCTTT +TTTGATACGGAGTCTCGCTCTGTTGCCCGGGCTGGAGTGCAGTGGCAGGGTCTTGACTCA +CTACAACCTCTGCCTCCCAGGCTCAAGGGATTCTCCTGCTTTAGCCTCCTGAGTAGTTGG +GATTACAGGCACCCACCAAAACACCTGGGCAATTTTTGTATTTTTATTAGAGACAGGGTT +TCACCATGTTGTCCAGGCTGATCTGAAACTCCTGACCTCAGGTGATCTGCCTGCCCTGGC +CTCCCAAAGTGCTGGGATTACAGGCATGAGCCACCACGCCCGGCCGAGAGTTTAAATTCT +TAAGTCCTACACTCCAATGTGTGGGAAGTATTCGTGCTATGCTTTTATAACTAAATCATC +TCAGTATTTCTATTTCTAGCCCCCTTTTTCTGCCTGATGGTAAGATACTTAATCTAGTCA +ATTCCAGGTAAACTTTGGCCTTTTATGATTTTTCCTGATCAGGCCAAACCTCAACCAAGT +CCCTTCTTGATCTTCTCCTTCACCTCCTTCTCTCATTCACCCGACAATTAGCCTCCAGTC +CACGGGCTGATGCAGCATCTTGGTGTCCTGTGGTCTGAGGTCATTTTCTGTCTTTCTCAA +GCCTCAGCTAAAGTTTACAATCCTACCTTTTCTCATGACCTTGAAATGCCCTAAGGTTCA +GGGGCTTCATGGTTGCTGCTTCATGGGGGAACCTGGCTGTTCTCTGAGGCTGCTCGGCCG +CGAACACCCCATCAACTACCCGGGGCCCATCTACGCCCGAGGCCTCAGCCATTCCTGCTC +TACAGCTCTGCTGTCCCATTGGCACAGGGAACTTCTTGGGGCCCCAGGGTTCCAGATTGG +AAGCAGAGAATCTCCTCTGTTCTCAGACCCCCAAACTTTGTTGTGGATTCTAATTGTCCT +TTCCCCCATCTCACTCCTTGGAACCCACTGGGAGGTGAGTAGAATCCCTGTCAGAGATTC +TACCACCATCTCCCTCATTCTTACCCTAACTTTCTTCCTCTTCCTCCCTAGTTAGGAAAG +AGGATCTTTAGCCTGCGGCGGGGGGGTGGGGGTGGGGATGCTTGATGTTTCAGGGGAAAA +GGTGACTCAGCTACTTTTGGAATATCTGTCATACCTGTCTACTGGTGCAATGAGCTGGGA +TCACACCACTACACTCCAGCCTGGGTGACAGAGCAAGATTCCATCTCAAAAATAAATAAA +TAAATAAATAAAGACTCTGGAGAAACAACTCAATACACATGAGAAGAGGCTGGCCCATGT +AGGGAAAGGACTGGCAAACTATGACAACTCTTTTCTGTTGTTTTGTTTTCAATAGTCTCT +TCACAGTTCTTTTCACAGTTTGGAATTGATACCTTTTTCTCTTCATCAGAACTCCAATGT +TTTTGTAGATTGAAGTCTTTTTTTTTTTTTTTCTTGAGAAAGGGTCTCACTTTGTCACCC +AGGCTGGAGTGCAGTGGACCAATCACTGCTCACTGCAGCCTCGACTTCCTGGGCTCAAGA +AATCCTTCCACCTCAGCCCCCCAGTAGCTAGGACTACAGGTGTTCACCACCATGCCCAGT +TAATTTTTATTTTTTAATGTATTATTATTATTATTATTATTATTATTATTATTATTATTA +TTATTTTGAGATGGAGTCTTGCTCTGTTGCCCAGGCTGGAGTGCAGCGGCACCATCTCGG +CTCACTGCAACCTCTGCCTCCTGGGTTCAAGAGATTCTCTTGCCTCAGCCTTCCAAGTAA +GTGGGACTACAGGTGCATGCCCCCACACCTGGGTAATTTATTTTTTTGTAGAAAAGGGGT +ATCAGTGTGCTGTCCAGGCTGGTCTCAAACTCCTAACCTCGAGTGATCTGCCTGCCTTGG +CCTTCCAAACTACTGGGATTAGAGGTAATGAGTCACCATGACTGGCCTACGTATAGCCCA +AATGGATGAGCAGTTCCCAAGGCTCATTCCCAGCCTCCACTATCCAAGTCAGCCTCTCAT +CTCCTTCATTTCCCAGGACTTAGTTCTCATTTTCCTCCCCTGTTTTCTCCGGATTGTGGC +TATTGTTCCCTGGTTGCTAGATCAACCTGGAGCACAGTAAAGCAGTGTCACAAAGCTGGA +AGGGGTCTGGGATGAGTCCACCAGCTACAAGTTCTTATAGAAAACGTACTCCGGGGATGG +CCGGGCCCAGTGGCTCATGCCTGTAATCCCAGCACTTTGGGAGGCCGAGGCGGGCGGATC +CCCTGAGGTTGGGAGTTCGAGACCAGCCTGACCAACATGGAGAAACCCCGTCTCTACTAA +AAATACAAAATTAGCTGGGTGTGGTGGCACATGCCTGTAATCCCAGCTACTAGGGAGGCT +GAGGCAGGGGAATCGCTTGAACCTGGGAGGCGGAGGTTGCGGTGAGCCAAGATTATGCCA +TTGCACTCCAGCCTGGGCAACAAGAGTGAAACTCCATCTCAAAAAAAAAAAAAAAAAAGA +AAATGTACTCCAGGAATTGTCATTTCTGAAATTCAACAGCTTCTGGAATTGAAGCAAACA +GCTCATCTTGGAAGAGAAATATGTAGCCAACTCCAAAGCCAAAGCCTTTGAGTATTGAGA +CCTAGCATGCTAGGAGACCTTGATCCTGTAACCTCAGAAGAAGAATCTGGATCTGGCCAA +ATTGAGGTCAAATTCTGCTCAACTTCTCCATAGTCAGTAGGAGAAAAAAACCAACTTGAT +GTTTGAGTCATATGTTTTGACAACTAAAGAGGACACTTATGCTGGGGTCGGTGGTTCATG +CCTGTAATCCCAGCACTTTGGGAGGTCGAGGCGGGTGAATCATTTGAGGTCAGGGGTTCG +AGACCAGCCTGGCCAACATGGTGAAACCCCGTCTCTACAAAAAATTCAAAAAAATTGGCT +GGGGGCAGTGGCTCATGCCTGTAATCCCAGCACTTTGGGAGGCTGAGATGGGTGGATCAC +GAGGTCAGGAGTTCAAGACCAGCCTGGCCATTATGGTGAGACCCTGTCTCTACTAAAAAT +ACAAAAATGATCCGGGCATGGTGGCGCACGCCTGTGGTCCCAGCTACTCAGGAGGCTGAG +ACAGAAGAATCTCTTGAACCTGGGAGGTGGAGGTTGCAGTGAGCCGAGATCACGCCACTG +CACTCCAGGCTGGGTGACAGAGTGAGATGTCATCTCAAAAAATAAATAAATAAATAAATA +AAATTAGTCTGACTTAGTGGCGGGCCCCTGTAATCCCAGCTACTGGGAGGCTGAGGCAGG +AGAATCACTTGAACCCGGGAGGTGGATGCAGTGAGCCAAGATCATGCCACTGCACTCTAG +CCTGGGCGAGTGAGACTCCATCTCAAAAAAAAAAAAAAAAAAAAAAGACACTTAAAGATG +ACATTAAAGAGGATACTTAGATTCTAGACAAAATCAAGATATAGCAAATTGGGGTGGGAC +ACACCTGTAATCTCAGCATTTGGGGAGGCCGAGGCAGGTGGATCACCTGAGGTCCAAAGT +TTGAGACCACCCTGACCAACATGGCGAAACCCCGTCTCTACTAAAAATACAAAAATTAGC +CAGGCATGGTGGTGGACACCTGTAGTCCCAGCTACTCAGGAGGCTGAGGCAGGAGAATCA +TTTGAGCCCAGGAGGCAGAGGTTGCAGTGAGCTGAGACTGCACTGCTGCACTGGTGCCTG +GGCCACACCAGTCACTATGCCTGGGTGACAGAGCAAGACTCTGTCTCAAAATAAATAAAT +AAATAAATAAAATTTTGTTTTGCTGTGTTGCGGCTAATATGCGTGCTATAAGACAATGGT +TTCTTGAGTCTCATTCTCTCTGCATATGCCTAAAGCTTTTTTATTTTTATGATTCTAAAA +GATTGTACCTTCTCATCTCCTAGATTCTGTCCCATAGGTTCTGATTTTTCCTAGAGTAAC +TTGGAAGTTAAAAAAGTGGAAAAAGCTTTGCGTATTAGGTGCCAAACCCACTCAGCTCTG +CTCAAACCCCTTCTTTAATGCCCAAGGTTGTCCAATCCTAGCCCTTCCCCCTACCCTCAG +CTTTCTCCTCACCTACACAGCAACCTTAGTATAGTCCTAAAGTATGTGTTCTTATCTTCT +GTTATCTATGCCAAGGATGTTTGCTGGTTTTGTTTTGTTTTGTTGAGACAGGGTCTTGCT +CTGTCTCTTAGGCTGGAGTGCAGTGGCACAATCACAGCTCACTGCAACCTCGATCTCCTG +GGCTTAAGTGATCCCCCCACTCAGCCTCCTGAGTAGCTGGGACTACAGGTATGCATCACC +ACGCCTGGCTAATTTTTTTTTTTTTTTTTTTTTTTGAGGCAGAGTTTTGCTCTTGTTGCC +CAGGCTGGGGTACAATAGTGTCATCTCAGCTCACCACAACCTCTGCCTCCCAGGTTCAAG +CAATTCTCCTGCCTCAGCCTCTCAAATAGCTGGGATTACAGGCATGTGCCATCACATCCG +GCTACTGTTTTGTATTTTTAGTAGAGATGGGGTTTCTCCACGTTGGCCAGGCTGGTCTTG +AACTCCTGACCTCAGCTGATCCACCCACCTTGGGCTCCCAAAGTGCTGGGATTAAAGGCT +TGAGCCACCATGCCCGGCCCATGCCTGGCTAATTTTTTTTAATTTTTATTTTTGTAGAGA +TAGGGTCTCACTATGTTGTCCAGGCTAGTCTTGAACTCCTGGACTCAAGCGATCTTCCTG +TCTCAGCCTCCCAAAGTGCAGGAATTATAGGCATGAGCCACTTTGCCAGGCAAGGATTTT +TTTCTTTTTAAGTTACATTTCTGCCTGCCACCACAGCAGCTCTTTCTCCTGCTCTCTCTC +TCTCTCTGTGCTTTAAGATGATAGTCCCTTCTTTTTTTTCAAATAACCACAACAGGAAGG +ACTGACCACTCTTGTAAGCTGCAACTGATGTTTTCAGACTCCTAAAGTGACATCTAGACA +TAAGTCCATATATGTCAGAATATCATGCAGGGAATGCTCAAATAGTTGGGAAGAGATTGC +TGCACTGTGTTTTGCACGCCCAAAGCCCACATAGGTACTCAGTTTAAAAATCTTAATAGA +ATTGAATCCTGCTCTTATCATAGGAAAGGAAGAGCATCTGATAGAAACACAAAATGAAAA +GGTCAAGAACTGGCTGGGCACAGTGGCTCTCGCCTGTAATCCCAGCACTTTGGGAGGCTG +AGGCGGGAGGATCATGAGGTCAGGAGTTCGAGACCAGCCTGGTCAATATGGTGAAACCCC +GTCTCTACTAAAAATACAAAAAATAGCTGGGCGTGGTGGCGCGCACCTGTAGTCCCAGCT +ATTCAGGAGGCTGAGGCAGGAAAATCGCTTGAACCTGGGAGGCGGAGGTTGCAGTGAGCC +AAGATCACGCCACTGCACACCAGCCTGGGCAACAGAGCAAGACTCCGTCTCTCAAAAAAA +AAAAACAAAAAAAGTCGAGAACTGGAAAGGAACTAAGCGCATGAAAAGAAATTTTATGTT +CCTTCATGTTTTTATTTAAAGAAAGTGAATCAAGTACCAAACACGGAATAAAGGCAAACA +TTCATTTTTGGGGTGATTGTTCCCTTCTTGGCAATCCCTGTTTTATTGAGGGTATCACTA +GTTATTCAATCCAAGGATTTTTTTTGTTTCCACAGGAGGTGGGTGTTTCTTTGTCTTCTT +AGAGTCAGGATTCCAGATCTCCTGATGTGTGGGACTTTTCTTGGCCACTACGATTTCATC +TACAGTCACGAGCTGTAGCACCACCTCAGCCACTGCTCGAAATCCTTGGGCTTTGACTAT +TAGGGTGTCCCACACCCCTTCCTGGGCCACATTTATTATCCCTTCAGTTCCCACACCCAT +TAGGAGGTTCCCACCTTGGTGCACTCCACTCATTTCTGCCATCACGTCTGAGACAGCTAA +GCCTGCATTCTCTGCCAAAGTTTTAGGAAGATACTTCAGGGCCCAGGCAAATGCTAGGAA +TGCAGGCCCACTGGGCCCTTCCAATCTGCTTCCTTTATCAGAAAGCATTTTTGCCAAAGC +CATTTCTGTGGCCCCAGCTCCTGGAATCAGTCTGGGATCTTGACATAGCTGGAAATAGGC +ATCAATGCCGTGGTAGACGGCCTGCTCTGCACTCCGCAGCCCCTGGGTGGTGGCTCCCCT +GAGAACCACAGTGAGGGCAGGTGTGCCTGTACATTCCCATTCAAATACCACAGCCAAACC +ATCTCCCAGCTCCTGCCTGTAAACCCTCTGGCACTTGCCTGGCCTCTGGGGAGGGAGCAG +ACGAGGCAGCAGAGGTGTGTCCAACACCTCACTCAGGTAAATGATCTCCATCCAAGACCT +AGCTTGAATCACCACGATGCCATACTTGTCCGCCAGTGTGAGGGTCTCCTCGTCGACCTC +CCCCAACACCACTGCCACATTAATTCCTGCAGCTGCTAGCTGGCCTACTTGCTTTTCTAG +TAATTGATCGCTTCCTTTACTAAATTGAGCTAGATCAGCAGGACTAGAAAGACGGGCCGT +TGCTGGTGCATTTGGATGGGCAGGACCAAAGGGGCAAGCAAAGAGAGCCACCCTGGCACC +ACTTAACACTGTGGCCATTTGCCCACAGAGCTTCCCAGATATTGCTAACCCCGGGAGGAG +GCAGGAATCCTCCAGTGTCCCCCCGGGCAGCGCGCACACCCCAACACGCTCAGGCTTGAA +GCTGCCGTCTAGTTCCTTGATAGCCCAGCAGGCGTGGGCCACCAGCTTGGTCAAGTGGTC +CATGGGGGACAGGGTGTGGGTATTCATCACAGAATGGAGGGCCCAGGATGGATCTTCCAA +AGGCCCCAGAGATTGGATGGCCAGGGAGGGCAGTGTGGCCAGGACCTCTGCAGTGGCCGT +GGCGTAGGCCTCCCGGAGCTGCGGGCGAGGCAGGCCAGCCTTCAGCAGCTGCTCTGCCTG +TTCCAGCAAGGCTTCCGTCAGCAGAACCACGAAGGCTGTGCCGTCCCCACTATTCTCTGC +CTGGGTTTGTCCTGCTTCCCGGAGGAGCCATGCTGCTGGGTGCTCCAGCTCCAGGGCCCT +GAGGATGGCAGTGGCACACCCCGTGCACACTGTTTCTCCTTTCATGGTCACCAGGAACTT +CTGCCGGCCGTGGGGGCCATAGCAAGGCCGGATGACACTGGCCAGGGTCTGGACTGCAGC +CAAGCTGCTCAGCAGGTGGGGCTCCTCCTCTTCTGGACTCCTCGGGCTCTCCCTTGGGTT +CAGTGCCAGCCGCTGGGGCAGCTCCAGGGCTGAAGGGACTGTGCTGTCCATGGCCCGCAG +AGAGAGGAGAGGCCACCGTGGGTTGCAGAGATGCTCTAGAAACAGCAGCTGGGGCACTCC +TGACACCGATCGTTGAAAGTACTCAAGAGGTCAGTGGAAGCAAGGAGCCAAATGCCCATT +GATTGGTATCTGAAGACATCAGCACGGACCAGCACTCCACTGTGGGTCCAAGGATGAGCT +CCAAAGAGCCCAGTCCTAAAGCCACCCCAGGGTTGATTCTGTAAAGGAACTGGGTCTTGG +GGCCTCTCAACCTTGGTGGCTGAAATGGGATCTTTAACTGATGAAGTCACAAAGTGGAAA +ATGGAACCAGGATAGAGAATGAGGTCACAGAAGGCTGGTTAGAACTGAGGAGGCCCTACC +AGCAGGCAAAAGTCAGGCCTTGTCCAGCAATGGAGGTACATGCACCTCTGCACCAGGTTT +GAGACTTGTTTAAACGTAAGAGACAATGAGGAGGAGATCAAGTGAAAAACTACCCATTTC +ACCCTATCTGGAGTGCAGGGGCATAACCATGGTTCACTGCAGGCCCAGCTCCCTGGTCTC +AAGCAGTCCTCCTGCTCAGGTTCCCAAGTACCTGGGACTACAGGCACACACCACCACACC +TAGCTAGTTTTTTTATTTTTTGTAGAGACAGTGTTTCTGTCTGTTGTCCAGGCAGGTCTC +GAATTCCTAGCCTCAAGAGAGCCTTCCACCTTGGCCTCCCAAAGTGCTAGGACTACAGGT +GTGAGCCACCACCTCACCCACCCTTTTTTTTTTTTTTTTTTTGAGACAGAGTCACACTCT +GTTGCCCAGGCTGGAGTGCAGTGGTACAATCTTAGCTCACTGCAACCTCCACCTCCCAGG +TTCAAGCAGTTCTCCTGCCTCAGCCTCTCAGTAGCTGGGATTACAGGTGCCAGCCACCAC +GCCCGGCTAATTTTTTATATTTTTAGTAGAGATAGGGGGATTTCACCATGTTGGCCATGG +TTGGCCAGGTTAGTCTCAAACTCCTGGCCTCAAGTGATCCGCCCACCTCGGCCTCCAAAA +GTGCTGGGATTACAGGTGTGAGCCACTGCACCTGGCCTTTTTTTTTTATTTGAGAAGGAA +CTGAGAGATGATGTCTGTGTTTTGTTTTGTTTTGGTGTTACTTTCTCTTGCAGTACTGTG +TAATATTAGCCATGTTTTGCTGTCTGCCTTTGACTTTTTGGGTATCTTATCAGTTTGTGC +TTGTGTATCAGGTTTCTTAGGGTGTCTGTTGGTCTTTCAGGGTGCAGGTGTGGGAGGCTG +CACAGCGTGCATGCCTGTGCCACGACTCCCAACTCTGCCTCCCTGGCAGAGGCAGGGCAA +GACAAGTGGGGAAGGATGCTGACAGCTCACAGACAAATAGAAGTGAACCCAGAGGGGTGA +AAAGCAACCAGCCTCCCAGCGGTCAGGGAGGTAGAAGCCTAAATGGGGTCCTGAGATTTA +AATGCGAATCGCCTTCCCATCCTAACCTTCAATGCTTACAATTTAAGTCTCTTTTTTTCA +TTCTCTCTCCTTTCCTCACTTGTCTCCTCTTTCCTCCTATAGAGCCTACTCGGGTAATGA +TGCTTCTGCTTTAGTTTAACACATATTTAGTCTGGGCGTGGTGGCTCATGCATGTAATCC +CTGCACGTTGGGAGGCTGAGGCGGGAGGATTGCTTAAGCTCAGGAGGTTGAGGCTTCAGT +GAGCCATGATTGCACCACTGCATTCCAGCTAGGGCAACAGAGTGAGACTTGTCTCAAAAA +AAATAGGGGAAAGGTCATTTGGAATCCTAGTCCAGAGATAACCATTGTTTACAACTTGAT +GAACATTACTACTTTGCACATATTATATGCATACATAATTATAGATTTACACCATTTTAC +ATAAGATTATGATACATATATGCTATTCTGTGATCATTTCCCCCTCAACATTATCTTGGC +TCAGAGAAATGTTTCTTTTTTTGTTTGGACATGGAGTTTCGGAGTTTCGCTCTTGTCGCC +CAGGCTGGAGTACAATGGCGCAATCTCGGCTCACCCTCGGCTCACCACAGCCTCTGCCTC +CCGGGTTCAAGCAATTCTCTTGCCTCAGCCTCCTGAGTAGCTGGGACTGAGTAGCCATGT +GCCACCATGCCCGGCTAATTTTGTGTTTTTAGTAGAGACAGGGTTTCTCCATGTTAGTCA +GGCTGGTCTCAAACTCCTGACCTCAGGGGATCCACCCGCCTCGGCCTCCCAAAAGTGCTG +GGATTACAGGCGTGTGCCACTGTGCCTGGTCTGTGAGCCACTGTGCCCGGCCTGAGAAAT +GTTTCTTTTTTTCTTTCTTTTTTTTTTTTTAAGCAGAAACACATTCATTTATTAACCAAA +GGGATGATCCTAATGAATCCAACACACTTTGAAATAGCTGCATGTAAAATGTTTGTGATA +AAGATAATTGAACACAGTAATGAAAAAAAAAAAAGAAAGAAAGAAACGGTATGGAGATTT +GCTCATTGAACTGAGCTTGGTCATTCTCTTAGTTAACTCCTGTCCAAAGTGATGATGGAA +TCTTTATTGTACTTTTTCATAGATCCGAGTACAGGCGACATGGTTCATGACACAGTCCAC +CACTAATTTCCCATCTTTCAATGTTCTTGTTATTGTGCTTTCCTTCCCATCCCACTCCTG +ATGCTGAACCAATGCACCATCTGTAAAGTTGCACACAGTCTGAGTTTTTCTGCCATCAGC +TGTGGTTTCTTCAAACTTCTCTCCCAGGGTACAAGAAAACTGTGTTGTTTTCAAAGTGCT +CTCAGTTTTTATGGTGAGGTTTTTGCCATCACAAGTGATGATACAATCTGGCTTGGCCAT +TGCGCCCATTTTTTGCAAAGCTATTTCCTCCTAGCTCCTTCATGTATTCATCAAAGCCTT +CGCTGTCCACCAGGCGCCATCTTCCTTCCAGCTGCTGAACTGTGGCCATGGTGGGTGCAG +GGGGGCTGGTGTGCAGAGCAGGGTCTGCGTCGGCGTGGCAGCGTGCTGTCGAGAAATGTT +TCTAAGGAGATCTTATTTGGTCTGAGAACCATGAATGATTATTTTGAGCACTTTTGATTC +TGGAGACTCCATTTGGATCAGGCATGGTCCTCCAAATTCAGGCTTCTGAAAGCCTGTACC +TCAGAGTAGGCTTGATGTTCCATAAAAGATGTGGTTATGAGTGCAAAGATGACTTGCCTG +TATTGTTATACAAATGTAAAATGTAACAATCAACAAAAATGTAGCAAAGTATGCATGTAT +ACATTTTCTCTAAAGATACAGTTTCTTTTTTGAAAAAATAAACACATTAGGCAGGTGTGA +TGGCGGGTGCCTGTTATCCCAGCTACTCCGGAGGCTAAGGCACGAGAATCTCTTGAACCT +GGGAGGTGGACAAATTGCAGTGAGCCAAGATTGCGCCACTATACTCCAGCCTGGGCAATA +GAGCGAGACTCAGTCTCAAAAAATAAATAAATAAATAAATAAATAAATAAATAAATAAAA +TAAACACTACCGGCCAGTGGCCATGGCTCGAGCCTATAATCCCAGCACTTTGGGAGGCCT +GAGCCAGGTGGAGTTCAGGCATTCAAGACCAGCTTGGGCAATATGACAAGACCCCTGTCT +CTACTAAAAATACAAAACAATAGCCGGCCGTGGTGGTGTGTGCCTGTAGTCAGCTGCTTG +GGAGGCTGAGGTGGGAGGATTGCTTGAGCCCTGAAGGTGGAAGTTGCAGTGAGCTGAGAT +AGTGCCATTGCACTCCAGCCTGGGTGACAGAGTGAGACCCTGTCTCAAAAAATAAAATAA +AATAAACACTCCTATAAAGGATCCTCTTAGCTCTTTTTCTAACACCTAATCTACATTTTC +ATATTCATTTCAGTTACCCTACAACTGTTCACTGAGCTGCTGTTGAATAGGGGAAATAAG +GCAGATAACTACTGCCATCTCCGCTGGAGGGACGATACAGACATTAATCTGGGCACTTTG +ATTACAGGCAATGAGAGCTGTGAGTGGGGAAAGCACAAGGTTGGCAGAAGCATTTAGGGG +GACACAGCCATTCTCACGGAGGGCAGAGGTCTAAAGCAAGAGCTGAATAAAAAGTAGGAA +CTGGCCTCGTGGAAAGGGGAAGGGTGATGGGACAGCCTGGTGGTTTGTAGCCCACTGGAA +GGAGTTCTGAAAACTGGTGGTCAGGTGAGAAGGAAAGCTGGGGAAGAGATGAGCACGTTC +GCCAGAGGGTAGCAGGGGCTCTCCGGACCTAGTGAGTCAAGCCAAGGAATTAAGGCTTCA +GCCTGCAGGGTGATGAATAGGGCTGTCTATTCCATTTCTTCCTTCTTTCTTTCTTTTCTT +TCTTTTTTTGAGACAGCGTCTCACTCTGTCACCCAGGCTGGAGTGCAGTGGCACGATCCT +GGCTCACTGCAACCTCTGCCTCCCTGATTCAAGCAATTCTCCTGCTTCAGCCTCCAGAAT +AGCCGGGATTACGGGTGCCTGCTACCACGCCTGGCTAATTTTGTATTTTTAGTAGAGGCG +AGGTTTCACCATGTTGGTCAGGCTGGTCTCGAACTCCTGACCTCAAGTGATCTGCCTACC +TCGGCCTCCCAAAGTGCTGGGATTACAGGTGTAAACCACCGTGCCTGGCCTGAAAATTTC +TAGTTTATGATACTTGCCAGCAGAATGTGTTCTGTCACCCTCTTCTGAATAGATATGGTT +GTCTGCTATGACTTCTCCCACTGCTGCCCTTCCCCCTGAATCCACAGATGCATTTCTTTT +AAAACTATGATCTTGTACACAATGGATGTAAATATTTAATCTTTCTATTTGTATGTTTTT +CCATGTTTCTTTTCTTTCTTTCTCTTTTTTTTTTTTTTTTTTTTTTTTTTGGAGGTGGTG +TCTGCCTCTATTGCCCACAGGCTGGAGTGCACTGGTACAATCTCGGCTCACTGCACCCTC +CGCCTCCTAGGTTCAAGGGATTCTGCTGCCTGAGCCTCCTGAGTAGCTGGGACTACAGGT +GTGCACCACCACGCCCGGCTAGTTTTTATATTTTTAACAGAGACAGGGTTTCACCATATT +GGCCAGGCTGGTCTCGAACTCCTGACCTCGTGATCCTCTCACCTCGTCCTCCCAAAGTGC +TGGGATTACAGGCATGAGCCACCGTGCCCGGCCTCCATGTTTATTTTCTAGTTGCTTACT +TGTCCTTTTGTGTTTATCCTTGTTAACTACTACTGCCAGGCTTAAAGTATAGACCCCTAG +AGGGCAAGATTTGTATCTATATAAAATGTACTGCAAAACATCTACTTAAGCCTCACATTC +TTAAACACAAATTACTTTTGAAGATGACTGTTCTGTTTGTTTCCTTCCTGGTTTCTTCCT +TTAACTTTTCCACCAAACAGGTACATGATATACTTTACTGAAATAACTTATATAGCAATA +TGAATTTTTTTTTTGAGGCGGAGTTTCGCTCTTGTTGCCCAGGCTAGAGTGCAATGGCGT +GATCTTGGCTCACTGCAACCTCCGCCTCCTGGGTTCAAACAATTCTCCTGTCTCAGCCTC +CAGAATAGCGGGGATTACAGGCGCACACCACCATGCCAGGCTAATTTTTGTATTTTTAGT +AGAGACGGGGGTTCACCATGTTGGCCACGCTGGTCTCGAACTCCTGACCTCAGGTGATCC +GCCTGCCTTGGCCTCCCAAAGTGCTGGGACTACAGGCATGAGCCACCGTGCCCGGCAAAT +TTGAGGTGGAGGTTGCAGTGAGCTGAGATCGCATCACTGCACTCTAGCCTAGGTGACAGA +GCAAGACTGTCTCCCACTTCAGCCTCCCAAGTAGCTGGGACTACAAGCATGTGCCACCAG +ACCTGGTTAATTTTTTTTTTTTTTTTTTTTGAGACGGAGTCTCGCTCCATCACCCAGGCT +GGAGTGCAGTGGCGCGATCTCAGCTCACTGCAAGCTCCCCCTCCCGGGTACACGCCACTC +TCCTGCCTCAGCCTCCCGAGTAGCTGGGACTACAGGCACCTGCCAGCACGCCCGGCTAAC +TTTTTGCATTTTTAGTAGAGACAGGGTTTCACCGTGTTAGCCAGGATGGTCTCGATCTCC +TGACCTCATGATCCACCTGCCTTGGCCTCTCAAAGTGCTGGGATTATAGGCGTGAGCCAC +CGCGCCCAGCCAGGCCTGGTTAATTTTCTTTGGTATTTTTTTGTAGAGACGGAGGTCTCA +CTATGTTGCCCAGGCTGGTCTCGAACTCCTGAGCTCAAGTGATCCACCTGCCTTGGCCTT +CCAAAGTGCTAGGATTACAGGCATGAGCCACGGTGCCCAGCCTACAGTGCAACTTTAATA +ATAACAATATGAACACAAAAATTCTAAGATCTAAAATTTAAGCTTTCAGTAGTCCTTCTA +TAACTGTGAAAGTTTGGTTCCTAAAAAGCCCTGAGGAATTTATGGGAAAACAAGAGAGAC +AACATTTAGTAGTGAACCTGTGCATTCTAAATAAAGACAATATCAATGACGTGTTATAGG +TCTTCAATTAGTAAGAATGAATATTGGACTATGAATTTTTATTCACTGTCACTTGTTTGC +TAGATGCTTTGAGAATCTTCCTTGCCTATATTTTCCTGAGATGTTGGTTTTTCTTTGTCA +CAGATAACAATGCTCATTCCCTCCCCATTAAAAACTAAATATATATATATATATATATAT +GATTAAACGATTACTACATGTGCTTTGAAATATTCAAATATTTTAGACAGTAAAAGTCCC +TTGTAATTCAACCCTTTGCAGATGATTGGTTAACAGGTTAGTACACATCTACCTAAATTT +AAAATCCCATATTTAACATGTATACTTATTAGAAAGTACACATTCTAATATTTTTCTATT +GTATTTGGTACTATTTTCAGATGCTCCTGCCTTTTTCTTTCGTAATTTTGAAGGACCTCA +GCTCCCTGCCTCCTAGATTTTTGCTACTATGGTCTCAGAGCTGTGTAATTTGGATGACTG +AGATGGAAAAACCTCTGGAAAACCTTTATTTATGTTGAATAAGTATTCCTTGAATCCTTC +CTCAGCATCCTGGGTTATATTTGATTTGCTCTGCTCATGATAACTTCATGCCAAGGAGAC +TGCTATCAGTTCTCTTAAAACAGATCCCAACTCCCTGCTCATAGTGGCCAAAGGAATGGA +GATTTCAGGCTGAGTTTACTTACGTGCATCATCTTCATCTATCCAGAAGCATCCCTGCAC +AAAACCTCTGTTTCTACCCTTCCATTCACTCGGCTCACTTTTCTGCTCTTAGTACCCTTT +GTTTCTTGTGAACTCTCCAGCAGGAGTGACTTGCAATTTGTATCCACTGACACTTAAGTT +CTCGGAAGTGCTGGAGAAGTGTATGGAAGTAAATTATCCTGATGTATAATTTTGTGCATG +TGAAACTCACCGTGGAAGTGCCTATCTAATTTCAGTATGGAACACAGCTAAACATTTGGA +TCAATAATCCAGTTTTGAAACCACACTTCATTTAAAGTACAATGTGCTGAAAAAAATGAA +AAAAGGGTGCTTTCAAATTTGTACTTAGTAAACTTTCACTAGATCACATCATATGTTTAT +CACTAGTCATGTTGTATTTCTATGTGTAATCGCCAGGCACTTTTAATTTCTAGTTTGCAT +TTACCATGCCAGCCTCCTCCTCAATCCCAAATTTCCTTTGGTTATAAATTTAGTAAATTT +GAAAGAGCCAGCAGGGATTAAACCCTGAAGGTATTCAAATGACTATCTGACGTTATTCCT +CATTTCAGCCATTTCGAAAAATTATGCTTTCATTTAGAATAGGCTCTGGGAATCAAAGTG +TGTGTATTTTGCCCAAGTAGAAGACACAGTTTAAAGTTAACATCCTAGCTACTAGAAGGG +AAAGCAAACAACATCGCTGCAAAAGGAGCCTATTTTTTTTTTACCTTACACTAAAACTAC +ATTGTGAAGATCAAACGAAATCAAGATGAGAGTGTGCCTCTTAACGCCAGGTCCAAAGTA +GATGCTTATTAAATGATAGTTTACCCCAATCCTTCACAAATGGTTGATAGGTCTTACTAT +TTCCCCCCTATTCAAATCTAGAATTTTTTCACTCCCATATACTAATCGATAGTTAATGGA +AAGCACAGAATAGATCATCGTCCAAGTGTTAGGTATTAGCCTGAGGAATCCGGAATCCCA +TATTTGTAACTGTCCTTCTTGAGAAAGTGCATTTTTCAGGCGGATTCTAGCCCCATTTTT +CCTTTTACCATTTTTACATGTTATGAGAGGTGGCTTAGAAATACTTCGATTTTTGCCTCT +TCATCACAACACACTGAACGTTAAAATCAAGTGGTTGGGTTTTTATTGGCTTATTTTGTC +TCTAACCGTTTTATTTCTCGAGCTGTCATCGTTCTTTTCGTCTTACATCCTTATGAACCT +TTTCTGGATTAAAAAAATGACGTTATAATAAGGAAACTGTAACTGGCGTTGGATTAGAAC +GAAGTTGACTCCATTCCTTTTCCTCCCCGTAGTGTGGGCGATACGAGGAAAGACCTCGGC +AAGAACCAGCGAAGCCCCGGCTGCCCTCGCCCTGCGGGCGCACACTTGCTCCTCGCGCCG +GGCTGCGCCGGGCGCCCGCGCCGCCTCGGCGTGTGTCCGCGGCTCCCTCCCGCCCTCGCC +CGCAGTCCCCCGATCCCGATCCCGGATCTCTGGGTCCACAGCTTGGCTCCCTCCCGAGCC +GGAGCCGGAGCCGGAGCCGAAGTCGCGGCTGGGCCCGGCCGCCCCGTCACAGGGGGAGGG +AACCCATGGGGAGGGGGAGGGGCGGTGAGGTCAGCGGCGGCGGCGCGTCCGCGGGCGGCG +GGAGCTTCGCATGCGCGGAGCGAGGCCCGTGAGTGGCAGCGGCGGCGCGCGGGGGGCGGG +CGAGGGGCCGAGAGTGGGGGAGCGGGCGGGGGCCGTCGAGGAGGCGTTGTGTGGGCGCGA +CGGCTGCGAGTTGGGGAGGTCTGTGGTGCGGGTCGCCCCGGGGGATCCCCGGCGCGGGCC +TCGCGCGACGGCCACGGTCGCGCGGCGTGTGTGGGGGGTCCACGCACACCCGCAAAACTT +CCTCCTCCCCTGCTCCGGGAGAGCGAGCGAGCGTGTGTGAGAGCGAGTGTGAGGAGCGAG +CCGCGGCCCGACGCCCAGCGCCGCCGCTGGAGCAGCTGTCAAAACTTCGCCGCCGCCCGG +GCCCCGCGGCCCGCCCTCCCCGCGCCGGGCCCCTTTCTCTTCCTGCTGCGGGCGGCCCGG +GGGAGGGGCCGCGGGCGGAGACCCCGGAGGCCGGCGCCCCTCACGCCGCCCGCCCGCCCG +CTCCCCGCCCGGCCCCTGCGCGCGTGCGTGTCCTGCTCGCTCCATGTTGCCGCCTCTCCC +GGTACCTGCTGCTGCTCCCGGGGCTTCGGGAAATGCGAGAGTCTGAGCCGGGGAGGAGGA +ACCCGAGCAGCGGCGGCGGCGGCCGCGGCGGCGGGAGCCCCCCAAGAGGAGGACCGGGAT +CCATGTGTCTTTCCTGGTGACTAGGATGTCGTCGGAGGAGAACAAGTGCGTGGAGCAGCC +GCAGCCACCACCCCCCGAGGAGCCTGGAGCCCCGGCCCCGAGCCCCCCAGCCGCAGACAA +AAGACCTCGGGGCCGGCCTCGCAAGGCGCTTCCCCTTTCCAGAGAGCCAGAAAGAAGTAA +GTTGAGTGCGAGGGAGCCAGGCCGGGAGCCAGCGGCGGCGCCGGGCCGGAGCTGCCACCG +GGCGCCCGCCCCGCGGCCTCCACGCCTTGGCGCCCCCCGGCGGGATGGGGGCGGGGCGGG +CCCGCGGGCGGCGGCAGCTCCCGGCCCCGGCCCCACGCCCCTCGGTAGCCGCCCGCGCCC +GGCCTCCCCCGCTCCGCGCCGCCCGCCCGGGCTCCCGTCGGCGCCCGGCTTCGCACACTT +TACTTTTCAGTCGGGCCTTTTCAGTGGGTCTTCTCCGCGACTCTTCTTTTGGAGAAATTT +CTCGTAGCCGCGTCTTGGCCTAGCTGGATCATTGAGAAAACAAGCCCGGAGCGCGCGCAG +GTAGTCCCCGGACGGACTCCGAGCGAACCGCCGAGCCGTGGGCGCTCGGGAAACTCGGAG +CTGTCAAAACGCCCGGGCCAGGTGGTCTCGGGGCGCGGGCTGGGGGCGAGAAGAAAGCGG +CCGGGCGAGTGCAGCTTTTGTTTGTCAGCGACTCGTTCGTGGAACTTTTCCTGGTCCCAA +ACCTGTGTTTTCTTCTTTTGATGATATATTAGGAAGCCATTTGGCTTCTTCCTTCCCCCT +CCCCCAACACCCAGCACCGCACTCCCGGGCTCCGAAAGCACAAGTCCTGTGGGAACCCCC +AGCTTCGGGGAACGGCCTGCCTAAGTTTTGGAGACGTAGCCAGCGTCCCCTCGTAAGGCA +GAATACCAAGAGCACTTATTCAGAGAGAGTGCAGATGTAAATGTCGTTTCCCTCGTAAGT +CTTAGCTGTAAGGGGCTTGGGAATAGGGTCGCCTGCCTTTGACCGACCGTACTGTAGGGC +TGGACACCGGCTTATTAGAGGACCAGAAATGTCTTCTTACAGAACGGTTATTTGACGGCT +TTGCTTGTAAATTAAGACACCGTTTTAGTGCCAGCGAGCTGCTCGGCTTCTGTGGCTCTC +GCGTGTGCCGTGGAAGAACTGTGAATGTCTTTCGAAGTTGTAGAATGGCGTGTGTGCTTA +CTCATTTCATGAGATGATATTCTCATTGAACTGTCGGGAGTGGAAGGGTGCGCTGGGACG +TGAAGGAAGCCAGCACGTTTATGGATAGGCTGTTTCTTTGGTTCGGGTGCATTCACTTAG +TAATAGTGTTGTTTGGTGATTTGTAGTAAAAATAGTAGCGTGAACTGAGGCATAGCAGAG +CTGGGTTGTGGGAACCCATTAAGCTCTTGACTTGAATGTGCTCTTTTCTTGCCCCGCTGT +CCTTTTACTATGAAAATGATTCAGGGCCTTCAACTTGCCTCCATATTTTATTGCCAGCTC +TTACCTAGCTATGATAATCGTGAGGGAGGCAAGTACAGGATGTGTGTACGTTATTACATT +AGCTTCTTCGTGATACAAAGTTAGGACTTACTTATGCCACTTGCGTTGTAATACAATGGC +AAATATAAAATGCCCTTATTCTATATTAACTGAAATTTGGAGAAGGAAGTGGAGGTTTAA +GTAATTTTTAGACGTCTAAGCCACTTTTTTGCATCCTTTAAAGCAACTCAGGACAAGCCA +TATTGGGGGTTTTACCTTGATTGCCTCCCATTTCACTATTTGCAAAGCATTTCTTCATCT +CTTACTGAACATTAATTTGCAATTTTTTTTTTTAATTTGCATTTGAATTCTTACTCCAGA +AAGATTAGATCTGTGTTGTCACACCCCACACCCCATACTCCTGTAAGGGCGTGCTTGTGC +ACGCGCACACGCTCACACGCACGCGCACACTCGCACACACCCTACTTTTGAAATGAGCTC +ATTTGTATTAGTGCAGCTCCTGAGTGCACTGGACGATTAGGGTATTGCCACTTTATTATT +TTAATTCTTAATCTCATATTATGAAGAAATAGGTAGCCTTTGGAGAAGATAAAAAATTTC +TGCTGAATAACAGTATAATCTAACTATGAAACATCAAAACTTTTGGAAATATTTAGAACA +AATGTAAGTCTGTAGAGAGCTTTTTCTTTTAGATTTGAAAACTAGTACTGCTTTCTTTAT +AGGAAAGTAAAGTCTACTGGTAAATTTCACGGGTCTAAACTTTTTAGAGCTTTTTTTTGA +AATTGTGTCTTTTGAAGGGAGTGGAATCTCCAGTTGTTTTTAGAAACATGTAAATGGAAA +CTAACATATGAATTGGAAAGCAAAGAGAAAGTTTTTCAATTGTGTATCTCTATACTGTAT +AAGAATCCATGCAGAAAAGACCCTGTAGTTGGATAGTAAAGACCCTGAAGGTGAAACTTA +TGTGTAACCAGTGTAAATTAGGTTTGTAACCAGTGAAATTATGTGAAATTGCAAATAATT +CACCTGAGAAATGAAAATTAATCTTCTTTGCTAAATGCCATAGAGATATTTTAAGTTGCT +AATGTTACTTAGATGTTCATTAACTTAGTGAGTTACATTAAGTAGAGAAGATGCCTTTTT +TTTTTTTCTGTACGAAGTCTTGCTCTGTAGCCCAGTGTAGTGGTATGATCTCGGCTCACC +ACAACCTCCGCCTCCTGTATTCAAGCGACTCTCCTGCCTCAGCCTCCAGAGTAGCTGGGA +TTACAGGTGTGCACCATCGCACCTAGCTAATTTTTTGTATTTTTAGCAGAGACAGCATTT +CACCATGTTGGCCAGGCTGTTCTTGAACCCCCGACCTCAGGTAATCCACCCTCCTTAGCC +TCCCAAAGTGCCAGGATTACAGGCGTGAGCCACTGCACCCTGCTGAGAAGATGCCTTTTG +ACAATGAAGTGGATTTGTATATTTATCTTTGGCTTAAAAAAACATGCACCACCAATTACA +CTTTCCTCAAGTTTAAATTTTTAATAATTAGGAAAATAAAGCATTTTCTTGTCTTATAGT +GTTAGCTAGATTGTTTTTGTGTATTTTGTCATGAATAAAAAGCATAGCTATATAGTTACT +GCTTTTACATTAACTATAAATATCTTAAAATTTTACTACCTAAAATCAGGAAACTTGAAC +TGAAGCTACTAATCTTAGAGTTGGAAAAGTAAATACATAGAGGTTTCCTGTTGTACAAAT +GTCAAGTGGCACAGTGAAATTTACATTCATTTGAAAGTTTTCCTTAACTGTAAAAAGTAT +CAAATTACTTGATACTTTGGAGTAGTTCATCATCTTTATCAGAGGCACAGGTCTTAACCA +TTGGCAAGCCTCTGTCAGAATATGCACATATTAAAGATCTGATTATTTTTGTGTTAATGT +TAAAAAATTTTTCTGAAGCTTTTATCTTATTTTTTCCATCCTTACACCGTAAATTCACAT +TACCAAGTTGGGAAGCCAAAGAAACATTCTACTCTACTATGTTTCTTACCAGTTCATGAA +AGTTGATGTTAGAAATGGGTGTGGGTGTGGGGGATGGGGGTGGTTGTACAGAAGCAGCAG +GTGGTAGGGATAGGATTTCTGAAGCACTATCCTTGGCCTTTTTTGAGTAAACTCTTTATA +CCCTGAGCCACTTTCTTTTCAGAGGGCAATTGCTATTATTAGAGAGCCACCTTAAGCATT +ATTGTTGTAGAAAAATTAGGCACAACCAGTGATTGTCATTACAAGGACCAGCAAAAATGG +CTAGGTTGCTACTCTGTATTTGTAACGCCCTTCCCCCAACAAAATTTCTCCTTTTCATAT +CTGTGAATTAGAAATAAGTGATAGAAAACTGTACTGCATTACAATATATACCATTTAATA +AAACAAGTTTATAGTTGAGAGCACTATTCATGCTTTTTGAGATAATGCAAATTTGTAATT +TTTATGATAGCAATTCTTAATAATTTATTGTCCAAGAGATTTGATAAAATTTTTGATAGT +TATTGGTCTCTGGGACTCAATAGGCACTGAAATGTTTTAATTCAGTTGAAAAGTTGGTTC +AGGATTGCTACCCTCTCTTACCTGTTAGGAGGTTGTTGTTTAACCTGACCTGAAATTCCC +ATGAATAAGAACCTGTTTTTTTTTTTTTTTTCTTTGACAGAGTCTTGCTCTGTCGCCCAG +GCTGCAGTGCAGTGGTGCGATCTTGGCTCGCTGCAAGTTCCGCCTCCCAGGTTCAAGCGA +TTCTCCTGTCTCAGCCTCCCAAGTAGCTGGAGTAGCTGGGACTGCAGGCACGTACCACCA +TGCCTGACTAATTTTTGTATTTTTAGTAGAGACGGGGTTTCACCGTGTTAGCCAGGATGG +TCGCAATCTCTTGACCTCATGATCTGCCTGCCTTGGCCTCCCAAAGTGCTGGGATTACAG +GTGTGAGCCACCGCACCTGGCCCAGGGAATTTCTAATATTTGAGAAGATGTTATTTTTAG +TCTATTATACAAATTTATATATTGTTTACTAATATATAAATTTACATATTGGTTACTAAT +ATGTAAACACCAATTTACATATTGGTTACTAATATGTAAACTTGATAAACATGGATTTCC +ATGGAAATTTAAAAGTATCACAACAATTTGTTTTCCCATTCTGAAACTTGTGATTTATTA +CATTTTCCTACTATTTCAGTTAATTCCATAATGCCAGATTTGTTGTCAATTTGCCGAGTG +ACAAGCCACACTGCTTCCTCTCATTCCTCTATTCCGCAAAACTGCAAAGTTTCCCAGACC +ACAGTCAGGTTTCTCTGGGTTGTCCAACTCTGTAAACTTACAGAGTGGTTGTCCAACTCT +GTAAACTTACAGAGTGGTTGTCCAACTCTGTAAACTTACAGAGTGGTTGTCCAACTCTGT +AAACTTAAGTCACTTTAAGTTTATGACGGAGGGGCTTCGTGAAACTTCATTGACCTTCCA +AGGTGAAAATTGGTCAGTTTTCAGTTATAAAGGACATTAAGGATGGGTGTGGTGGCTGAT +ACATGTAATCCCAGCACTTTCGGGAGACTGAGTCAGGAGGATCACTTAATCCTCATTTAA +AAGGAGTTTGAGACCAGCCTGGGCAACAAAGTGAGGCCTTGTCTCTACAAAAAAATTAGC +TGGGTGTGGTGGTAGGCACTTGTAATCCCAACTACTCTGGAGACTGAGCTGAGAGAAGAT +TGTGTGAGGCTTGGAGGTTGAGGCTGCAGTGAACGGACATCACACCACTACACTCTAGTC +AGGTGACAGAGCAAGACTCTAAATAAATAGGAACATTAGATGGTCTCTCTGCACTCTTGC +CTGGTGGGGACGTGTTAGATACCCTCGTTAGGTTGTGATTTAGTTTTTAATCTGTGAGAT +GTTTGGGTCAAACAATTTTTAGCTGCCATGGAATAAACTTTCCAGTCAGCGTGTGAGTTT +GTGTTTGCCTTTACTTTTTTTTTTCTATATTGTTTTGGTCTATTTTTATCTTTTAATTTC +AGAAAGCTGATTAATCTCTTCCTTTTCTCTTTAAAAATTTTCTTTATCATGTTTGTGCTA +CAGTGGTTATTTTGAGAACTTGTTGGCAGGATAAGTTGCAAAAGTTATGAAGTAGAATAG +GGATGATTTCTGTTTTTGTTTTTTTTTTTTTCAGACAGAGTCTCACTCTCTTGCCTAGGC +TGGAGTGCAGTGGCGTGATCCTGGCTCACTGCAGCCGCCGCCCTCCGGATTCAAGTGATT +TGCCTGGCTCAGCCTCCCAAAAAGCTGGGATTACAGGTGCATGCCACCACACCCAGCTAA +TTTTTGTGTTTTTAGTAGAGATGGGTGTTCACCATGTTGGCCAGGCTGGTCTCAAACTCC +TGACCTCAGGTGATCTGCCTGCCTCCGCACTCCCAAAGTGCTGGGATTACAGACGTGAGC +CACCATGCCTGGCTGAGATTATTTCTTTTTTTATTATAGCCATTGCTTGTAGATATATGC +TGGTGGTTATCTGTAAAAATGTAATAGAAAGGCCGGGCACGGTGGCTCACACCGGTAATC +CCAGCACTTTGGGAGGCTGAGGTGGGCGGATCACAAGGTCAGGAGTGGGAGACCAGCCTG +GCCAATATGGTGAAACCCCGTCTCTACCAAAAATACAAAAATTAGCTGGGCATAGTGGCG +GGCACCTATAGTCCCAGTGACTCGGGAAGCTGAGGCAGGACAATCGCTTGAACCCAGGAG +GCAGAGGTTGCAGTGAGCTGAGATCGTGCTATTATTGCACACCAGCCTGGGCGACAGAGT +GAGACTCCGTCTCAAAAAGAAAAAAGTAATAGACCAATCTTGAATTTATAATTGGAAGTG +TTGATCCCTTTATTTGCAGAATTTATTTATTTGTGACGCAGCTGTTGCTACCTCGCCTTT +TCTTTTGTTGAGCTTAATCTCATGTCAAGTCATTCAACCAACTCAAAAGCGATGAAGACA +TTATTGAATCAACCTGAACTAAATCAGACCTAGGCTTCTTAAAATATACAGCTTAATGCT +TCCAAATGATTTAGAAAACTAAAAAACCTAGCTACGCTGTAGGACACACAGTGGCCAATA +ATACAGGACCCCCAAACTGGCCAGTGGACCACTGCAACCACTATTTACTTCCTCCGTGTT +TAGGAATGTTCAACGCTCCAAGCCCCATAGGCTGATTCAAGAAGATAAAGTGAGACTCAA +GGAATTTCGAAGTGGAACAATACACCAAAGCCTTAAACCTGAAATGACTCTCCTTTTCTG +GGGGGTGAGGGGGAAAGAAAAAGAAAAAGTTTCTAGGGCTCTCGGGGTGGCCTGGATGCC +AGGGTCCCAGAAGTGGCCTTTTCTAGCTCCTGTAACTAAACCTGGCGGAAAACTCCCCGC +CTGCTCACTCCACCCCCACCCGCCCAAGAATGCGTCTTCCCGTCTTCGGTGGCCCTACCC +AGAATCCCAAAATGTGGGTTCCAACCCGGGCCCTGAATGTCTTCTCAAATCCCCGGGACC +CAGGTTCCGGTGCGTGCCTTGCGTGCCGGGTCTTGCCCCTCGGGCGGTACCACCCAGGCA +GCCCTAAATCCAGCCTCCCGGGCCCCCAGCAGCGCCCTCCGCCCCTCCACTATCCGGTCC +GGCTCGAAGTCGGGGCCAAATCCAGAGACAAGAGGGCTGTGCCTGAAACTGAGCAGTTTC +ACCACTCGGCACTCCTGGCGGAAACTTCCCTTTAAAAAAAAGAAAAGAAAAGAAAAGCAA +CAGCACTTTTGGGCTAGCATTTCAATCCTTCCTGCCCTTTAGAGTTCCCAGTTCTGCTTC +CAGCTGGCTTTGGGTGTTCCACTAGAATTGAGTTGTAAAGATATTCTTTAAGTGTTTATA +GAACATTAAGACTTAAAAAAAATCTTTAAAATTAGAGGAGGGAAAAAGCCACCTTATCGC +ACACATCCAGGAAATGCAGCCCCGTGCATCCCTGCTCAGGGATGAGCAGGCGCCCCAGGA +CTCCCGGAGACAGATTTTTGGGCACCCGAGGGAGTCACCGGGCGCGTGTCGGGGTCCGCG +GTGAGGCCCAGCCCCTCCGGCGGTCCCTTAGACGCGCCCTCTGCCCGGCCGGTGTGGACC +GTCCCGGCCATTGTTTACGGGGGATGCCCGTCCAGACGCATTGTTTTGGCCGTTTCCAAC +TTGCCCCGGCCCTTTCCGGGGCATCGCGGGGGACCCTACACCGACGTCCCCCCTCCGCCC +GCGCCCCAAGGGCTGACTGGGCAAATTGGCAGATCCGCCCCGCGGGGCGACCCAACTTTT +CGGAACAGCCCCCCACCGCCCACCCCTGCAGATCCCCGGACCCCCGCTCCCGGCGGAGAT +TCAGGGAACCCCGCATCCCAAGCCCTTCTAAATCGTGCGGCCTGAGTGTGACGGCCAAGA +GCGGATGCAGCCCGGGATCGCCCGCACCTTCCCGTGGGCGG diff --git a/tests/data/dna/genome.fasta.fai b/tests/data/dna/genome.fasta.fai new file mode 100644 index 00000000..b542e338 --- /dev/null +++ b/tests/data/dna/genome.fasta.fai @@ -0,0 +1 @@ +chr22 40001 7 60 61 diff --git a/tests/data/dna/targets.bed b/tests/data/dna/targets.bed new file mode 100644 index 00000000..54498e32 --- /dev/null +++ b/tests/data/dna/targets.bed @@ -0,0 +1,2 @@ +chr22 1 15000 +chr22 20000 40001 diff --git a/tests/data/dna/test.dna.bam b/tests/data/dna/test.dna.bam new file mode 100644 index 00000000..d0c6a339 Binary files /dev/null and b/tests/data/dna/test.dna.bam differ diff --git a/tests/data/dna/test.dna.bam.bai b/tests/data/dna/test.dna.bam.bai new file mode 100644 index 00000000..ce91243c Binary files /dev/null and b/tests/data/dna/test.dna.bam.bai differ diff --git a/tests/data/protein/genome.gtf b/tests/data/protein/genome.gtf new file mode 100644 index 00000000..d86d9564 --- /dev/null +++ b/tests/data/protein/genome.gtf @@ -0,0 +1,40 @@ +chr22 havana gene 1 2611 . + . gene_id "ENSG00000233995"; gene_version "1"; gene_name "AP000547.1"; gene_source "havana"; gene_biotype "unprocessed_pseudogene"; +chr22 havana transcript 1 2611 . + . gene_id "ENSG00000233995"; gene_version "1"; transcript_id "ENST00000454360"; transcript_version "1"; gene_name "AP000547.1"; gene_source "havana"; gene_biotype "unprocessed_pseudogene"; transcript_name "AP000547.1-201"; transcript_source "havana"; transcript_biotype "unprocessed_pseudogene"; tag "basic"; transcript_support_level "NA"; +chr22 havana exon 1 124 . + . gene_id "ENSG00000233995"; gene_version "1"; transcript_id "ENST00000454360"; transcript_version "1"; exon_number "1"; gene_name "AP000547.1"; gene_source "havana"; gene_biotype "unprocessed_pseudogene"; transcript_name "AP000547.1-201"; transcript_source "havana"; transcript_biotype "unprocessed_pseudogene"; exon_id "ENSE00001732752"; exon_version "1"; tag "basic"; transcript_support_level "NA"; +chr22 havana exon 682 727 . + . gene_id "ENSG00000233995"; gene_version "1"; transcript_id "ENST00000454360"; transcript_version "1"; exon_number "2"; gene_name "AP000547.1"; gene_source "havana"; gene_biotype "unprocessed_pseudogene"; transcript_name "AP000547.1-201"; transcript_source "havana"; transcript_biotype "unprocessed_pseudogene"; exon_id "ENSE00001623204"; exon_version "1"; tag "basic"; transcript_support_level "NA"; +chr22 havana exon 1018 1186 . + . gene_id "ENSG00000233995"; gene_version "1"; transcript_id "ENST00000454360"; transcript_version "1"; exon_number "3"; gene_name "AP000547.1"; gene_source "havana"; gene_biotype "unprocessed_pseudogene"; transcript_name "AP000547.1-201"; transcript_source "havana"; transcript_biotype "unprocessed_pseudogene"; exon_id "ENSE00001597534"; exon_version "1"; tag "basic"; transcript_support_level "NA"; +chr22 havana exon 1396 1501 . + . gene_id "ENSG00000233995"; gene_version "1"; transcript_id "ENST00000454360"; transcript_version "1"; exon_number "4"; gene_name "AP000547.1"; gene_source "havana"; gene_biotype "unprocessed_pseudogene"; transcript_name "AP000547.1-201"; transcript_source "havana"; transcript_biotype "unprocessed_pseudogene"; exon_id "ENSE00001739921"; exon_version "1"; tag "basic"; transcript_support_level "NA"; +chr22 havana exon 2440 2611 . + . gene_id "ENSG00000233995"; gene_version "1"; transcript_id "ENST00000454360"; transcript_version "1"; exon_number "5"; gene_name "AP000547.1"; gene_source "havana"; gene_biotype "unprocessed_pseudogene"; transcript_name "AP000547.1-201"; transcript_source "havana"; transcript_biotype "unprocessed_pseudogene"; exon_id "ENSE00001596670"; exon_version "1"; tag "basic"; transcript_support_level "NA"; +chr22 havana gene 3337 10681 . - . gene_id "ENSG00000239435"; gene_version "2"; gene_name "KCNMB3P1"; gene_source "havana"; gene_biotype "transcribed_unprocessed_pseudogene"; +chr22 havana transcript 3337 10681 . - . gene_id "ENSG00000239435"; gene_version "2"; transcript_id "ENST00000493696"; transcript_version "2"; gene_name "KCNMB3P1"; gene_source "havana"; gene_biotype "transcribed_unprocessed_pseudogene"; transcript_name "KCNMB3P1-202"; transcript_source "havana"; transcript_biotype "transcribed_unprocessed_pseudogene"; tag "basic"; transcript_support_level "NA"; +chr22 havana exon 3337 6047 . - . gene_id "ENSG00000239435"; gene_version "2"; transcript_id "ENST00000493696"; transcript_version "2"; exon_number "3"; gene_name "KCNMB3P1"; gene_source "havana"; gene_biotype "transcribed_unprocessed_pseudogene"; transcript_name "KCNMB3P1-202"; transcript_source "havana"; transcript_biotype "transcribed_unprocessed_pseudogene"; exon_id "ENSE00003755408"; exon_version "1"; tag "basic"; transcript_support_level "NA"; +chr22 havana exon 8785 9261 . - . gene_id "ENSG00000239435"; gene_version "2"; transcript_id "ENST00000493696"; transcript_version "2"; exon_number "2"; gene_name "KCNMB3P1"; gene_source "havana"; gene_biotype "transcribed_unprocessed_pseudogene"; transcript_name "KCNMB3P1-202"; transcript_source "havana"; transcript_biotype "transcribed_unprocessed_pseudogene"; exon_id "ENSE00001839032"; exon_version "2"; tag "basic"; transcript_support_level "NA"; +chr22 havana transcript 9051 10576 . - . gene_id "ENSG00000239435"; gene_version "2"; transcript_id "ENST00000472972"; transcript_version "1"; gene_name "KCNMB3P1"; gene_source "havana"; gene_biotype "transcribed_unprocessed_pseudogene"; transcript_name "KCNMB3P1-201"; transcript_source "havana"; transcript_biotype "processed_transcript"; transcript_support_level "2"; +chr22 havana exon 9051 9261 . - . gene_id "ENSG00000239435"; gene_version "2"; transcript_id "ENST00000472972"; transcript_version "1"; exon_number "2"; gene_name "KCNMB3P1"; gene_source "havana"; gene_biotype "transcribed_unprocessed_pseudogene"; transcript_name "KCNMB3P1-201"; transcript_source "havana"; transcript_biotype "processed_transcript"; exon_id "ENSE00001878881"; exon_version "1"; transcript_support_level "2"; +chr22 havana exon 10481 10576 . - . gene_id "ENSG00000239435"; gene_version "2"; transcript_id "ENST00000472972"; transcript_version "1"; exon_number "1"; gene_name "KCNMB3P1"; gene_source "havana"; gene_biotype "transcribed_unprocessed_pseudogene"; transcript_name "KCNMB3P1-201"; transcript_source "havana"; transcript_biotype "processed_transcript"; exon_id "ENSE00001937172"; exon_version "1"; transcript_support_level "2"; +chr22 havana exon 10481 10681 . - . gene_id "ENSG00000239435"; gene_version "2"; transcript_id "ENST00000493696"; transcript_version "2"; exon_number "1"; gene_name "KCNMB3P1"; gene_source "havana"; gene_biotype "transcribed_unprocessed_pseudogene"; transcript_name "KCNMB3P1-202"; transcript_source "havana"; transcript_biotype "transcribed_unprocessed_pseudogene"; exon_id "ENSE00001930505"; exon_version "2"; tag "basic"; transcript_support_level "NA"; +chr22 ensembl_havana gene 18725 20784 . - . gene_id "ENSG00000198445"; gene_version "4"; gene_name "CCT8L2"; gene_source "ensembl_havana"; gene_biotype "protein_coding"; +chr22 ensembl_havana transcript 18725 20784 . - . gene_id "ENSG00000198445"; gene_version "4"; transcript_id "ENST00000359963"; transcript_version "4"; gene_name "CCT8L2"; gene_source "ensembl_havana"; gene_biotype "protein_coding"; transcript_name "CCT8L2-201"; transcript_source "ensembl_havana"; transcript_biotype "protein_coding"; tag "CCDS"; ccds_id "CCDS13738"; tag "basic"; transcript_support_level "NA"; +chr22 ensembl_havana exon 18725 20784 . - . gene_id "ENSG00000198445"; gene_version "4"; transcript_id "ENST00000359963"; transcript_version "4"; exon_number "1"; gene_name "CCT8L2"; gene_source "ensembl_havana"; gene_biotype "protein_coding"; transcript_name "CCT8L2-201"; transcript_source "ensembl_havana"; transcript_biotype "protein_coding"; tag "CCDS"; ccds_id "CCDS13738"; exon_id "ENSE00001806026"; exon_version "2"; tag "basic"; transcript_support_level "NA"; +chr22 ensembl_havana three_prime_utr 18725 18850 . - . gene_id "ENSG00000198445"; gene_version "4"; transcript_id "ENST00000359963"; transcript_version "4"; gene_name "CCT8L2"; gene_source "ensembl_havana"; gene_biotype "protein_coding"; transcript_name "CCT8L2-201"; transcript_source "ensembl_havana"; transcript_biotype "protein_coding"; tag "CCDS"; ccds_id "CCDS13738"; tag "basic"; transcript_support_level "NA"; +chr22 ensembl_havana stop_codon 18851 18853 . - 0 gene_id "ENSG00000198445"; gene_version "4"; transcript_id "ENST00000359963"; transcript_version "4"; exon_number "1"; gene_name "CCT8L2"; gene_source "ensembl_havana"; gene_biotype "protein_coding"; transcript_name "CCT8L2-201"; transcript_source "ensembl_havana"; transcript_biotype "protein_coding"; tag "CCDS"; ccds_id "CCDS13738"; tag "basic"; transcript_support_level "NA"; +chr22 ensembl_havana CDS 18854 20524 . - 0 gene_id "ENSG00000198445"; gene_version "4"; transcript_id "ENST00000359963"; transcript_version "4"; exon_number "1"; gene_name "CCT8L2"; gene_source "ensembl_havana"; gene_biotype "protein_coding"; transcript_name "CCT8L2-201"; transcript_source "ensembl_havana"; transcript_biotype "protein_coding"; tag "CCDS"; ccds_id "CCDS13738"; protein_id "ENSP00000353048"; protein_version "3"; tag "basic"; transcript_support_level "NA"; +chr22 ensembl_havana start_codon 20522 20524 . - 0 gene_id "ENSG00000198445"; gene_version "4"; transcript_id "ENST00000359963"; transcript_version "4"; exon_number "1"; gene_name "CCT8L2"; gene_source "ensembl_havana"; gene_biotype "protein_coding"; transcript_name "CCT8L2-201"; transcript_source "ensembl_havana"; transcript_biotype "protein_coding"; tag "CCDS"; ccds_id "CCDS13738"; tag "basic"; transcript_support_level "NA"; +chr22 ensembl_havana five_prime_utr 20525 20784 . - . gene_id "ENSG00000198445"; gene_version "4"; transcript_id "ENST00000359963"; transcript_version "4"; gene_name "CCT8L2"; gene_source "ensembl_havana"; gene_biotype "protein_coding"; transcript_name "CCT8L2-201"; transcript_source "ensembl_havana"; transcript_biotype "protein_coding"; tag "CCDS"; ccds_id "CCDS13738"; tag "basic"; transcript_support_level "NA"; +chr22 havana_tagene transcript 20869 28084 . + . gene_id "ENSG00000287285"; gene_version "1"; transcript_id "ENST00000656324"; transcript_version "1"; gene_name "AP000547.4"; gene_source "havana_tagene"; gene_biotype "lncRNA"; transcript_name "AP000547.4-201"; transcript_source "havana_tagene"; transcript_biotype "lncRNA"; tag "basic"; +chr22 havana_tagene exon 20869 20949 . + . gene_id "ENSG00000287285"; gene_version "1"; transcript_id "ENST00000656324"; transcript_version "1"; exon_number "1"; gene_name "AP000547.4"; gene_source "havana_tagene"; gene_biotype "lncRNA"; transcript_name "AP000547.4-201"; transcript_source "havana_tagene"; transcript_biotype "lncRNA"; exon_id "ENSE00003870517"; exon_version "1"; tag "basic"; +chr22 havana_tagene gene 20869 28084 . + . gene_id "ENSG00000287285"; gene_version "1"; gene_name "AP000547.4"; gene_source "havana_tagene"; gene_biotype "lncRNA"; +chr22 havana gene 23063 23459 . - . gene_id "ENSG00000240122"; gene_version "1"; gene_name "FABP5P11"; gene_source "havana"; gene_biotype "processed_pseudogene"; +chr22 havana transcript 23063 23459 . - . gene_id "ENSG00000240122"; gene_version "1"; transcript_id "ENST00000430910"; transcript_version "1"; gene_name "FABP5P11"; gene_source "havana"; gene_biotype "processed_pseudogene"; transcript_name "FABP5P11-201"; transcript_source "havana"; transcript_biotype "processed_pseudogene"; tag "basic"; transcript_support_level "NA"; +chr22 havana exon 23063 23459 . - . gene_id "ENSG00000240122"; gene_version "1"; transcript_id "ENST00000430910"; transcript_version "1"; exon_number "1"; gene_name "FABP5P11"; gene_source "havana"; gene_biotype "processed_pseudogene"; transcript_name "FABP5P11-201"; transcript_source "havana"; transcript_biotype "processed_pseudogene"; exon_id "ENSE00001596581"; exon_version "1"; tag "basic"; transcript_support_level "NA"; +chr22 havana_tagene exon 27394 28084 . + . gene_id "ENSG00000287285"; gene_version "1"; transcript_id "ENST00000656324"; transcript_version "1"; exon_number "2"; gene_name "AP000547.4"; gene_source "havana_tagene"; gene_biotype "lncRNA"; transcript_name "AP000547.4-201"; transcript_source "havana_tagene"; transcript_biotype "lncRNA"; exon_id "ENSE00003867293"; exon_version "1"; tag "basic"; +chr22 havana exon 29861 30189 . + . gene_id "ENSG00000100181"; gene_version "22"; transcript_id "ENST00000558085"; transcript_version "6"; exon_number "1"; gene_name "TPTEP1"; gene_source "havana"; gene_biotype "transcribed_unprocessed_pseudogene"; transcript_name "TPTEP1-205"; transcript_source "havana"; transcript_biotype "processed_transcript"; exon_id "ENSE00002570186"; exon_version "1"; tag "basic"; transcript_support_level "2"; +chr22 havana gene 29885 40000 . + . gene_id "ENSG00000283633"; gene_version "1"; gene_name "AP000547.3"; gene_source "havana"; gene_biotype "lncRNA"; +chr22 havana transcript 29885 40000 . + . gene_id "ENSG00000283633"; gene_version "1"; transcript_id "ENST00000592918"; transcript_version "5"; gene_name "AP000547.3"; gene_source "havana"; gene_biotype "lncRNA"; transcript_name "AP000547.3-201"; transcript_source "havana"; transcript_biotype "lncRNA"; tag "basic"; transcript_support_level "1"; +chr22 havana exon 29885 30189 . + . gene_id "ENSG00000100181"; gene_version "22"; transcript_id "ENST00000400593"; transcript_version "6"; exon_number "1"; gene_name "TPTEP1"; gene_source "havana"; gene_biotype "transcribed_unprocessed_pseudogene"; transcript_name "TPTEP1-202"; transcript_source "havana"; transcript_biotype "processed_transcript"; exon_id "ENSE00003795523"; exon_version "1"; transcript_support_level "1"; +chr22 havana exon 29885 30189 . + . gene_id "ENSG00000283633"; gene_version "1"; transcript_id "ENST00000592918"; transcript_version "5"; exon_number "1"; gene_name "AP000547.3"; gene_source "havana"; gene_biotype "lncRNA"; transcript_name "AP000547.3-201"; transcript_source "havana"; transcript_biotype "lncRNA"; exon_id "ENSE00003792889"; exon_version "1"; tag "basic"; transcript_support_level "1"; +chr22 havana transcript 29922 40000 . + . gene_id "ENSG00000283633"; gene_version "1"; transcript_id "ENST00000592107"; transcript_version "5"; gene_name "AP000547.3"; gene_source "havana"; gene_biotype "lncRNA"; transcript_name "AP000547.3-202"; transcript_source "havana"; transcript_biotype "lncRNA"; tag "basic"; transcript_support_level "1"; +chr22 havana exon 29922 30189 . + . gene_id "ENSG00000283633"; gene_version "1"; transcript_id "ENST00000592107"; transcript_version "5"; exon_number "1"; gene_name "AP000547.3"; gene_source "havana"; gene_biotype "lncRNA"; transcript_name "AP000547.3-202"; transcript_source "havana"; transcript_biotype "lncRNA"; exon_id "ENSE00002911043"; exon_version "1"; tag "basic"; transcript_support_level "1"; +chr22 havana exon 30018 30189 . + . gene_id "ENSG00000100181"; gene_version "22"; transcript_id "ENST00000426585"; transcript_version "5"; exon_number "1"; gene_name "TPTEP1"; gene_source "havana"; gene_biotype "transcribed_unprocessed_pseudogene"; transcript_name "TPTEP1-204"; transcript_source "havana"; transcript_biotype "processed_transcript"; exon_id "ENSE00001675045"; exon_version "1"; transcript_support_level "1"; +chr22 havana transcript 30024 40000 . + . gene_id "ENSG00000283633"; gene_version "1"; transcript_id "ENST00000591299"; transcript_version "1"; gene_name "AP000547.3"; gene_source "havana"; gene_biotype "lncRNA"; transcript_name "AP000547.3-203"; transcript_source "havana"; transcript_biotype "lncRNA"; tag "basic"; transcript_support_level "2"; +chr22 havana exon 30024 30189 . + . gene_id "ENSG00000283633"; gene_version "1"; transcript_id "ENST00000591299"; transcript_version "1"; exon_number "1"; gene_name "AP000547.3"; gene_source "havana"; gene_biotype "lncRNA"; transcript_name "AP000547.3-203"; transcript_source "havana"; transcript_biotype "lncRNA"; exon_id "ENSE00002800237"; exon_version "1"; tag "basic"; transcript_support_level "2"; diff --git a/tests/data/protein/protein_mini_with_cazymes.faa b/tests/data/protein/protein_mini_with_cazymes.faa new file mode 100644 index 00000000..69b7e895 --- /dev/null +++ b/tests/data/protein/protein_mini_with_cazymes.faa @@ -0,0 +1,136 @@ +>QEG36041.1 +MSTDSSESSCPEEVYRSKATICVALYSFDVGGSERLGLDLVKYYAEHGANVVCCATRRGL +GPLVSIVKSLRIPYLALDLENRTRFGRALSRFLLMRWLVNHRVTCIHAQHFSVYADVHAP +AVAAGIKNRIVTEHTAEPLLNDRKYAKLTARFAHKATSVVAINQVVKDALCHVSGIPASD +VLLIENGIDTRRFTPGDIRSSGQIRIVWMGRLHPDKDILTALSAFQEATIATNLELRLFI +VGDGQERKKAEKFVDINNLGTKVVFEGELNDPLPILQQADFFLMSSRTEGTPLVILEALS +CGLPVVATAVGGIPNTITEEVGLLAPAENPSALARCILILAKDRELRLKMGKKAREIAEF +TFSVERMAKTYTKLYANLE +>A6QXU0 +MSIITEIQGDLFDAPEGAALIHACNCQGSWGKGIAATFKEKYPAAYRIFRSHCQQYLSHP +QTWTQTQTSRQQSRAFKLPEGTALIIPPQPADYQPQPQPQSQTAPLSNCGRGRGRGRGRA +GGGALHNSRELTALSRPAGKKHWIICLFTSWHYGRWSRSPPDIILENTMSAMADLKRQIA +AAAAASSTTSPATTTTTTTALAATGGCEEEQLGELWGCRLNAGLFEVPWERTKAVLEEAG +LAVTIVQPPGSGYE +>ATL15305.1 +MTLLRDLLLLYINSLLFINPSIGENILVFLPTKTYSHFKPLEPLFQELAMRGHNVTVFSG +FSLTKNISNYSSIVFSAEIEFVNIGMGNLRKQSRIYNWIYVHNELQNYFTQLISDNQLQE +LLSNKDTQFDLIFIELYHVDGVFALSHRFNCPIIGLSFQPVLPIYNWLIGNPTTFSYIPH +VYLPFTDIMSFWKRIINAVFSIFTAAFYNFVSTKGYQKHVDLLLRQTESPKLNIEELSES +LSLILAEFHFSSAYTRPNLPNVIDIAGIHIQSPKPLPQDLLDFLDQSEHGVIYVSLGTLI +DPIHTDHLGLNLINVFRKLRQRVIWKWKKEFFHDVPKNVLIGEWFPQIDILNHPRCKLFI +SHGGYHSMLESIYSSVPILGIPFFTDQHHNTAIIEKLKIGKKASTEASEEDLLTAVKELL +SNETFKRNSQHQSSIFRDRPMSPMDTAIYWTEYILRYKGASHMKSAVIDLYWFQYILLDI +ILFYSLIVLILLCILRIFFRMLTK +>QIL38354.1 +MALKRIIFGLITISSLSLQAQESAIWQDFKTAKSTGVTPVLPDFSYAGYRYSEEAIPTVN +YKVFDVTSFGAIPNDNLSDKKAFISAIAAAEKNGEGIIYFPRGRYLFNTANDDQQVIRIS +GSKIVLRGAGNGEGGTVLFFDKDLPPANPKQMWSVPMAINIGAKGANKKLADVTANAPRE +SHIIEVSNASKIKAGDWIILEVTNNSPELIDYDLKGIKPDTSWASLIKKGVQVNERHQVA +KVEGNKLILVEPIHYDVQAKHHWTVSGFAHLSEVGVENIAFEGNWLKKFVHHRSAQDDSG +WSILSLNKSVNSWVRNCSFRNVSNGLTIGASAACTAIDLVFDGNMGHNSVDAAGGSTGIL +LANITDLTGMHHAVGVGGGSTTATVIWRSRYPENTCFESHSSQPRCTLLDEVTGGLSDGR +AGGAIFNMPNHGRNLVLWNYKQLGAARKNFEFWPAKSIWWKIVPPIIVGYHGAETTFNKD +QVQVLESLGKAVQPESLFEAQLELRLGKLPQWIAEEKKKIKAQDILAEK +>Q21SX1 +MSQIGINDGASQQPAGTTLGGVAARLGKVAVLMGGASAEREVSLMSGQGVLQALISQGVD +AHAFDPAERDLAELKVEGFSHCFIALHGRFGEDGTVQGALELLGIPYTGSGVMASSIAID +KVMTKRIWRSEGLPTPEWRQVDSAAATSEAFAALGSPMIVKPDREGSTIGLTKVTQIEQC +GAAYALAARHDAMVLCEQFVKGDEVTIPLLGSGAGAHALPVIRIVAPDGNYDYQNKYFTD +TTQYLVPANLPAGEEAHIQQLALKAYQVLGCRGWARVDVMIDARTRAPFLLEINTSPGMT +PHSLVPMAAKAAGVSYPALCLEVLRHATLDYAAAGGDAQQPSTEPA +>QGH63253.1 +MNITLSLIIPVYKVEAYIEACLCSVLQQLPDWAEVIIVDDGSPDDAIGIAEEVLCRFPQH +KLRVSILRQQNQGLSEARNSGIAHAQGRYVGFLDSDDVLLEGYFSILGRLLADNPQADIV +AFNAQRFSAFNNGKIQPDGTLAIVPGNAAPQQRDAHMALLADSFNRSLWFAWARIYRKTL +FDQARFPAGRNFEDIQLIPQLYLKAERIVVCDTPLVGYRSNPNGITRAPKRRDLDDLDYA +LDGADTGRREGVGHGLYSVLFVTTLKARLLVGLDFFGLRDALRETRALKRRYSGLRAEER +KMLSRKNRLFYRSPLAYYLMARLYNLRVK +>AHB32714.1 +MSSQNYISVGIPIYNASAYLEDAIKSVLAQSFQNFELILIDDGSTDDSLKIAKSFNDSRI +RVYSDGLNKKLPTRLNQIIQMAKYDYIARMDADDLMDVDRLKNQFEYLKNNPNIDLVTTG +MYSIGKNNEILGKRIPNNKMMTASEILGGITNLLHASMLARKEWCLRNPYRVDNALAEDY +ELWLSAAIKNDLKYTVIQEPLYYYREIENVKLDKMIKGYNTQIDVINSYYNGVIDKFEKD +DIIKKFELKKKIVKLLDALNLMFILQRRRVQKVSNIDIEKYKTNLYKIYNVGGDNE +>ANU40728.1 +MKQHKRLPLALFLVFALCIQTLSFTSAAAALADPEPVFSASDMTFSSGTDAVALPQSAVT +QLSGLSSGTIIVDFTPTAMSTANCLFSLSNSSVTDAYFNLYIDNRGFLGLEVRNHGETRY +VNLQGPAEVTRNTRHIMALSADPQEGYKFYFDGDMVFQMPVALYELWEYDYRFMSTVNSA +DVGYMGATYRNGSFGYPYYGTIDSVRVYDTALAQDVLEAETYIEKDSEIIRQENVFSFED +WNTEGIRIPSILRTDKGTIIATGDIRFGDAAGASNDPPNNCDIGIRVSSDDGATWSQPKM 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BzuI/59XABek3ra0RzuB/x1frUj/2yLaw4jP8KdaySnaX70iyEmxjoQ+Kn/fBZ+l9Brmhe3cmU/168X/F9tM1rsqTwI3NWc7vEfIEKnfBoXMh2MjmYcPugT/IxX7gy6tVRjW6+G2rKs3mEe/Gngc+KMeZPyhLVsHfya65aM+LPe/g3XSDi/GFR9ziZsXK3CvbFLMuOXXZ8i39E3TaFezerJe9mcI/ca///m/kjPAd60FcUrrSruT6mXgXcK9+bhXzPIhLvWIZ9zx+hz4oEX1o59+3w/vU6/sJ/5ctQN/lNRy+FExcZQf6MoyIN/fbMA9ap4b/41WuiNe0tddZty0y4F+te4B8w3XXMbfLZ/wX2t7Z753z1fqwcFRjA8XvcI7JKun7C+/wvqcwQy+S50JfsoFHeAnhPDPxPuKS2w9NP6rLYqAvug9lyOOkaeeBz26Z3PqdVEK1uliGOmMN4EdSYUOrFNecCOfbWxY14tsCP8u+ltDPxWjfOxXqw34r1hpivu18H2wXzl1M+JXYdUI4m1Vf8ZpZQdxjhR3n7hwcyrPazsW8lL1KuCOZpkPHNWfmlC/TOzpP8IqcL7wIIl+pcqLdZe9jDu062kcN9nI90Rk8/+jZn4M6Eo8AryTousYlxSUUR/OBhK3o9KRnwgZUdAv3Wog9SeoGeOfmHTEi6pkBjuUPl0EvkgG0YwTlYP032Nq+J/09RfGpYu8Gc+tvsb64PwHxJHTPckn7zzGlfdWg4/CbxN47uHOwD8t0hd0KG+e0t9Nes74c9AZF6wvNeV/aNFQyEOMG9Md4+YhjFsGCJC7bmOM/yV92kPa/dIi4sT0ZP5busuQq3bFjXHiku+0z2GX2AY7U5/7/pfnlj+BfuhplvQTrm8pn0lOzAdLFtAu8qtZP61cwH/NZgXEOZerkLfQ9BvepfXvxP+K2T9xfvJ6tOKR32gnW0cxzrjVG3xSLmzkv/CkVuCjatgEeCkOMyYevN/O/HXkEOr7/iT+5xjl0b8d8EecIslRkJdaf18f6f/zTgCJ 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eJwt1ntczncfx/HfKrmRY8Imdm0OOYRt5Rx+lMOyFknI6arUshAVIYcfSUqFxC0tXSjMkmPdc2h+RLY27SY5li5STovNaUbNfe/z6q/n4/35vL+f35X9M0VRTGkBs1VFUYz3rb/+R1NMJ1F3cheNA0NExRjL/sRB5o2jQv5RHWCYI/OBrqIyL1g0Bx4nuz1lP6XzXJmHjRSV4tn4MFY03N9N3nxO1Gvui1qA5Tx517cbfhYgKr6xopq2n5xahI/vi4a0FqFyp9ZB1Ea7iar1XuYLCkXl/QcYbjlf9us/FpULY0RTxATmJf5kt3D2nsmitnEnuedheubzzINeiIZgqwWi2k3UCpzJca6i0tRbNA4JEPXccOaj14mm7Sm8S62h95VdmOxzPxON00aJekMvUf3Vn3mjSHp1q0Xzjc2i4VCpqHneY9/4T/ru1uFyJ6krBo0WjY5Boik+QlSKtrL/MZvcs4Q8sFJUc57jLqsIcfv7orm8i6hU9xON/q6iFjlBNEz0I4+ey7u5S+ifWCvqBSncGVXIPu8a/aFV2F9ZKHeTbEXVqgt5ZH9RL/Fi3jJYNM+PxDOxojI8hb5hF/n8EVGz/kk0VN2ln/KMO6UWi6QX3Uo0RXQWtfHO+FwV9Ute9Lb50wudKxpvxIhq5Bb62l5y9zz6jc7zPraUd+2qyWnP6Ds0iJR+gZ2o/+EgavMH4p5R7PdPEc2bZolKcRjzO6vIbtvJk7JwaB4mFrI3lOC+Slz6knupDReLN9uISteuov6qj2gcNhozfEQ1M4icFYE5Mbzft5lsnSlql3Iw+nvR0O4C72uquB/3gtzacol816kNxhtE/XQfUb07QjR0nyyabQLpJYWzt4omlybTO7uL3rVjmHpeNM67xN0GZnp93vAuzm6p3Pmku6j59RP1EFfRfNpTVHYZyYPn0DOsEY3h/xYNC/cyb30MR57l7tQS3j8tp2/xiDziFfce2UZJ/6ePRWVoH9FwcBjzrEm4agFOX0mvS7Kor/5GNH+7R1RtjtLLOEOvukw0+jwQTW0tl8n9IFvROKybqD0eSs4dK5qrJ4umZ7PYZ84Tleka799LFvU6E7niCP3sC9yJviqqRdUY9op+dsPlcu9JG1FP/Fg0fvIp2X+IaHacJppaBKNfFD2fjbzvl4Fph/DeKVHp8CP9H26QPR6iw2vuJ1mvkH1YJ1F94UQeoorK9+NE/aaReZNI3LIW0zPppRzBqQWiMecKe28zvnvNnV7/Wil/j0U70VTRQ1R8+4tqi+Hsg7zICQH0mi4TjUoiufk23pkO06+4gOtu0jv0UNT//os7mrUm/aKmojGrNTnCXtSPdhINU3uyt3VibzNI1CLGi+qqyfTO+ZFvzsaEcO4ULBHNK1cxz4sXTaHJvGu5nbtPMvjOtX307x/k/bfH6Y8t4LvXf8FeV5jbldNbXoUtHnOvz+/ccXhN37sOr1ivkt/xaVNRWWkn6pfsmSd2Jvs7khVntBlKv9ZVNMe6Mx/uLWovZmC3IN5bhrGPWULusZp9kySyTQomp+KhHew7ZIrGyBzRcPAY35t0hv3Hv9D3K2XvV87v+r6S7x15RA75g/7iv7j3p8VqsVljUR31vqjlGsgZ3URDp09F3Xcw83fDmd/xYF7nvZr703kf+hV5/hz6IxfRN6xmvzCB70ZtoTfpG3JmFvfGHaA3Lo/3bU8xTzqLGb+wb3WNfXgZd5pU1f8dT8mbXnL30Ft0sIiWeW1DUfW1FfUDBnLT7qI2wwmvu7AfNJJ3n3mIhhUT0XI682az6Deawzw+jLxnCXvXGO7UJjI/kkKvxw6+a53FfnMOOuViaD5710LR+NtF3nW/KpprbpE977Of9hvv+r/k3Yl36Ge9Rn6HS3NRz7QTzeM6iAafzuyv9RJVr76iUR0qajlj6LWdwvuJfsyvhHAnMBzDl7Evjube9UTe9U5jHryTu3Xf8Z0LR3HNKfrFhTiiGA9dxWdV3Kl4xneH1OIwqxi5m9pYVGqbi+rktqIx1iAaJvUQzb/2oV/Ql5ztQn/D57y/P07UDkyit3Y68/4h3PsgjP62peRtMXhnPT27ZLTZxnc90slF+8kND3P/2H/I+ae49+4cNi/hu1/cJF+r4H3iA+b7n/G+9DXzunfMNzVYK/O8Zmj+QFTTP0Kvbri7N05xpmfrgjtGibq9J/sRvqLycwB7ZTa5fD770khcthwDE9iHp9Mv/w7fu8jdcVfYf17GPOAe+e4T9jPfMG9pGSs5tpGofWcrKi/smb/phNaO7IOdRLWrC04ezrxiLE7x5n3pVIwLoJc/h9wonHszo+iPjSYvXo+1m3FYGvu/dvPuQDZ3PjiGf59if+Fc/bufmW+8Wv+uknf/ek62qSW3sFoneZQNtm0j6sb25LOdsJsjfuTM3taF3MQLtan4NoT9uwhyxHJRbRrDfE8C1qSwt8ogV+6mtzdbVByPsf/mJPPDheSgYqwr492bF+yTa5lvs4oTB9rgSzs8Yy8qrR3JmU7kS4Pj+Pd2w3Ye2HsiOk4T1cLZvGseQQ5dTh6SQC5Nwbnp3J2eyfvJBZh2lb1jef27KuYZNZhsHS/7kib4wA6tOsaz7445zthlJG6aLCqBgegThQ/XippzIs7bzL0V2/GbLCw/yh31FL1L58hfFeGrK/W929x1vofrf6Pf+CX7hDr6LRqsl/2uJuhoh8/tRX1DF1HVHJmf60vuOISc7kYe5yFq27yx+XTmM4PI40LxaATzG8t5vzaG7xgT0G4rDs+gNyeLXJVNbpXLHc987HWe+eXL9PaVkx/cY29TQ279ku8NqKU3zCZB9lGtReVre3L7zqI+wxlDXXC5Gx7woNfVm3fBM8iNAvFwCL3xEbhhOX4SI6prEug12MLcLR0b7GHf+wB7z1zytnysKaS3+SJ5wxWyqYzf8fAe+fpb3j+3TBRDm4pqrJ2oe34oKrPc2M8az77MBx/OrZ+vQcsE9E3h3cE0en77yPEH2G88hk4nmO89R577M/n4ZfKzMn7Hf6u509s2Sebu9jigi6jmO+LA/swzh4j6Km/mRdPRMbD+XSh54zLyxvX0W+4kd96Dx7KZv86lP+Y0eXQhRpeJStQD9kueMs99ybxJLR632iA2aCHqQ9tidgfms7riYkfx//9/KKrbBzKf6Ua288B+PrzPD2c/ZhnvOsSQbydgyRZ6P5vYa/t4f+kk/vUrnryKVmU44DEWveJOSh1utNj4j6Ysa1HJa4xezUXjt61E8+h2oqa0F/XWHek17Mr7gh6iauotGiZ8xvu4vuQ/BtP/aRj3bF25N2UUvdtjeX/3S+Ydveh9Mo397zPxkT8WBNFfE0K/fRi/Y+Eifl/lCr73di15Qjy9uCTm2cn4Yivzxmnk/bu583hP/Z0cvqOdZr6jiN7MYn5fgxL256+zf3ObvzeymrspT+j7/8n+xzrufmGxSeZFjUXDty1FNceOeXF70VTbkezWSTSPdyCX9yBPGkQvaSjv37qK+s5RorG7O/t+HvXvxmPfifQe+fLuyQw86s/eOpg8IpTvNA3nzppFaKFx//RqUVscQ14Xzz43iffRW3mflMbcKoO/99ZO5pf3Y04O3z11hN6dPN7PPsHd3/PJPoWYWkzfpYT3z69yd+lN5vsr6OXd5Z57Nb9z1TP6ma/oR74h176XLL2LDUV9QRPRfKCZqNS0Eg1R7TDOnrnHh6J6shM5wAFLezAf05t8sS+6DuT9Ihe+N2IYvZsj+J67O/N9X4rGNhPIaVOx0Uz6twJE7V0Qv7fma+ZTQ+kNXsT3IqPola9gf3A188+TmH+ZzPt2W/ld91KZRx2lt/Qiv2NZKXfP38JeFptlPuAj0fyhg6hf6Cuq6W6iaYi7qM2fJBpO+mL1V/RHzuHO8XncuRLH/OwGUTmYzP6LNN5572Cev4u87zvePcjhXVoec8+T/A7f0+So89wJL6JvW8Lv6neNd4Mq2Deq5Hf3vI8zatjffUq/7E96P/yNIZYpsg+2Fo27mpENtqLhhx7MPfuQvZ3Iv/YXlVODyOkj2P/oJap1PtxZ4Mu8MpXe0R3MKyvIh6vw4mNRq7PdIt5dJKqtokT9at4W/jtZbRUHNBeVwDFb1f8Bu/N2eQ== 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b/Ip+1vTujft6eCM+V4JLWA/9G4W8XCGYJwUW3EO/XGsG/lNvkO7RENPOeIfvmd7roT95M79DSinYQvaW0Wfrw76EXpoo6byf46yveyzn3kjj9TMNcSTrjOYH2WVrD8X5kCueJEDfsOyJ+JDtaX+wrMu9FTmpCFftdH7oJeydTfxqNkd1rUB30CePFHO+Dr+mnnzYDHztrPJPmGmPfSVHVivxJ6tfFcsbYx7mTl7+A4Q+JDvCH7P2K/H3oTd9bhc5K06N47/GxwrYB3y/on9XVw98Cnrs/i+fy0CfhEBs4Ar+sPr7HcPT+Oc+Zb+kLmFyDeZcYJx9tUx4KZY+Jy4/C4T++TZGuZ95gPmT8gI2slYzLlotcb32VMqccorDfYw19g1Ff8FN3SbwA== 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eJwt0Hl8TXcexvETdNRYS2hFxS2xxVJNCCUjh9zSWmut3SXWEFsSKoJjCZHFksTWS12NxDrU0lLrYSxDEbFkqhVuJRND0urY2hclM30+/ev9er7f5/c9NzEMwzXbGWUa//ddnyl/aKZmSOvdbdLxyTXp6VFh6h8aoROk55tEaQ7bKu2S8+xblkjvO85pml8aK802M6RlLJTG3S2Ye0h62lWcru8t9MP1jaV1sLP0tOwtzdho6V1pSaNNCvYJmKHe/NbSKNtHulqNmcHfFYMh2dLudBLnXaPXoYB3WT7Rul+umrTcTumpOADjR0szIFoaKYvIM9PY53yJSRd5v+tHcv0n0g4ppf9WgxjlmeHYPkJadaOk8WqR9HZex/7AWfYv83Bd2Vj5yFcaxxvjoyxp3j5AHlVvplzbSlqe/thhtTQ3Z7Hfc0jaU87g3hvS1b+YfLjcLPUifKVpJmLpJub3DkirzknpTcgl9y6UdpUn9Ccan8qc96Q1aJD0tFkr7WfbpCvhDF7Jo5dbLB3pT3n/VfnZyruDpflRF2kcGSCthzHSLllI3pAmXa++kF73ftx0jF69b7kT9B294fd4d2hgnPbrJ0izYZw0cpdI64yb7D6AdX+T3sc15khHgLRGBEs7tJs0kgaSr43HC7EYtgTLuXm3+ow0Z9/gXWnNeN11NpaOnu2kMa+ftObGSvNYwp/zNfRqbpeu4LPYKVfaB37kXeWH9I0yc/V+TjXpuhSEPcOk8Xw6ecFeejknpB13hX2He9L7s8883T1XS3r7NZHGtvelXeCUjsO9pFk8SXp8UrHBRubjjnCn4gXpWpmHS4rofeg/X/eym+IDU5qBc6XxYBnz3dvwy6+l54Mb0lu9kH6nJ/Rj/2Ipn6gk7TkB0vINlI7AUPbucPZbu5PPfsz+30OZJ46SRuVI8i/R7D/OoL9vB/uZe7l/zsawy/TuXyPH3SLvLOJO8hMsW4ptX18g/1MNG9aWXp9m0igIJg8NldZmpzSr98IxQ9kfGkPOncy7mzOw5xze1VokPbOW0TuRgcnbsOt+vr/jMP12pzH6AqZfYd/sJiY9Zp74gu+/6b9Q86+aSteENtJwdiCHdCa36i4d5ftJq+JIaZaNJ99OII9fQb/gMFY6w/1yF8kPb9C/f4v+wLv4zSN63cstUj5aUdr9akojua50xAeQ/9aW/bBQ8qfh5KrdpKtZb+ZDhuL+UdxdNVFa+THYNp7+jUXS+990vlNlA/v4bJz+d967r3MvKJ/8WSHfvVLCnSHPmYc1XCxrvCvtq+2kERkmvZO6Ma/TV1rBw5mvHCtd30Yxn7OEXp9U3qdmYFc3/TAP+cQO+i8P0j92nO8Hn2a/MZe7vW7Su19EPlVC7vGMfvMX5GlGAr+rAr5fTXr/4SddRU2YF7fGrDD2RU7yuv7kVUOkGTmad80jsWkM8+5x9DfMp/94sbTLJkvj8/X0zM2YuZV5xm7y/OP0m56WjvIX2Xe6zr1X33N/mpd51SLebfqJXOEZBr6+RHeCqknH1NrS9DjwTiPpvdlSWpm9mJsDcMFw5i0nSGNlFP3Ls9jPmMt9z2Lyo3R6Ldczd28hL9/J/u2jzENOYa1bzAcX0htTwvcCnpJzXlsqg9+Q5gM/aS9/h7nZVFrZwXi6PY4x8buu9Or1IhcM5k7NCO58EMV8Xwy99XPZv1zMPnE5rl1Db5WbXu3N9C5ls9+3Dy8dxkbn6G3J513JXeYTSzDwV94Xv2T/1muJyoMqYfM30a4n7Yzm5NPB5Cah0hoZjt26S+NaP3pDhtN7bwz7BpOZ+8X8uY/HHgnYNgV/S6Oft4n3lbK4++Z+snmU3tPzuPMKc59b2LSA91NKcM8T3h+tskz9i77SGO0vrczm0vYNYj/RyTy8J3lXFrnPfnLudd4H3ObdiZfsc+KStJ+6GOum4O+rMaAYR/41We8La0ozor+0s6cyvxNDHrgWd22U1qp/kp+Xkkc2SVEe1UMao/pLM3UY8w/HYuAkad3MJD89QO/QUXLmNfbf/UBuVsi9myX07j3BkPqpmg8NktbDjrjVmcr/oYc0Kw9kvjOCPGoye0cs8wmrceou9qf3kV1HsfNVDCm3XN9rXXk5v6OOtO8EoE9r3BJKb7yTfDAK98Qy77gY81O4syBdWiPcf86/x96+K7Tf/jaWD5DGzubkMp9Ie/wwXBLB3BmNlTfST95Krp6HafnSqltETviJ9+G/M79Qd+Ufeh318WywtLqESHtFe+lxdmTfdYR0NBuNVcfRXxpJLpwtzcy5mLyAfY10cspn3Bmczf1+O6Rr5m6+k7+P3tQc7oVfZT/rX9zJukvv11IcW3WV/u6NNaQ3oha5np80l/uTW9RH5wh6DUdLR+dxuG0eJizEjTvof7EbI7+WluuU9KSf5c7wC3zHPwetB/Q2/yzt5Ef0i5+xD/md+3m+aXqf3ECavzSSnpQgTAuRRlJ76YjqSK9qBP4wjn1mpLTPTsEW06Urcya9pDju7ZlHf0o23y3dzvz4buYBl3k/7Sp5YR69IT+QjTvcrVTAu55F7B9WSVdeWktaW/2kI81fuvrWZ9+iEc4JxJPh6BlCr/0I8kcR5PuzM/Td9GRp9l1BTnZLr99+adc+L11vXCIX55JX38Uuj3mX8Iw71nN60fVX6/debi8dpzpKe0Zn6fq8izQO92bvv5R5bTe9ZmXW6F6dydLz6oh0bXmBrUoxv8xa838yGd5x 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RhZCH6lZEvKJlveO+C1uz/p/vxviTTIa8f01ZDX5/s448mIy3+3kZm/4+9t1fO+8M4N2nPwr65zaJqzLkqezf0wdzPj8+BzxuDqA9V5VG9QHhtc12Eu9VQFca7FdmOftLYizBpWQr28KYx1+xRHxof6wCHWJMEqAA3U5+VStv4tRqbrPfiJvJePVxg/ytDWOxMu1Isb/cSvaQfai33p8hLwmrbVpLv4Ps0ScbA== 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eJysnWV4FFm3tivB3d0al6DBNYUHd7fGJcE9WBEILgkQXDqNS3DXSpDgGtwKd3f/Duvuea/J+XjnMDO7/tTVSXftXVuWPOtZa2vav74cHt9C9P+5Wwlmrv55t+dZG/rzbkTcWyb35atn6P++Gc1autMh7axavVaem3fEJrm/fLLx59327NUqaef8tOkq2tPX2Uf9fI7j/UR5D3NROmlHv3tG2nWcKrlO/j7FWCj9WHJZvm/b48V71761Wb7/8OIO6U/uatvl+8NDNsj/4993/pt+2n4clHF3LOhOP/LukPbMM95yt462lnkwV61Z9Od2zIlpZ//8bF8cXf6vFVyzRfq5Y9hW6X/ZyvI8bdqDaf+mf39cxv2h838+x2g8S8bFcS5gjbTf2ybtW8tGrpDPAWNClLQ39P4saScwjaxHvd+19fL8yLvSrnYkSZTx+KeX/jZ07s/nmOH3lv6827qnWvnzbqR4s0T+vmtcsJJ2cnaQcTFbX5B2HMk+yniZjQJkPLWhx5W8j5HBmCLP/Vpo5s+7MXSfjKPZf758NksvVrKvtKJ3guQ5yQPnyHPHvJf1aMTNEKTi+Y6Rp9kXjfPL+tKnb5X5d6wtzvy8izFLRTtmfpv01/BaIvNgb7RN2nMENJB15zi4SubLSJ9czfycHivyT2vSWp5rVRuzXJ7vHy53W8cbvN/D+KyPxVsWqmhXW1XEkOfUTirr3er6Rp6vFZgm42ouvM74dt4i7etXxkcd37VrB8nf2wbIfNuOr3FKP89kXCy/j3gj72X3nCF3PcXWBfL/hBWVyB+tTRp/eV6XhLIfzfRNZVysja2kfXvWwzJ+jj3pWS+3D8v7aLbdvFejZaJX9JzpRY7pxzrRz9kjRY6bmYuJ3NS6xRG565jSUubfXFVm5p/7b6SbJp/tPSJlnGyL3ogec2TfJs+3PczPeDzoy7z1HCDtmPMjnPK9/CWRA2tSy/gaF66P+fPz/6/LzLxK9q9evoOMg1H1qcyHPcZj9PcxD2lfe/FuntwDmo79q+ebARvke7YYb2U96o0Lss6dRYb9+Xd6l37yHCvysqwbY3/bnfL9tGn3y+dXG7jXP7VL3u9KbemHXr+2jLc26IzoJ3updKaMw9faYfL9xb3k+7bpI1dF6adZkHYK3BC9a6+xSH5nPE22T56bYbo8z3a8vsyDlTy6U/5ey0fWnaNMTeahXc0NjIOb6G19yKtt8tl/hqwL46W3fF9bdpF9UcVg3y18Lf/XDw4QO8Gch51iFmkj9oMZfpN1s2kO9xpT2UebT0i/jGNJ6WePAO4N2sj6crSez3t9KxNFr1hTdst7mFvS7pX3HduO8bzfQe7m9NMH5DkjfCLkd4nqymetvybtaamDdfn+jCTM58ab7It2obKOjdM+vH+CftIf4/oIGU/b3nIyD1bGrfLZXNZE/q/tOyft6mbWk/KcEaPOyf/ntjgr3w8Moh/JdL7fcnm4/D2skPzf3iLdTXnflh9uyeeqw2/I/4P8r8hzMw64Kv044iV3/XPl6/K9j3Guyefgwxelvfrv5LM99hd5nnHu4CVp70ayM/L3PktPy+fhA6RdW8m2kdJOzVD5rH/0PSLP8a3GunxdhfUwuazMo1E2XMZFq5uC9TUiB+8zIDv/D7gwST5PCWG9pMrEOHWuKOOjXa/P5/xbmYc5pbFTthVDf8SJx34sV+e35KAR/660Z/nMFDvHbPFG9plRp6K0ox90ymfH5AXYRQEBUeSH4fAOkN+nqo08rrkU+/DaZdZlwWuy/vVyL0XOGS1GYN/2a4Z9Oz9it7T7Y5asVzPvDVlPxu2rk+V7B7YjV6sk5fsbBsv3tUse8tna0VbsTv1YIHKu/Wb03NfE6NecGn8vmkX2m77oUxR7Qd/fgfdfFhs7Mu899vGlC8h7zVfey3agOfL5eF7s+W7n8CPSvZf5tZfLjB7d3EDko7arMfrs83h5ntVhPnp9vyXyTu/T4td2ywZ38Qe0A0nE/7FyPZP3sM3qIHLf/rq3zJOtVUaxF40G7ZXYV4740wN/PscR97XIC/1IPtZVkYLSrv7Dwg73viDjYQzzQc6uGS/9NKdtV6J39TKe4+T51ZbK+DtqVJV2rdJFZBxtN5ME/lY7WdwGSr933ZT+6WGerINC4TJuutde0d/21Aflfa3vE5mfE3Gd8vxeHdHXz7Ijr++EYHfWH4h967NDid1p9Jgj42ame0c/q974rfezbc4h39Md7VhnsTyVrAMzUwnnz+fYlzTCH992VdaBbV1t/KCRPRwq2jHaJe/18znmkmjYFdXDZL5N7+myT4302/6WnfJfr7e1Jspze9jEjtLXLqa9+bcDVDzfTDJA/B9tXkfZD7bjnxmfR5vx87IeGKWiHa3Ks7Y/n6O3GS79t2+rj1+3uq2sY7NrCdZp5XY+Stor+0TWt6PCLZFr2nnsdVu957Iu7EOLOFW0Y3TrOlXayTMMP2B7ZpGrWkBG8Aj/asifxIfmqGjPnFYTe+V4b7H/HFtfIlfr+PCeX1ZG8UOMk/Hk+45i/cA3Rj1A7j/+iNyv9BV/uno+JfLAdDuFHiqQA/vuxUzRN46zfaSf2vTy/2j/WeYz5NyHuOAnZ+LgP7xMogQ/MeemRV8efoCceHRdxsc2+oaMm2NZhPzdcn6Q97DqNQLPOe8V5X0cuZZjv6Tsh/3o0VLsRW2mW7h8btlc7Corflfpv1Hv6GAV/Tc2vAbfOu6B/XqyofTfWuXL+7x/PVdJOwmii5w217dhnb0LnvJ3nmtOLC72ivneB3t+9XCxJ8zzxbArqmSdqqSfU5rK8+ynWrNP7g+UebRV38U+ve2nBCfQ3XvL+rM1eyZyzKp1Dv+q9RHsnpN3legb89kxxm0dOIh2oiJ2+SI32c/2lLWwW1ttirL/zXXDsP/u7gMvHXWRdVEoTNaJVieDknEwkp1jvGcgb60Fsdg3Nacjb18fBG949Nipoj1b02XYN8GZ8CfPeIr9bfSfLONhSzpX2tPbePylXWG1uC39sXf9LP01EpXEzg8ZhT+TcyzycVTQaHne12LgrL63wOMr3MDuL/5Q9ruxvKf83ub9DX/GMzCqn/5Pr/HJxM7Shw7Bn+iH/2D6fOG+3s57t3y++M/t6Sum4N+3eIL8vOKPvji3m/VyqQj++4wI8JAvutztg5cin8YlYxw22Ig3BCfk7x2/Ma6tm9aT+5zXrMtLefGXitfAj28TgX94rQO4xMKe6MN50ZEby9PIe9kPJ0Y/rfAVv8s8/ob3qZRN5JfDkVTJPtIPzZ4g7fe+DS7WPlhN3OSUU55rHmrH+M2ein6a5I2+yjWPcc70FpxvzhXBB41J45HbY6rz/kfDeO8BTvzCTzXm/U7/zNyDWR9BN8ROtLYU+z0/4/+4jDJL+0o/IxahP04Ucsq98Fz8zI3HwK1q32B+ylu/9KN0w1fsSLPhDHD2aXGQS1+nyfP03IXR42UbyHhoT7v9Lb3y3y494TvBcfVZZ+S5esXY4JkLD6B/lm8E/09WT/SO/nE9/59TivjX0WD2/7Mw7Pq9l4cr6deSeuDo/dbjj0XkVuP/+M6S8TcSTZb301u1UrMOIpqAj/nFY10Xboo9NOMW4xNzRBTc9x9fua70l+dsTM4+GbMcvXH4jrRrZo+Jv9BjYBR72qic3E++tzEB/rFbTOzPazWwc/PfI06b3oWTHI0VdX1VyuIt33+1AXy4QBfkXJKR8hyrXCD40vBr6MvLAwb8nffVJ36V+XU8ngUOsPAe42j7Ci4T2hU8+aIFznPs6Dz+XkT6q6crBl7xuGJD+fv6vOPl97tfg9tkXwreMTgh+IobcVbjcgfkQqHu8n19VD4ZH/tTb/meMW8T+ESjwH+1TvTcL2S+7JneIt8rT0Ifm24i9/XLush1K6ICfkqJ6f5y396/przPyYXSD33PTXC3vD5iJ+vvyoCblcko86jPyKAkrqQ5HhaR/i4qA47cazj2+tQle+Se6Zt8tiZ6o7/3POwqf/8+Gfv3WE/wlwsviH89mcd+MK8R133LfJqfp//SvjIztpZ1YER7SNy0S1z5na2lpzxHj+HynxpeB0dP10f0qvG+k4yr1aCyjLP5oo4Sv007s2ikPD+irPTHyjoVv2HOLvabx2A1evJ+KnDW29kZJ9O1fg++YP5fBDmVtJP2sYyLeTsn+6jtHdlHRp5BPP/zJfm7PjjROBXtGd1jyHw60hAPNRMUEHtJ65FH7vZWJbC3cq5Qgz/lTS372mr6EbnY5iX7Y2Z+4jAvt8o8are6KNGj5qws4HUj8oh81HMvwl68vgp5MsETO+BbEvRkHn81uFGJEcSZZjYiHpkF+9e+4zFy04jmVNGO3nM2+3qOB+vinQ6ed+AUn0tvB3crW06JPrX7r4XXY+USOWEvbso+cDy+S3w7yB+5kfkBuMSZUtjXfttkvG0dG+P/jE7EeG9zYjcVn4+dU7yEEv6RcfsS8dDYNaQdxwsb67eNRjw2v9eIP7ejzz8NHublwP960p54Q+Fh+KnzW7A/pi/m/fob2IsdP/Ae7T865fsb8mFX7PxAPKTfcfT2tiZK3kuPP11wVWP9KNEnNv+xtPPmBHGJrKboM/1FkUlK2juTk3l77YWfeVxz+XF+yL3jO5SsK3NXTOyi0SnhH+3IK+06/NLKfrF9Hok/frgy/umFg7Lu7E1ZV+b4wf8oDmKMqin7XZ9QgOdsGIfeKvwIvbqK+Jj9znn0pX6TdbQoBeOcJQ/ze7I84780kvjXolb4TYerox83XGSeAoKR54nC8CeWTe8jz9kVT9afffd77JpjD116LDP21ZoQmU8j9gb05vwUxLfipOL/oxrN/vP7G2vwp+xjNor8sYJSyvxZfsnAd1r4OuV5bmmQ9341eN8N6UVfm+nnM8+fEka1AzLWlnVs3JyK3BzUGxxs/xj0UbYq+HEJJxDPupUfvGc8PDpHvpfI/QEh8PAaxRf9rbcKk7u9cy1wqWSj8J+jNQWPqRdL9qVWYDX2uvu5fzbfjSYRb/uUC/19dh5+//DnYrcZX0OZ74L7aT/JW3gd1ZrJ74xewdz9NyMHb03BHvB9y7qcnhictcRe7KqQ8+DHoY8EX9EKXoFXGNQYnoD3c/g5XytVkvcuOg7/7dp5ibc6hlU+JJ+Xz5P4ttViz0H5/sjlzNP8NDJu1tA44GJ6LPmd8a5UuNxD7RK/tn8qI3iuma0LvwsphJ7d26TNX42jUaOyxGvsx5xO+d4k8BQtPETasUV/Lc/VW24iPl5hNPyaAZ3ZR3WqgPMkTA/OVDc5+n50BPfjhyXObat9Wp7jKPVM3s/eZZLwDqwj1eWzZVq086GJ8CbMksnkd+bkxsTLT70SnoTx0EfujkQ55f0dz+rL72wnY8JDeXifv4dHyvPNug+OSntDG8NzCHDxG2rYDzGOS4V/YLQLET6D1rat8BtsTx8Lb0Gvn+sov7ssv9PXpTwvz8/W7q48N2LFI3lekiL35XPPZMJ/0GdOkO9pj77Kc7Q1RSz5fxa329KeZ2y+l7X6BX7XXu7Wgh7wMx4/Ez6E49gZ4T84NhaSvxtNQqW/2uFDx+X3SebSry7NwPeStcZv2J8Y3LrfStnPtvynwdkyb2JfDL8o+MP/6HP8us4FmceF1+GjTLAdlv93sfH5QzN4EQUfYb8eOold6fYN+6BKAeSk98eicv8vl940DryG08SFzICU7JcercS/MRrlk344TvbH3+g3CNzgnLvIE3vdVPDCBjrR3zkuu3DA/bIe9ebz4d1mzUB8p85hVxwKnqvjwn70WuccxMNfjfeX8SnXE/1fbDNyckJX8MFNKVn3lz7J3x2TqrAP59vRA+vO8TnDeuLcH+9GkVu632j0ebuj+MkniIdbpxrw+34u+//+TnC/wb7SHz1FqPA0tKk7eO84ScAbRlZETvYMJm7eZJb8zpZjP37Xm+zwojqNQe7dPIS/NipRFDlvT1JN5Jw1tyh+ZJdJ8KD39wWHX90RObdyDX7xpfLog3hDBU/UvozG/js+BHywhpPfDQgBVzz/DLtwdAv0vo8feF2mGWLPWtFzwHcI2oS8r/26pdwjIyTupHc5Jr+z4rTAH50wDv3d5AS/C/kSxe/T9zcTHME6mwm9vDEa/K5OXcHhq7fGX0wRgX4rV7nXn39vLm5JXPy0ht6+Fhu7OtLq8+fvaSsWwR9x68S8J0oHnrDgI+umSG7WYelFxI2PPlbix2ln+sPjS7XFKfetpVn/Wey0/6W6rHv7iNoyb7axgfj5Wwdjv+7IBO/TFoo/ceMK9sxE1/eyzQFfLNn1n/V3T13Gbwa8cVvcleyHjc3hhwTMYVzHeE78R8//31eX3L3lucmqyThb9hmsj20u/swf1/2sQ+RzUB/e98UN/NCtXZmnMdhBhsdTcGNHr7/kLxiHVoGP1q8ITvujqRL8wejTFPzffSe4ev873Itsmqzi+dq1zrV/PscsRrzCer9c9r3doxBydtZw9uv7D2p4viOL4n/Hao19PzoMfu3DQdiH3dOqwcusGOCxdS14//d08NH61bGbcyVSwm/QOhJPt1fcBV5bdxe4ybqsjFv8/L8VD/m/LvOIF+sp/ins/duexDcaXgGPj1lNyfyYyVfLfjUa5pT94viCHaxfKSWfzRxlpR9GfHA8e9NcxEWy1cWurJcL/VMDf8Nech72XOr+wu+0H2r/WL6XtO1zef4Dv4fy/TxVsbOyZ9mr4j3+uGwP64g+Mz1Lsi/duzJuDY5j3wycHWXcjIiP8l76ohOix2wXYznlfVf1xR9oEF/GWz9ZTuSWY1595McPS76nR8LPNe6GomdLlBT7TNsw4LY8r8vIB/KcDBuwE59Uhe+6qp3S9/7jsq15w3rcPxJ+cbVswvfV868QO9F60AP/YHbIEqXt314Gz27pFuLsEcuwE4Iuyb40+8/zV9GeGaM+8YLJ8eEvZu+AnxtnDzyKHRnZ7yWW/6959hR5bpaqBm/Mr4P8Xu8GXqilYv3r67xYL7X9lOA4Wqnq8A6nmth15xYowVG0SnFEn5mDD7H+ugaL/2RoAeI/6Jcn41fsdfkzGZ6dkvvA55uUtO+69OAg/IddtXm/M1VlPP/HXlIjn7a/cMpzCqYh3hKSVN7DUbGQ7CMzTQV5P6PdfNl3tqwh8JnKRQxV0f5/rtVe2C1LqmMf760PrzrMW/wV7fIX/I8Ba5TkMf3nyhqKnzVzPHI2zj30wZ0TavjAj8GbzLRbwXeOeGNn57vC/jh3eoKKdsxXE4mvByxALr/uyr5L4sS+3jphvIp2tCP4uVrMrPBRFzb/vXGaEFPiEI5RI7GLxlfFP+s1EDmZhvihPnr2L+1iY1g52in9hPjswRfYu5c3OuXvZZ4Rl7h48S/zd377ujVa1pntOv6Q40Z5/LPWM1zy770SO0QPXUkcJWmQ6BVbXD94H1758D8jiinJp9QCrmNX2aqA99Z7hL24Jh/vdX92FN7QH5cVPRR/7n5x/N0aLQUv0gYcgt+6cX4UPWccusz+XFvRFY8qCF51c4D83l5oDH5s+l5RcNa/ezmqhzqlX6dNcLSXaUVe6J4Lwe2a1AI3W3oGv/dhv6rSfqru+GG+h5DT9Y5jV8x6j19nvQIfr3AevMWtE5/fPYVP3mwG43HtKfyKbvHg74ycynslWkr82vMV91tJxG/UT8/Af8z3Dr9owzPszMg84Ms77hMfqDAEnLt3ddZDjaPyXPuopvjBs3b/cp7+uIzcCZBnR8DztcMNkJt3S4BX9r1JP4aPgBeRcbnzl8876CR+sHCuCw8ZDK41NhG87Zfkn5nXE4NHLxpHPy8lBhfwrP7L5xp5VvG+nm58L206cJM3qZmvTHWIEyUvDd4bHLTgz88xSrwGh3H+IP73hPwy/U4PcJHhlcgzbBJPDe/yM3E6bYw789Z7Ejy4frvwszK9/PV7dokQu97WFFzaNmQH4+PZEVytqw/+TO06angzAQ3FrrJNy8h8lbyF/1TDl7hpz/fIrbeb1OTHHt0n4+LoGRMe9Gsf8JClF5jXzeFR8AHjmJfsE9vYlPBOpm4kjpulPLi9f3Ul+kkfuhM+4bwKTulH+EryhDPYkKebpqCHj135e/7+ssvEv4c35Pfuz7FzZ3+JimOkmyb6S/c14Ev1Wcy8p+zAvL94TLymeOpBf6v9/3KZx0qDk3T3Rf9VPYlc/TJc4mBmyCyxP/Q98K0NN7uauLBnBezS0B3IQa+7+HGeoTx/agk1fJGNxJn0pVOwC3t7kuf5LlLiODYtvfjD+vzq5Nd2WIReNir0U9G+tmCw2C22UaXwV4v1YP98qyT5Webp8dTLSJ/N+fNur4JfZi7soEZvuy5jwTFwAv0bOOPO8ayj5uH4J0mGq8GT/rg6u/DEUZeItyU7Sd5iKhP9/34g8rCLi/cX3r+RivaNWPEFBzZG1US+OHODexz9BC+pV0OnfE5L3NA0fbGjy33EP6o/aaSSfhR7Al5T3w2e14KixC02xyA//uVh4pRX4zlVtKdNri54vH0HfHN952jsp/QNaK9v7ijtGHtfy7q0z46PXVClNvPk8QK7o1tH0VPWovXI43y91eBmPWZgf4+DN2dZoeRV3IgNP3KQjxqe9spt8H6W1WPcd/TivVo0xA5t5Yo/JYD35GgO79BxJy54uNYBOXRkpqxP23Dqh2jHXfGrxOT1GpFn4e/bsWPM0nmcKvr/n/d40xn+2YWF2JtF+uLP2gcKPmVc7yT9sKpTt8W0gsnDK3WcfI180eT7Nlss4raf64mfbp+dmHmv4Nlc7jEvStxCj78HnHZRHPChlXHBf9c2Is6TgriGY9dLeLLlmxFHqKyTHx3ZAP596oXUH4idj7jVimN2uTd9IPrGGj8Me/h0ReLUK64SX8zdXX7nsIXBu0iXJIr99k8v3eceerVoV+m/LSAIuVDRw8UDma0E97EuIV8cmzejr4NbYKdc+UQ8d29LJftI2z4IHK8l+Un6gh/s9+YjyT/xqIFdazTD3+j3GPtudif4w6/6yXxZL7bgn9xsiB/39MRSFf3Tc1wVfW7vT16Mnuk69ZIOLSP+vSAm9nmrJmrqvDTvIu1ZjgHwQAx/7KY1y1mXO+IrsVvNd/mJ/6wYhb10rrHz592xHf9LT3tPPmsnDoCf7JuhBje5tANc4k0EPLrC5J86IlrSXro92G0tbT1VtKd7dCLvYTb2mXExixq8Nfmr1vKc+qewPyZ+gT8yZpTYY1rfy8KnsH3uKnd7yeJinxk5kSem3yI1fNuty1gPHa7j13SoSnzx6Cn03u428NCXJFPDt81OPMbIXwd7IG0Y/KJu11k3S+L/pV/+21et/dQLSpcFP675J/ZfsQ683/c2yINeHqyjsv2c0m6P4uivFDXhy+cYAg7SfZjoF8e03Oi/uhnIA0j3Ft7jsuzg+kV8kHcNllLHbUZxNfb7h1XEfYYHES+M9ACviU0cXW/4AP+/wRbk2hp/8MHJ/siBHwOQM5nykocRPAy+xIZxzO/yk8jNGnfBM7aTV2Em6PvLebflyI1fdns4cvMU9dFsmyPJm3XbRX8qRMBrSHKP58aNj3+7uh7jk71qlHxaY0sy5HO1pOjFtRdZ7yV6wPfZ3gF7I1Vr7ObRp6PoEfNkgFM+lwzDz5kSHzk7yR2+YdPFv3wf3Qveh609+snxYx5yMzAG92suHOlDPcb/wm70g9vo39IT+peMwiOwh63ALr2C3W/sj818nHrF/qvqq6bu2427+NN+j8CHs1Vy4UrHsO9ulGUdP05JHPWIH3biro/si7c+4F0962Af+sNLsb/cx36duDkKL93err1T/l/aQT6Oe1XudyeAE2avpiSOZ/RKSt2DwmeQI1nYx9Z8F6/77WoletQq3oT48ewyrMPeWal7cG82dkZoHSXtmLPfsU972dDPqZbgpyQ+Cr7j3UYN72D2JFkPulkH/bhiPPZeLfi62smKrMvQ49i7h9x/iSubG6gnZI7pTfxlzGzWy2yXvb+pgxr+x+WB8J7Swr+294KnrPcMoJ22D5AbfZbip95qJ3wt82he+GfvLzOes9hf9pCR4Fahw5TU+9AKzJT+2Q+9hxf4rBLz5hVIXvzMPGriipcy+ctzHEfBufMWBjdL1o44z/ecSuwRs3EM+AWFXfhWs0FK8qH0up+Jg49fT1zwTCvkzZRh8MXnUOdBr5kbO0h/oGT96CFvwdP6vYFnc7sq8nv6ZvZVYW/k2NbKSuwarelk8paLnyNfY0x15PpD6tqaIY/BcTftG6iiPSN3DfiggU2xv/tvRA8W66gmj63oaeT1dOIU9o/EKWwNb6O/7/Vi3RdLoqbuQa1vgsMYWy4Rv05dmfhXtedK8kbMRpPwP1IUg3+X1RV/2Z+LelaJ5oLTzQyn3Y8NwcHcJsh+s86+Q99Ur+L8edfzTP6tfWfEPE9drIbwN8wiA+E59xjMPg4CB9LnN1Azb3+0m3CmyG/LIzN29Yrc4HzfW/+yLog5qjp522evohee38K+cyRifywADzZqXkQfvUyL/M9/hXGbX8kp95HJwcnOHkEvuw+l3VNfpD/moZ3I7QnUPTFu5vnLuL1xdQP6pnRy4uSXt6jJw86ZjnpQWcjvNgcOhkemEe+wYj9lfeTagX5MOxl5OzKloaJ9M30feW9HjnToNSMtccGXj7E/R7QnX+X6UMa5Zx/y0e5P/i35aF7eCL8sIhh5G3SI+ehZEL5zNw/45dnqMt8vipMvviM28Y5ajVinXz4w/5XyMI+XRiDHJjVTU/csR0vyL9+eR86cpq6GrQ353I73u6LK550HRU5Yn59jd3xbhH9xfQK4rWMeevj9XDX1oL+Qv2jVKIpe7zYVvyJyPPmoU07T30TTwFeNZfw/RRPs5/256E/s8uibiu2QQxNS15X/jx4K329Na+TBocR/T59PTiz8Kt3bDs5+bCJ2z5Sh+IdbyR/Tko51+X0e6MOL3aLuo+MryB+ffo79EK8X+ZM/euFnBfAe+mw/7KjNrFstNvWytZdXa8n/K072l34sqkj9DiMn+WPGd+KHr9LB+w9Mgf9jlojiF+iN7dj3oevBxT3Kk2+1Zz7jn+A5+Qm9Wor/YS9PfRW9YD7win2JwHuju+yOponRJxcvwptYGgHvKl/McPn/sk6Cu9hz7wWX3NOJffamJ/6pR3fyxd4EgZcftZOXNXQ/zwvti5/8oTDjNi0WcnLLLfbJ0HfIrT4nfpk/bHuCXtHzbKSO5boN0i9b1njE5w4/l78bK2zorQ/tZd5sN6h/rq/Iz/xa1O8xmr6j3c9TOsm4GcfAnU9Ox48bkAM/eoJBPQHfgazj9PnBY5oeYH7Mfa7nnsb/tt0Glz72mryB+zH5/8nkUffns2/EDR6dgydRYaW8j9WotOQvORrdIx9q7UzJU9EGZSC/LTAm/kesLdQLfxRP9oPj1g/87bVHwCdqZ2JfZaHepnn3LfvfXgW8d9lc+DTXr/AeabawDir3gEcQmhu8I+MY+CQ1ydvSj2WU/Bmb/2ry0y4uI8/L6y71aFveIM/L3Q6ecKZ2FL/ffEx+qD6sPXUPvlST97QvmM+9xW7y5L7skDwl24ll3OuPlb+bpYsKvudIFE6eVOwXctfirBceoW3+ZuENOvanlTwto3856tm+6Cl5WrYXpSUPTI+xRXiVZrJIqUdrez3psjxn3/h78ruri57Kc9o3g+9caY/83jRKgDPOOwv+2F2X35upL8vvHVneS7v2FXfIL7t5GN6uN/NrL/OO/KvPuyX/SysfKL/XO8aQ/C/T7wfjOOKyU+5Pd7FO7oxg/WU8CW605S7xF4814F3hSZCvCfNgt++YDp63uTj7vNk94k45Lsi+1JZWpT6SMxHrxk5+v3GCOn1m9s/wlHa7sa+D2iJ/OsVgfbWKC3+mSzzwjkXrwA0PnmGdDbLAn+IXA6/9NJ+7d2fkQu65rKOsX2VcHDVmyt3wuyC/s70KIa7dpRn9zvyM+r/2dvI9vWJe5FNDB3mIm8uA2zVPDI/o0CTiWj6bGL8t58HZChcFr0xNnWvt6FfywbZGJ65UM96v8wpSTCbf5C08FK1OHsblBTwnfWpe5MXRL8i/WdR9ti5+JE+t40D03vXtyJeGvryXrSlxl+M50CPeM+EbDSvH+9R/Sz+XmvAO3N+46jpvIy/yTIX1f+6vrXc15vv1S8a5Syd46Um6UV+kUlF4Y0V6Ed/+EcH4nNSZX5+rv7Ybv9bFnkgbF17fFtd4x+8Drtr0DnJiQxn4Xk8ysI7u7Yni95jLA8nzSlALvVY4pawja0ks5I1HWFQ8JVuqpvKczbOJDxX3ZF2v+YxdY97n+ffzi31g70GdcUfFFuil+fC+tCnUXdPq/Tq/3miWXuxF462rbtOKVeDDrS7gzz15Ah6Y6y52Q+3S2PNFKvn/crz+5mXWOYgd2jsr9mOJjdgfjvzYd/1Gs783jnTVL9mD/rzfnXjv2SHslxngVFYzCx7Yt/To3ZKRzG9C8Etj0gjG4QJ1l62Z7i798YR5cw+mHmX9J7R3ejW/C/RTwnPRb/UjXjTZh/EOGeaUe+h97PgiGdjH5oL+StornQ178Vo09GazTmr8yMNT/OV5iw+w/uJ1JI6QcCfx8mzJ2NfjG5DfEJJIjV8WuhJcOUEsxm9Ucdbjwrzg415x2B+D9jjlfnIOfmXsrJwDUqE8uE/9Xn9rHIyhm6UOldmUet+OFyuxP5t9xE9afgd+XnzyqRz5YhInznSFeb25DDu4+ZBucm+Zr7jcP4bx9w7IJXtseNp6te1q+Ip/9N/8Rp30a9SBNj8/ZfyaNSAfaF8ZNXUxXo4DJwhYjN9yaib8vdCs6JcIX9bhXlMJH98YURIebqv+5GvEDVSDS55IJviZ2bgnOP/xeuB2D+5j131cTXu9yiqpU6edbS/4tD5tHHomRXHyGRslRA4V8nf524XU5N9O86r/8znmvpbgQG/Bv+2H67OO75B3b4udEf//6wI183WiOnm/m26Bd5X2dsq9Rj7wyURBHVW0Yzs3RtazfW4Y+qyFqz6tfSn4ybJryI3qx5Xgh8aP+/hv1mXXOl9IvpjdA71+I7aSPGEjURJZb/rjbfCtYl2EV3ShM/hjY2/ma1tjNedr5Tovek9PvhxcLvAacuoc9b30tq3/Wfy6XQXJazLnfEX+RJKv48jGeTm2d9RrMxZeQX/1O9nkH7XTeRt4VUAc4hYd6iipe6yVu0PeSuZC4AtdKmMX103migPGUsJTsGbeACd5jD2jpQvETjmxHPs39n01fIjn/V24SVXwiPSRrOM9g9Dvt4oiB/fMZ36OexO3OHAsKo526gn+9OH24EA53LB3Q5bgN70vwTlG+4rhFzQojh/3+hNxzpsviAd/cWDHFv5GvkYRl334HT3ieIJ+t4ptdOXle2IXtvz6W3xvvZov+Ywtv4PfNz0DLt2XOJ0+EF6YFlQOP+UA+RO2C6Wwi3s0IC4Voxjryf9zOxmnAcQNzHOsB/NhJLhakvNRcayvX6jrE96Jc3emdQafeDAfXKRPT8arFee96bmOKsGVjUbrnfKcftXJN419FL8iTnbiNWXmg0MGBketO/BHvzMtRA8VSIq9vWYC9k5Zl90TON35V/00oh9lvZ5fzDhvj0UdXnsF+K0VXyjBZe1BnE9hi0HdQ/vhz9TDetYO3KfKVdZNqczYIz17UQdtch/wWL+U+GUNi+Avno/DPBX+wDmGcUvgl2dqgz+aI77gOuahy8z71FrIrdjwJR3nkuBnHMsOj9ZnA/2If0vJ/nVMmsr79n7Gc5NuZP3O18E9t95gv+4+/Mt5/buXGXgDu7Wuht0QYyI8n9HwVfU4nxnPJuCetvob2b+zooMD1EoErtbxE3jB7JLge+6FqN/a5wh20MbYxCU8vrM+52x31aubr4QHY/geZn6qBBDPqdod3OKKB+uxsSuf5WwzJfa4brtLvZVR1E0zGpIfpjeeRnwigTvzFiu3Ej6MLe5I4kcnesMrjVECvtprp+Ai9tPl0Ss5u0n9GP3Ad+mfozLy3RhTBDnU3OJc0irLwT96DVSyT7WIjOQrdY0Gr33KTNZHyTnw0NPdRn7ehqf221ecTeSTGq/Iv7T2gV/McWe9fk/+j+L6Vr4u5EsUeIV9krAO+EO6J+RHLJ0O3/jCeezLqvvhLazYhj1avw7yvP5u6k9t3gFedT8S3LxGBvTljehRz5EIywFO2TML8ejy36iTd+QifsJN6kVrgdOQaw8fI38KalHqRv63S09iot8TtATPzP+YeUjdCTx7zzD8+j4h9KPAKMmjMN9lwA7dRb0Zu602cZjLH/D/z1Ln3vBKq8Tu0ptzfopR8iR89w5fiY93K/tb5wD9z/zI+QJW0ZpO6W+eTej9cgsYt7b9eN6PIWrqUa4YJHxks3cs6gwG1/5rP3v8Oon/GdFqyDoyDm9DP+xz5UcWWRs1/vfBDR7UjF3Iw9vT1ZyjOnYG5y+cJM/DcsRDHs6MD86+rpYafv7IWfR/TQficI/OqMGn3q3v8PM55p5z7L9Y7ZFjzhjst6K5iaddpt6XfuYjfIPGlfxUtG8mZJ6MCfHQa3OWYGcNjcE8ZsqmxJ/+z3XsO35mcCvaWz4TOzSzTU0eYsAh6m1NPAMe/bCyUz6Xwd4zuwYhr8bllH2or+opOIVtVF3k9pvD4OMTtoDL934jcS1bswOcj1gshsSnHPle35bvXWgjd8ea3Zy/mOGbxLOs1h2JZ+0Plv9b+wZx/mKfjJyr2N2NeO3RYuCA8WYin8+9BS+PPQG/JmtaNXUnhrkj11fXA+/rUQk9+8ZB/OJHkhPSv0+zpW6io3E9eU9t4BvqmlTrIfU/HG3mErdrEk48LnCi/N+o2If4mltpnrNslgunHq1mXrPNhS9y2lfsQit1W/AS5wPmbdte2ltLfEc/spTzKo16xH3qE8fQx68g/vW5gsSVHIcGU8clOL7EAc2Ib/g1ZVoqOR/pj0uv1IN8pdc9eW6nsk7pf+gG7BPfuC58NfoQFe0aNfuD82x4iT34dYiaeZjRFXkR6Q1PILCvGjmYdLHgfFroUuySa4nIF4t0gh/ESMW85RnMeVmNMhDfvVda9pPZfq7Ela2JPhL/tRZ0IL7bLZGsR31pL+p5Fr7DPXP3Y/IeZ6/BH+j5A38xY5ga3mar7OQph9dxSnv5HpCfVgX+uva8FufIhuyVfpq7HNJvc3h26u3E/iFxaD33Jokn2munlvfQXobJ/605iTmXdeMG6b/tfQnkdd54as4BzLkVO+WUF3KpYj3moU0R4sl+TfBX/ctTr2RjUvzPCyfALzU/7JUPs4h/XZwHrjV1upq8o8abBH91HAh3/rzrAQNYH5F+xImLJ8Q/8fJRwzt3XcbjZeB09/LhjxffwXnS91OwfztSX1QPzo6fFLmTuHrOaNRX7f4Iv7dNKTX7JtlhxvPOHvTFjqPES6ZUUGNf1b4j68nh6A6OVHo4/Byv2PgFBwJcvCLyncxJBdXkW0/VRe/ZXpA/rG/3BF+LOYb4tNdZfxXtaP7ZsNObN3X+vNt/JKSeZbyM/8qPNktif5hTsnJfc01Rfcfazp/PcXyHN2VOmExewN0X7De7HmVd2S6VApddmQr/yx5J3ckLqfHrX2zDT7pSF75ax+/gbfPgt9m73yRvpH871nWeAUrjffru3NiDraKjz7/Oxo6PHED/1k9hXR2sg92b5/Y/qzPm21xweVuBWcjJHNPBM6oug5eWui31gqbvhg/XJ32Uc9qM7d9pv9ZmeFuvXmBfzFlHXbK6vfFDtRHgIWU7EI8PaSD+n61XKae8VzDn89rsx8lbKzsY/O5aAuJ291YSr4sR8o/8ULNsQvTn5iromQu+4Gol54IrhNQnzjnsKvko0fbB1+3fAX7i6Sfgf8PeyLpwbA3DL37UC3n2aiLjtXIaPLoG5+GHtDoate7TOlf+wOKkjMue0XxvIvWUrI37iUsXvwduVmnDX+IcurUYnvPTlODqbq5zEo71B59d8Jx5STYTPfU5NviwV27G89Jl/796/n+7jJtp4EG4reb9xjjgKVUmrqG3cqedkDfYRfm6YN+l2QCuM3cKen8S8REjWeVfygHzWSLymk49gJ/tdwg/v+gh5FG3UPbdyTbkXbRy4cGR/cBZVnZjv67kXFezZyx416/L/mV9PH2lyfmx+xoS19rxCB5ANhOc6OBU+vs5ArlSajW4kttWeb5j+wpwlC/6/+JPdgUfPTWd/syrD9/sXF7WzbyKxBHyHYR/eai3q57yM/TK9V3wW6pajPu5Q8IzcXztiDywFeTc2tJTWF8dk6L/dzwHF39+mnVcUPt39QHTnSEf6wF1R60hvbF7zrjqPdcz2McvqD9lDdtG3OVbCnD8kgXI14mIAT57PB940JhwNedBfyqBX5fCHfz0Juc92lesR08uKYJ83bbGKffLm/GDxgVT3zHxauyT/dTT0XvHxa5/MZY6NsYp+r3zEHr/YU7mJXFd9rfvE/gTR1v/I3tG31hC7G3z8hLk/4tw8m+n2pCLSfYw/y0cneV+qBH9qwzPUQtcAR5pi8SOf3iPemGZ77v4cpfh0XiMYB0MiCSPpfgXNfk4afNzrvQB9LG25yNycssU+MZfUiMnkkT+EoeyNSdf2+i9kXjlm1De/1Ne9G6G9ciX1hv/VX28//T3+lLee/AH9PzLH8j90tSjsnL/M7z5f196xnjY+5m/8F7lF5K3PPwu4zTe55dxA2NnH/mdI9Zufjd2AbylETdcfAjNlcd1U805z2dqEwfu4s65Tp2fYT/nuqGmLsG3BuiFAx6sW/uI3/IvjZvP8N+fvIUX1tWO3Bh7BDs/9LwanHptO/+fz9GLL4HfYDVhfAeM4Tzbth7gKk/Jpzfq66642CZwkkSV2Hch+ZHfsUohR1JUw/+aQR1GLRrnXWvxyovfay9KfR4t8EBFFe+h5TxOPawbPdGfxfeCp9pTwRPwLQROfMqVF35kMPpjlYsHva8V9W2OzkLefb0teST6t3TM3+jW8Ek8Hzjl88tNv+VfGV0+y36ze9+DH5UxkPMxmvtin91wV4ozGQWJ15ujx2Mf1E3FuvYMRU63cKrxO53wlhyvSoLTvOjFOE76AH/bcyn5Cx1qEg+dPJd+VI+lhhe208lz0q2B51a3Mevy8kDitXn84VPfuIq/HWcQcYcCxTuraN9I3p1zNUZnwl5K7o68b2MRX7enkHVuXuIcScedDNgJI1znpd27rMY/d13mA1fdgM494cuf8QNXelZV6pFrbe9Td+VELfZt8AI1fLLBzfzluTlaID+OzgEPil4QvODEHPbj5Bdqzp+wkkt+lm00dY+MoNvESx4b1JX1zAXffc5Z7Gf3rmrOifg0jnj4DM7vcBysh17qOAZ5MjOemnoBfe6Jf2tMSoncmnEcvf9puJq6Qw23yTzo5d6hn49kQL+Mzawmz9/+HP7PegO/6+MUcMHtseDdVPBj3MbnJu6z/mmUcTOWhYOndR6MP5KiBfHjrL3gya8ch95/1koNzrTsMe1lKwu/a3568KYsNYjzTTrwt/LcjcMpwZOuZaPuWtvq8B4Hp2S9pqzNOZJxQv7deZjnE3K+XuQn58+7fUJh/OlOjeGdeIaCVwx7hv8SFI/6QKfIz7A9T4cfM6E2cX67Dft0UJfR/6pff1zX1yDnqixlf+5OR53Iiu2Ix+xwnXdW+8c/w2cnLuGc7qa+8GGWxMS+LPUKe9b/O+tr4Xclcs7KTV6Rbcxc9F2ixvhBT2YTlw18p4YHc3gsvOa1K/EvfVfhX89/q6Z+8dbG+FuVi+DPBp/Cn65zBhwhZCProfl08KsL+bEjCmwBf403B751wxp879uaX5/vvmqq7Ct7VXhytpEBrP+r7VlnJS//I/vVno7z5h2DiGPa3D3wt8cUgS+zn3Pa7T1cfMTKw6PYV2aNzSJHzZ0nXeeRlQQvGHOM9TOgFXrj/E41+rE7OLBVCx6frch1cAl37Ew95yRFdTizw0vN2h7/qv528LDUccGHK3N+pjWmMfjv66r8fxznoZsV74G3zq0SRc8YuzODW3UOBFfbTj1HvSd1KvVUdvLpRu911Tknv9XMvw57xDbVXz4feC3zbevcnXhtReqeGwNqw5tqGAe55X0aP2nYcuIaOeaCZ86FZ+tojB9o+cxkv5UboAZXv5kcPtlof9Z53mngv0nIT9NrfQd/u7BY6t05pi51yuebfvj3xxqBs/a9D+6TYz96avWAf7WO9Kt5RR47jheX9qyqFfDTtxrUwQq+TV3vHKxbzfdZlPia0fQq+ESztfDARjwBT1jYQ56nL0yG/s/+Uk1ezTXObbCqNsO/WRpJHfaATOBg1XvD1zNt7LsKnONpLz7ql/LTTJSWel5Pp/G7J676bi1foj8ecDc16tpbkZvAG7uSZ2cmSR0FZ7Mnbc86f2eyXg9tRh/N+Yz9Phx5YF6+wTgdWfi37Dpjpgd27/3DrO8Td8A5A/PBJ0/xEP3UboPzz881wjXweN9pjFtGcHVzZz70zIF3anj6VQtw/mDahazvtDWwlwuugj93twa4W9yvUfm318AvbJMXof9yIT+0XlvYv4Xasn8zjYzCFzIXfIKHsyo1OP8aznu2BYUxDk0439U+JFhN/aLhfdFbr3qzL2e1Zp5DqdNvdzumJg+hryvPyC0cvzqP63zwOP7YjQfKku81/Rq8/UM9wMuXlyAOUyIDvMOuy8B1lqb5pT40FnejHlMz+Mla1+vgl6m6I/ecW8CrxmYHF+rXQol/b3ZuwXm8t8aRjziBeIpV2qX/Y1XFbnh3RQ2/uQS8cHve78i39xPh8S3pwX7V1NQz1y/2kPGxTSuCfG4cF3kSHES+9/Nv4MdpsuLXOXcpkYv6bpN5KdYBfK31EuyANK64wKix4PXDJjOf/Z+o4Um4LkcZ8h60hQ84X/j8e84lS7NWeG66/SJ6uyj4otH0/DAl7ZecTNzOvTz5OR7u+FfT6qGvIl+xT/M8xZ5YGAf8KITzULXKR7HfIuHvGlvr8/sSdVz1ZrKoyTcpkZLzOSfkxi+eVk9JfMZKEAQeF1qF+MPSVtjTesJf1lH529etmsR/2oCH623CGM/GrGfNeYa8hZcJwcEmHQMXbTpXDQ5Y6bLwhvX05P/rM99Qf+rCbfKcvi9iPJOVUYP7+eSWvGRzFevUvBcfu/SRJ+um61l4sw9c732mEXZ2uLsaXpdHEDjz9TROee6tz/DnA5crkU/Go/asiyMe8KbmV4Zf3bIH+vhbOjX+gs8S5Mut29h/LajLaLTNqEbOTl4g+sxouQG9lK0Lcclso7CPfmRn/97uz/5IdYf7GOLVeiPquTq2U//XuOTijwV3VlJvQPtWjXi77yPs+oP30GN+gwereL6x2A0e8ue11MPaVAY9c3Y96zNNHer3t/nCOddVOa9E61YAuzHDDewCtwAledvGySTEufzhj2sLKoMrLl+sBB91xIt0/nyOdZq8JK39EezpFXnxDxcfJ360tif1D5JFU5PflYj1Y0WLTf0Ps7HweR31zwgf1P66hfCS9aUXpJ6QVuiU8D61wYmJg+e9rAZXTbgW/VHcVX+4b0LyyAK/wistVIp9cCT138PBZ9QGf4y/mTh2rMP4NTvISzTaE//W2sLjtqws6PXQMsQdFgVh79arp6bOXURF4ScbQyogL2y5kPPHtyjRZ8bLUSKnra8bsI/LTCFesbsS8ZQUs+FbJRyPvbKuE+eIVpipJg8mzgp4h+tbg1tNXwJesi0vPJVhB8gve34OnKXzeNZxJR8fFe0b15tSDzXuNfS0k3PqtYLT2bdLrrf4VTtGnSn4BYvWIj/7FGB+xph8vv8OOT8V3N+8kEeJPjYDK4scNZoOh+e2IT945eY4xIMnfkC+begfLve2N+GP+R5TysvVB41EL1em7p5xMAjeZZn9+JX1xuF/ThmD31suI/jfxKXYuyHV1cQT2hehDkH9gujv6JeYP7dl7P/cGTn/OVU/cNjbN0UfOKYmoE7Z2R135F63lNQ3s4otfybf3zf6/s+77W4Ryc/QplDvTZu3vr2KfmuH88l5LrZdR4jfxbor/pdRuqDEM+3Zj0j9M/uLg9fk/1VbSj6LdWyH9Mdon0P46tba/vBWViVTU2/kUbDYEY6KV8lbyTEO3uWVbNSjmx8kd+10SvJFb1VS49//ce0qwDkd5nnqUpZ9yXzeXcz8JtmuxE8wY2wk/+5HfPykRoWdP+9Wxu/okStT1NSLiejvL8/5OlDkgWNHSfCycv2QqxOeh0u71e/JXRvxjfOFfjQn73XMVDV2fNcXcq6X9bkEfuCFgeBkNTfDIygQgr10o4mac+Rdl9G5H/bdjkDiYdsXgEMNXyxySu94UPIsrE3rqDt4LkjynWwDelFX0LeA6FdH87JK15kx4yB4UvUp4C0TOW9TG9kLuZ9up5J1ZuQohr9WojP6bdsh/MJkrnMtXq4DZ/U6Bo5WvXGU+Kd+ajRxy5VXwHlb1XDK97agB82hauL8xkbODTQ37CIOvmcv9y5H/5W9qjd14znFh2I3tKYusbXIIE4dlofPZ4jXmo8PEP9ouAL/pWZH1uv1JfCohzSOEsc1S6RFD+WOAX+2yWh4QyOn4SeO3qCmvpjnLdrp8pS4yJknxB0eBxInX3uS9XNpVqs/t2ec+YjfV2qaU7532Q88agH4k7l+w2/Zcfqp77xHrPOskyf3eN9Y3VhXHRk/22vyy41iufnestdq8kk7PQFXWhBb3sPR7z7x7CCTOjdd/Vx20412Ktozt9pF7tlTxpf2NI/c2BMlHMQ10z6IOq+DmsAbK74T+/hhOXCfu8F/7W/cD6He0MsUrNOpV/F/3Tkf3CgwIsr86MHt0E/DqLusb01O3PPldtZrLfJltA7N8W8/j4xip5q2fhJf0r+Rp2KERvC7JVfB+R9Fo57MswLwNsI7Ms4nOJ/aGNac+OIezv3QvIfj7449DM8txkf83PW1qbN6Yg95tfcTkg9QdSfz1W5MFJ62PrEV5yjnXwVfITQW3/PlnHCj/SDyZHPmRw8XDcKP9/X6Lf/dWJNL1qGtQT/snTTZyBffVJF4RvMw+HrFhyOPl+dmfoOoJ2Hdi8v4ZMhOHLXSReJQd58SP0q2k/jIribwkjLVhGddd7LYxY47w/l949YyH1Yt4tHmoSPkBXeJhz6K3UI+W/HgC+oP4QtbM8FRzZObf+vcArMH59ibBULV8M48+vaW/jSezjmCW69jL166ib+2OS12focc1CU6lBM8I3Gc36qPZd7PTF358JXgRdUNxrXfQuJVB7Phhw3sGqV+sfF8EXo93Bc9trwM63ffXOq93DrNuGV1xWfy3yXuaF4An/C8Cw5dugG4eOEY4fI9Pz/XeYvNmY/d2uo/t2vOTOCUvwfNxZ5KX5C8jein/OU+gnNCjYo/wGV3eBOXWreO95q/mnjD1/jUdZgcEQXXNk9Qr8pWh7ryZt+sPOdSPXCWafil+rLq5DF1oT64sdKfdVT0JuO21Is6sduaoi9Wj6U+UERy6tfmzyD4iDk4PTjJ7HfUu44+h/xov4fy2fa1E3VuX1WT39tCXXkjelHW99aR1P02qlI/uXQ36jPHaXD7591+n3rNVqOT/H1NJblr7ifEv9Cn+EteuXXLLnfHxw635HkF2okfZC/eQepAO8ZW5vv+mcjz/TRX3tt6sDlcPttiSr6s3jGl+C9WunySN2scP8Q8xk1PvDV/Qep3n79BPvzbkgfo3zLeI3U6eMzNXecqTCmI3Vhognzfnmsv9Y7Hlscvu0PdfNsbzkHQB4PnGYGcx2S/ttJ1fvxT7N6Jmzknc/ayKOtKK3MD+brXX97HNnz3IT6HymdtzV3W0Zu94ODlqNPjGGRHXtZcLP652egK8rVNXOo3e06UfjtyN2V9dLfT7xq3WU+lnrO/Qquwb6q8Qh63dMWh46xjvfUchXy7lo+8ns6j4f1uHUcdqlu5BJcx9iQnjpf5NP0MHQl+M74T8vNeiKvu+Ujaz7SKONyWQeQ57gsAL9gZ49f2U4UIiQsY1128QvMicZAv1MvW21ZGDnxqyvje+YC9cviT/y+f98c1bKHUn9cPbgG3HEF+h9GzIn7o6Rvk0dSNxvrpGhu+c6ZTIr+sXeR9a6Opp257/gM9uG0X++n9WeJxtxZFqRtk+BSkX+sToY+TFydufiYrPKUuK4gb+vfDzjjREfzrEbxXR8vF8Me6U5/T3Nkxah7UsUWyLuxxOBfD4ejDOi2oIacCq6PPsoXjF9SrjP+9KjCKfa8vD4O34LkQO290G/ClZieZpwqt4P96BTvlezleIieqtZbPWvqkUeLH5uVxxGnCR7JeStMf81ss+rN6D/P4LQPjtfL8r/NRby/CLkpVFf8xyxDskrfhrvjUNiXxVf3RbuyxA9Qd1OpTN1tLkop9ecN1HnEH8gb0KXpUfqXnU6njYlbhvDxrUUreeyznfWnJ0/C+KRkPc6mD+PDox4zbgDzk9xa5Cb/gAPNubX2AvXbxobSrVYqpxH/UX/Ynn+HFLHjQbzhHyXi+kv3cNxv9rTKC+bni4o+Vs0XFLwakI97jtoI6xVnzoZd3PZfvOxL6Yl9/vkY9gfqcS6nZM8NDOH1F3st+HjvfvBPXxZNOpCZeMuCZ1Bkz05cEP/0aiX2931WfpcE07OW86ZTghtr6XezjZNhpjuwNea+HrvNMl3zGrjfhi5j79iuJZ5rzHcTpzl5n/NshP63R3DX3qcj7EeOcP+/2owvoV+aWLrvzgSu/8MMv+2N+mwDfvCXn7Th8OL/darcU/7rco7/0h41LGdBfR3KRX7A4DbyUeN7wf8vmhA+QORl2XOmr0k8t13H8pv3vwXdLHFTDzw/b5i/tzeWcMnu1UuinfCngaz2DT2IUj818dVqphE+tNxuEPTxxDHhC44To94uXsY+z5/klPqjXLwM+v5x4htX1AjzAa2nRf4duucalDfrDuzz1dRamZ93VKIC/1fw6crzxF/yHW6fxi4YsQl5VctmxARPh+10oQ/+i6cSry51Xsy+9D/CeV8HvzE3UadAzrPD/8/ONxEck/mqui0E8oP1j9NyUruACd2o55XcXyrrq7G2B5/kolM+nZxIf6Hqbdd4lkZq6+pPACfRn2ZAf3TnfTD81Uk38bJkH+QwtTvP8H4PxJwZtVXO+YnXXuXz7yuPXBRynjs2dc9i/IcvAiyeVw75tVJ3xNxr/s7oN//s6M0jiHVbwXfZ99pTYKzVKgzecMOBVDf+uJu9n0hXObcvlymfd+UHJPJnVmstz9Aqb8Mu/vwXHOHebOO/hCv8OJxxOXVf9qKt+/uPhauIHx/LJuFobDlOntfiTcHlu5hL4V/Ed1Aeb60k9Jp+15J0Vech6abheDZ/uSwPqD+6LQV7hpE3IJ9/Cv3xP49Bb9MOkOa7zmpbIuUSOeRME1zcCklEvKtMweEQpJ6jZLzkTIFeT1EFOtYn/y+cae95yrlOSetRx2VIIv6LyS/E/HeXbEuf6Olj8CMfJMPCjA7eV6DVjw0l4qnd7o58//4Bn/+0WuITPMDXnBN/O68q/xx7XeuXGrvL58et8kEvw26zwi9i/cfPhl2Y5BW6RJFLmT6v8SurVGRU5H8pRdLfcLY/4Eqd0fKop+IC9GHmYqi9Hlkj08NQv8Hv6vpO8Sz3hR+rmpegpOIX9zAzhodhyfANX6Z3OqbI/evMp8JrSxEQfb/HjfK6hl6UuoVHs+t2fdytHwwfSbpZdt+V7U4MEp9FWvWL95/HoqaJfZs+4nMerR2JHZ/n47/ZV0lXIYy/wdMNrAfzQWMXQB89GOeXz6LbYqXFccdrbo/ATR/grPZ/UXEJ9Eb3vBfDeUlXYrzurUQfxfQ3Wp30J9dqSB7rOq0upBodNl4TxrNIVO9wnQM1zL55mn0774arresJlD7nq2Fzor6adT17Ix83r8QN90rA/dwYSj24zU8bTdvZluLSf9S317h4sAK84Bz6vJXrAODQ7x7qo+0RN/2Ynh6fRrT/5fzFyUad51Xji4bt74SdNfA+usPs1vJNX29TkJS0uDa9sm+s8x105WW89UqjJ10m8mny4+7GxQzbCizEclfDnp6cCh+u6HzwpZCD4zWkv8KTN8ZTwrrRGYU55fl0f5n9NEuy5NpvAH6/5qan3kf627H9rYin8s3VTwGUjP8IfOXX1qvz//vjn0t7UF+9+3s0Wj+WuLSvP3wNziDw187vO01B0GTdLIy8PT3PF/+YpWce6UZM6bw8Lg4fln4t/8PaTUnmoVTgALrXnhfPn3VpyR+5G2aTI3wkJlPB3jeeZwfe8GnK+yQV4COaHeszrF+Ipen3OudVjzgfHP3Fa5teeqCy8qh42mVfr20K5644ML37eHW9TUdfWMXiHiv7+p9/VajKfGfaBf4Quhac+zsUveX0He2X/XtHTWngq4YNZkdvEjtC/vhE70Bi0Wd5Xm1pGyTlE2pQW1LN4PJV9VmcleqrrsXDp554VxJMmJyf/c7H3v6ur9b8u45KnzKdlxgGnmL2OeGxvO/HY5AXJm9rXGFxnULHfs3uLdYBP+bYrfISpHcHRE34ifjPIiR55U1Atz8jtLfV+q0XQ38QhxAOz9ER/3nqspt7CYT8ZB1snzo+y3lckDruKOmi22a46rPUV1eldQj1qo1tL9nnYNu7l8lJf/+E57K5VnKtrVF2Gv+U1T3jZ9oabiH9ksETeWkMboUfPPVGTP/HhDLyYu4vhWwzxIc9gbgH8w1ZV8RcdOdXo560vXedm3SbuMiGEfMZ518GTJi5ZoKIdcwB10/UW1DMzKjRXk/f04IPwuc3eVam71TGOU57bZwzxve0enBd86gq8jQO7iX/V2PNbvIPfvcwht4VHZST/Qr73zLXg/mE3qIfwKAwc8vBHlx2fUU1+2vi0xKvSpkOe9dyDHRTrK3yllO3wH94SlzdvXY+K/3Rqw98jklA3NEt60TOmw5343sUgeB9ZjqvJb/3jWtKkqPSvQhD7bMgk+LBX7+PvObaj7141Jr7a9HlvJe0PyiU4qNEyB/Px9Qv1GjN+Ih7aoiF1lyI6gk9sX6Em72BcNvTmmy8SZ7WuNkZfvoxGnfrWbdGTL+8JT0C3LeP86O/fqYdUpKCS9aKHj6D+c/M22E8F1hA/GZMWPuHorPj/08eJv2f78oh+JvQFZ2q/Xfx+w4rBPro3KWodmMNPvH9+dmxITT5fk+HwNgasY//d/QGfvvIbcNYVnM+kb7irhFenZ3bxBeqk5f0W5Cce9cRNib/xx2X2Lk9djGFZsPcztiH/douNfPxynMPsKD1GSV2V/1ypdlBfaf5Q2deO6fXwZ/a9Ic8u9Uw1/MQpwSKfjWdBxMcfUkfTppVAnmVYC64WvhW5k7RcWxXtmnuTgvtW7g3/pENOkQNW/Oecx/6qP3hEv/zgnwlzwpdp9T0Kj+pf9yPVJXAaH1c+fILySurS60fmyTq0TT4Pv6vIBngn7s0FFzU2XkT+lP9E/OFkN/T/RDX1wvXH4CuOzhngBw6uyj5858a+3t1C9r3eciTyf0Ft+CzeQ4iv+tyl7maCQfApgkY75e8Vk6qJ55YZSLzzbDz4Ie9DwKX6uOPHdO4r+sjwrEq9wydB8HQ+b1YTLxm2HF5d4y/UF7rS3/nzrrvOB7Ta4/cYn4rBT07ko0Su2B8VIa5WPoYrLh9KHEXfHWVczS6tRX8b9nHgGq+iiV2q983HuQqtFnMOQ1iQ6FHD8N6son//uQ6HY//H3gtP6fod8tzWY9+YTzOoye8t7cV+a50X+7RjBZ4bugP78cUw6mhP78I6HNeA8+GrelG/8EBX9lPWmvDFtLzE1QKzwUO+lop6i70n8/0EcWUf2Nacgf/4df/vracnY9Gf62OBaz1Ign1X0FtJXqYRfR3nSVeZghzOxPl+WvTdUXHoGtfEPjJHD2N+Qg7Cv5nYB57lZi/89J1f8Bfrp1STNzrvAf7UK3gf2rAuanDLypfgnQ+Pjp2eDB6+9XkwPKE+nJOlVRwndoWRfozEK4xVnySfyxzpTrziqr98tiWpK/Exa8ScUCX9++OaE4G/tpp4tblvkRo7w5t4mt0rMfGsbjvgzeY7AK7RNwX81fhP0Yd9B3IuTNhx9OTNLDIujs5BTvn/0SG/dT7d/3VZS0vyfo2+us6hmof/kNlPzXtXrCDy167lAK9uF9d1XuECeBmX11Fv6fVX8AjHfuJvqbfgB33cpiYPct4xeAglF7HfXlZ2/rwbnXbDXwqqS3yhQlr0RQU38Jf+CeEfeYP/2L61Jf/2zFel/pPxtQt8K+dM9NDt0rT/Megv398ISEp/Px9BXj3ag1xN7MIBIp6oOX+8fTd4TudTE1f4WhCcut8PtXjtnHKc+1F0GHlEic7C29nwA3vw2hvksT6hnZJ2h16BPxheGjnb66lT2ovRAzt4UA81edQB1Le1h9Tgfca7wb/19Sbu2uOb7H/bHM9waa+Cq171rMl/ze/Y+FDyS4y05J1Y06ITJ//whfjI1XXoyVufWFdvT6jxu1fAd7QuD8CPuFkQfufyaNQ9GPSY90xSWU1eYkhs9FDXK9it7q7zkr22g6M1u6Km/vjd4dQxvRALflbGLryfV19XvfeKxP3TZ2T9hwX7q2jXaPKCPGx7MeRReXfiBme+guMdvY3fubCLqx5sBjV5ewWXUH9qvje88PutsTOqLmf9FWqHnO7rxTrqGKEGZ89/T9at3pu8eK1pLvDwU43gA+4dCC5aOKbIL2PUdvycxj343vN84Mpnw6ijNq87dt4bz1/yQsxOo7DrqrzEDllHHQMr0wNw6n31iDOknxlVno1LJ+vBPFPZKe2+yMQ62Mf5yubWNcjDRCv/Xf1c1+XIXRZ9M2+N7Cfr6VPiDrtXEu9eW1FJXEAv3E3sPCtmLN479yXwswTe8EbOL4pqj168IbxwI34r9sX95vBMcwwkH+TFOuK0kd+pD/cxAL/m1R2ZD8eNt/hZzduhNy/nRg4tWy74h71Bdaf05y3n9FpeF+lHjR3I5XNGlHVnTNKxj/M/Qu9NOA7etbAa87v7M3rw+lP2S5XCrJuSF6UdY9QZ3rv3JO7tNfinZSbjp2TqKfvRynEBf/TSSdd5f2nh+6RznadyOZsSO8DaQRzb7uMNfz3eHPyZNyHY+5+oK+loNxk9+MRdCX9Vj3OCcam8G9zLIxJ8r4IvdXLOce64NfE+dnqVg8y/9zDqYbe/wDxp18E17DvF7rAKR+CXeBcjHn2R+pqWTw5wgKKT2affpmOnXD1FPkBEQYm7Ob4tx16clgBco2hG8mfqO4gDpHed+3CjB3hy+dL0u0UVcLuwbfCeAwcR76heXfL7jU3t4GeeHSL6V7OiU58mQTni7bNLI+/eV2VdeNu7yP8zLJR+2ToMZt37TiWfYukoaddeqRb5ANXy/iO5rGduBK4/LSb9vneCfMUy78Cx57nOt/OpGHW95e+HvqidjPHJ2lfGx3Z4EDhsgjJK5IV5D76iWakaOHHJDuBcHVqD9ycPidIvs00uV1xrJ+/zti44T7ePxB/MY+ynXunV8CimznPx8gphd5Qb6TqHKSc4wqge8M/CXyk538I4FgauufQi8ulzIPL50RXsg9Dj4LdT70fx08xHdvTL7mcyb/ZBz8h379mSOpZbbxKHDx4YBSfSZ6ai3mnwbae8Z/ZMyNtF8PWMdSfU2OEVv7BPEk8SeWqLnRl7svF74tQB01nnxQsTRw778Uv/xCiQAHusSkt4I1uc2GnPvyNn2wQgzws+B7d4PJi8hIO5ids2HAYOtCsJ8xeciXzHBqfhW41Phv6P+HWexz+9jPLUd3T0y4A9cT0m6z3PPngb05cwv+Pb4Yf0L6nE7jRSjcD/rNWY9dCLus3W7bbk7+nJ1dQ1bQWua4QmYv6m+fJeXrVkXzpmRhM/xHoRKrxprcwtzo3elUP4H7ZhjySeZa0+LfEsq0Z3+IUrzmI/RjulBjfYnJD4w/hnrPOtSYljzcgAjv3RPEj7JcOl//NikS/q/h492a8T9VovJIYfFwv82fZhBfkqE/yc8rxGndmnoV3IH4nYrgZHmzRE7Brr3TDWkf896rREPhIc16y7Bx76+brgXU/DlcaV9CKJqR9uH439YisK/yKGgziFZzPX+QqlGqho1yx+CnvxTXPZ545tHbC7rpMPpQ0rxf6P9pb4i1/wWBXtGrnncb5Fu9LYHzVzMI6pA8HJJ28hnvStH3wP/1LhP+/myir8//QCJfkT+pCuIgft2TZSb8QnK3JzEnW79aDa6OklKdS8947z5Jt5hoGf777myr9spkQeaf3hrxqpWzOuVV31w3YUUFMHuE0uiX/bepTnHNjG6bDDDunk+2ej7pF9xQrBR8xNQ6jf9jyxy1+srqTOmD7gO+MVg/OSjfmpsVvu2/BXImIj/6305KcvTk3eQ6e20i9r9yHZx2bXE+TBhz+iLkO3TMTZasfAvhlbWgnP0Kx6DJ6QWZdx2FjlX82H/iQc3KhhIeRUwvrYz7sWU/fqkes91nNevdYyKfa3Fo38mdfv8IOvr1azrlNuhM+RORN2g41zCvVcrnOLX6cijqvdYn2O7Yff1+Ii8mVpTyXxij9wf30QdQb09VXZB18WKOJpNRY8UB88GT9qXzvsmb1dJU9Jjzvv8c+7rVDWDz/vZuqsX2m/10f5fjxv4YuaOR5LnoTRySIOtmRZHxX90xuEgBsP/4Be7JiTevI1u+FPFVgOX6J7PDW878Fb4X2fGol9PqkF8ibNbCXrSru5G1yv0HrwyJ7HsR8qJxYeq/WpK3bN2qNyt2ffLXVaba/CRV/bv12OUg/h317m0fHgRUk88HOjp6aeznrsXdPTHV7RFuoYGRXVxEHMj1f95fmr7xJfSUcdSGOIm5r8sjExqIP1bhD75sBw4n0l8HfM9AZ2s5ehBC8zXrriJnV7EbdK74Fc3LlCjV5f+1DwIGvYFvyfTz74JWe7if7Su1bALh2YlnPtQlaxft4Fv5a7R4rv8r3Gj93K//y856x8tpWqIP+3DR0j60y7cBTcalMrNfkLqRMQx6wTwH4akcpVn9ypZD+Z5UcgH2P7gKfUqUO9mGvd0CMDPoP3XPCEBxi6RfwF3ewheXCaX8yX8j2rioyDw/2q5DHY8he9Lb87rAtvRQtqSl517eLUyxiZBnvnQgXsyIgX2D0zbWr8jgvD4dNFWwxu8vYhcrVRXNZxngg1+iWS+l36SPSLmT8e+m51XSX7Qq/sIE+ngjvvkddPTX2RLhup293gG3p3SVvmp+cgJXU49e+JOed9aAS8tLIG/MIB8WR/OeItFL/ULHoEXkDYVNGXWsgY/KkDDbDrx/RArrXZoqYOc0dXffrBrnyVoYfBLcfXVMMTzwrPwkrldP68O47fgA+RaQ71tPMWg/e8rhR5uLmpW2SunoI8tVXJ/jv9MKa4Uf/iUUzwpzkvpT2jkyue1vwh+j13MfZXwEY1eQGD02Jn1+IcIrPndMFfrfm9md/zD8TetDJfox9rY9mjtLu1D3WPMoXR71f7qffy7B34m38G7Pe1HmrO9WzSDhysYiLqSD92g7835SF1Nap6Yg+XS4F/neSMEtxA3x0Tnlhr+Bh69ejYvQ3d/54+W9atofx+Ui/kSnLOB9QeFxW8xt5kPvySCsHkFV30hads1JfPWtsNwkPRiwwAd3a0Lv+32v8vl36EfFvbJurqGAndGMeYR6jbMHMP/tOOSdQdvBkJnmtRR888dxp9diQW+mzMOiXyUgtmH5vXJhBPurQEuRztEf5Htxb4F2vOqJGjgTGJP1aPwP8NmkY8yWxHnkGP+WrwoJD89Lf+KeLVBw7Be5zcH3s2Nbwbbcwd9s+0CDX4l+vSx39Hb2/Yh13brku4tB/DxI/OFEi9t+15WZ9hydTwnS8ckHiSmcCD/I0JObD7414D96oQpORcjT8ufdRGxnfiTnC+AZPZX80ncN5D1wPky22cBJ8z0Ma50DdiqzkHZrQ3eN/wK+yX/aPIi+v2nHp4dwrKZ1vTktiv5Req4Q+860v/jxai/tD3FMQRI4aDJ/euwX6272KeyxxDzoetIR/pfA81PN+9y7DHZz91nV/2gDjjjrPgE7M4l8FI/b6vkva2+ss+sS8Z6Px5t3IshBewaw44eJCac8j+016GANbz5yzwX5avJL+nZVp4kKdjUK//myn2kRU+hfyOGN1ErtqaD6TO4PEl8GgXxgcPPRAEvlZiZvjPu75pBXhP4l5KeL9/XOZ8V92stTXRa/dzYAeUJ+5q9ulHvZ56lcTOsYeZ4CHH90k+mMOIgx+ctQVx0YKNsIfa+1E30+chfMoPHdTknTirUA9n7QPRM7bcA8h/6RgTP6Bi90F/bsfotxa7sP1p8Jyk1JvW0uXj77bCauTN2WyCA+idOM/anvQc/tb5wcTHtl5jv7U/KfOuZ+iGfr3wXUnc84/L2JeT9dgiI/s4YwXsoh8bifd+ea70PA1j1TLyY177cZ7uq+TIuQ/FicfVu68kzm1kKgqe8Ax/zChzUs35hXdmcO5p2ULwLe6uo251mBvyeKXNKe1trqgmv77DLNbv0L3IoXS/rvPz//2uZ0Haj+8g31DzQq5fy4zcye8ZZR3pbkfha9Vxpy7n8U/E1xq6y7qwf+xBvYwD36g7c/Ct+Pm6570v8vcnCwUXsRYN/yHPr13ljdxLZhG+t5F/Krykl7Ow//z+X2tnHVZV1639JQYWdgdu7O4Ca6lgtyIG6rbFRgxAxSV2d9d2q2BiY6JLxG4UA1GX2NiNWN/n/O3nXIf3eDw+Ovc/69rEqjnnmGPc4x73QJ9H65eT/PvyeeR/hsJ3VdvQ104LdZNSj6rsiEAn7BR1mMaiE/BzPoQQZ166g75xwFzyAXnGC90ppXHeWPH8e+6Ac/i3/yB+nuv6O/F355YLfNdSoDE6N4XceK8T8sqpMxi7gXrvw674yceSgmPE9pTD5//nOvOKE39u82deH2zDPBmxHL5w3Rfw42Y4YBfWOJOnzaDSn2VeJPH8vf5CB8GkjiauH/1a4D+W1282yrxfJaAc72V8ZuxITDXGszf5V23WZvJ4A8qxLj+kgF9Y6nOwzPvQ4iNFfKlm7Sfen5GVvvEmv9xc1z3IKn5f+bMU/EKfswc/rewr7Nr4dHLyeNWS0h9r73oRv5gHBsJnTlmB8fyWTOg6qYM3PRXXXfWY70fuivVt6rWHeqcyYXLw58x3qK8ZU4860Yu1xDxTS34W80oduFXopCgh3wWeZIxvBk/ad5uoszBv9xN5R1Ph2virB/ISH707KycP4f4U3G/DfOqdrniTLz6cgrqrDxXpQ+CHXrplcnryoaOiwL9KjUYv/HxO4cepppvM2wsXpOjpKqkmkT9qfRN901O2/tjZ39H3ueFmvhd5Jpf3f78yuimPNas4fj1O3rSHDd/KbpGja9vKFm8l1GK/2NKQvNYxWx/SavSB1CdXkdN3sl4N9qGpT4nH+twXfrmx5oHAWUz7ksf+OFq+zhV146acHmL/0PqidyDro1bbi1/TJQO8nznu8Mzc01vFfV3y5j2PviKn3iF+ItcrXQX738Q10fzUAk6Jfc7S6Bv1PzvXkCfI3gqdxnbDRNysJ8ynn9fbQPT0H+UFj7oQIiX/rwWeFnGJ6VEB1mHuHfBWk8ITUp5/EbpzpuhQvq92h4+2MomIq82h9JdUZ7nK2bfPnKROqUAI9Rpl3di/E3bIyQtdfyXmo+pG/xbV+zr8Yi83+i2MfS/qTpVHpclnWYuF/zhqKb+zThY1B2dYf9Eq/t64Tjw3ZYKUuEtv0AF9/vwr0Ut5Tj5P/VA6cb22T032i2L4zeb9FanLqo3egfKkgLhvxaMteguzWlPXMKOtHHzY9lFdbP2gk9jDv321Gvvtb+sn79KVODDEpo+edaGUfcQ4cog402UieZIdkeis5yoKX7CSvxSdFyVvKnCtGAWe9dqC8HgHZsbvnVwQPOfsLHTRXMpL6aOqeE0ifulcAT7Zm3TUqXuXhCdb7Ci882tn5OTjxpyjPjPsHHj++/vgkfGN0S3wXIIf2juW/HL8mxIyrqucf4u/8jIleY1qs+F/5o+lTrLxd/Koz/swn1outorr5iqryri+7tpJzEdtsU0PrVsR5nOapXLihCufyT90oO7XUqqBsKMmu7HwMX0z2nDu/PhDpwtJ1fXQwlqSVy+XGp66o7ewB+awveE/jkYvP/y8ozOJ557UlLNubB91/DH6B8Q0oH70VDA6xZ2as06q3qgm43pa1g/wiSJ8qbfJeRC7syCAPJmLrT94q2GM8/lITxnXVXqNBNe4Wp8+Mi3RvVP06dTxtpkOf2ix5Y/GVRvrQB/K2+MYl+sNiJNWPwDXHUDcpO9PCd6klf01zvXiBXnbRoWoq/OzsH+vf0kd05T61F3FgGtZPn7mPWZZlMjf1VtZ4M98fMf95GkixtXSDz6c6urIfdS61u2X9/ObH913GrjK6PHUn3jSB83Y68b1PItJ8T/0lhXZV+Oq4w/7HqEOsRD9cowHu7mPT5vgR25clhh3K9FF4Jz6t06su2mPqRdy9bDpB9E/RovNyjioXagDy/ZWXM8U7oGu3thh8AsHZUXHoepJ9KMmHBbxol5kJPXwL6gnU+NO/it7pfs/ZV4toP+KUcuP8+zZRr4vbhl2K43fv8Lx1VFZWdcfk8GL+QKPS0tJPsZ4NxUdKq9qYrzU+fnFujVePGD/XLYPf6yuo/Av1Z7o+FruhxHnZWgoR197xyTws9zZmaeda8O3K3r/t+Jwbe56/NTDrfAji2fgePlI4rjwwrjh4nvmCvBMt+xivaRCb8b88Sn7uYM7+26Lvcy30mfwO1ZklqNvP6kcOOES+vqpTdHf0K+YyeuG70JvvPpq4qKH6+i78GUY/lSrm/C4fdJyXyWXJ4pHLd086R+zgX6eSptF2L3bUZx/6HjWaZIK+BV95jGuzWYxH4qamH8V98up1z9/n7pV3xT40R9aUVdaKqVYX0ZtC/004q+C060eBy8keiT3+W0q9VMdDqJ/FRRH/vaRW2IcbO8U4ujOVcD/xs1kP6/AfDXdnUWeuYwr/nfv/qzrmxHUIyzbjt1M24G8d/VRfPcl728+UAucbv0d4jP7M9TDTg+j/sxnBf79uXLwB1xykB9qeZzrTTxJHuvwccG/tnSqxn5xvzn7fa9o/i/+G/xtvyCBA2lvaqGX7nJc5NfUqJ7h4v+G5uO8RS8Kvo2W5Bl6zLu8yTMnhFDHudmRfaTOWPIjpsGJ49+H19BZ8X4Gv6Pv3L+rq7ZXsVOZyzB+ih/3mXoF77lVBepGOy/8K36w/rEBeh6LT9LnqVUX8KCTYegzr9zBvC6THFzVa1SieEDP8pX1laY1ecZF4dQ3xnemLvDmGuoC57e1iuc4VFHEq/rj1fQJfH8V/3gO8ZV5Rhi8nK1+5AW2XaAvyqXH8EyGuFF/MsEdvprP/t/aJ9WhBVnHS+3wU13yU0/k0pznnfVGDj5UuZ9Yp8abvvgzflbirLKriCN7vuE5U09nPQw6jF+y+Jb1T65vtKgl/s+4X4s8fRKdvM2CVey7G7v/dL8zpnizvhPo96Y/T8L/zUtP/Pnow1/pPOil4/EP6yUln7eoNHUXRzpQh5w7hvcRY6IeeMwKOfFJYF36mx87hI5Qz5zUN/S7CN45dzF6tT4n5eTzQ0KFnTW3zUo/scNRjO+Hi+z/eUZI0b1TE6hjsbxGx8tYv5v31uQo+1hoiJw62DpPxPNoHq+p07v8jPl7vRl+z66a4JixYfAiIlbJ0ZULK0hesmM09iCmiFgfpintqbvyaiulrlgrMkz4NebqaeB3PA8HZzmYnDzNnfs/7/f3b6/TzZf47SB5AWPYLvbP2Pqst1yDWQ+948TRZIkibnm7x1bH176HjPtQB6RHj36GI30aW79kPTyoSZ37olfYiXs92LfmNZdTl+DmCu96VBXyU8e/4ae16i+Hfzo1GedJUYrxuhbH/Qcd5jqRr+Tw+g40ZV8f1Ik8tPN7eMjp3/xW/Kt5l+A+isPXs0QPw09blQ+/6n40fYam3yIeWPEZfnC/OPyeWdPY998sBJ+rdUHkG0whHkKP1nK3m9CV1xrZi7y78jFLUnGsk1bUJRg9hgi9eSX9UfLYH+fTx9RVgZ+8aiJ2Ps0JniuqgxQ7bDJVJS+23Y64ccsc4sMG8XL0v158Z1zsbf2tcubj/g9OlaMH3K4kvN2OSalzab2D8zc+9svzm3o/Aeebnx8eVGQN+inlTk0f4ySdRP7PvFmBR9g5+rH4/dnzjOP6zra6ML8EcbTP91pcr48LfSMaZcQ/euImRe/UnM6DeGFVMHFzMif8gp0LwWPHwLdU3o2Rw4s/VlSMv+56k7qPyOLMj6eN5ez/007hJzqns+3DZ3jvFWoIv19b7wlv5W2IqH/Xm30T71+b8Jm+RWHd4CvE7YJ/dWnzYCn3ZfuYsiwlXzfyMjqyen306qdbRbyiLh9BH4SJrajjb72MebhxqZw6n8b9ifOblobHuzQFcaofuj7qR3RgjM8u+PX+acif9u4npR5E6fNBnMe4co64b9etcHFdx2T0M+p9VhwN3xr0xzbt2yzluraPOnQaOkZ2PvhLCwtSF+CfkXglLlaMv9FoCf29SsY/Ecdtl+j7tWiJmD9KzaNhMu9LWd2APFmzZ8Q18+iDpL+8jz9wva+c9x93nP1mMLiNZVsReB6F1pOPeBxJvnTRNbF/qw1GUM8xe7qo9zK7DYr9cTQ9PYx+xFo3+qwbM9Ahjy6zVcp92j76+nL4o3l92N+P3CeurtNbTh1Q6zDyNTM6gKtEpgPnCawvh0/9uBZ4gl0c+1+SlKy3kNWL//v59Zc18evth2KvTmyCFxIVD16xJhTd/5PO+G/RxfBH1h6W0vdSD/gI3pXD1uc4E/q9RsOn4L1BGVivrr3xRzKtADeq5Scl/6oFOApesHb/k5jvJuen+Ekp54DvfBnJPtHtjpTnVd8uRT89CLxTyd6K50pZCd1mT/SulGIz5dQv1NzFuguoBO9w3T3yvRNtfSiyaeiabJkFXlpqpxye6sMx7LcDR+F/KjeJe0aXBf/4UJI81uec6EGlvs28ir4j57n/uY+St3nu1WPAK3PRV8BUr2oinEF/EC9wNbXhHuzgcXs58YLDHupndzjxfEXryPE3kvekj/eKovCBbs0QeKNubyf2CVMb+kEZFZfSL2JxPLrfkUvZ3zPJ8bfVQ8PQD4lHZ0Hv1UMKvqFPuk5/52xbxXsz1z0Pf1clTrFUTyl4JuZ8HmLfVj4npc9p79Uin2N4eEuJ2//5aOWdeK6Gw8EJQq3Mo4sGfrHqAG6RqQL5rorw/dVsLuDaD3LJqU9pmpp8S8x6/IglQ/5VXkM754BfGdgCexul8J7ue8DTDnWU0g9aCcovdLj0WtVZf7kyYs8bjwBHXXCZfEZ0MeKoHqfl7Ht2U9D9nfGdcRkF/qmN30Bedpkv6151loO7evalrmPvIPCimvj/Jqf74PWfdibab//4OqeLkmc0GexHgTZ+9FNP8qbb1siJP0NHkL/Za8d9T4tiXuRsBN6RZbLYL5XptVqJY10T8/32K/axXKfQoVoQgr059VrwwPSd1UQexDS8Q7g4f+1I/i99byn6k/rkpeiR2a8mbvhQAr7EnoP4syeaWcV3r4zgh8W2Mg+KtPvp+tFXHiY+TTYGPL1EeYEfqxbqm/RhV+HpZ6yAjliwCZ2bcqelzCvTx9PoebiVJX+4+xX5+rHFmMcFKuEnBNeSghvq4wKEn69PfwJ/+FZF+JHek8GDTj5El6EWurVKPxc5+8eUfNiJcez/ejUr+ZdxnuAffV/I0YVxbkF/lWEfxHs1VhW1ivO2Asez7PXlOp2WkFeda+Ofu05k/yxfRdQ1a5GNBP6lDv1If6ZmwdQ3z6++Q8Z9/vPR3Cujf5JkPTyc9OnxezNVIo86pD3313GY2PcsQQGCz6C9aME83Lv513V3Sxfjd+dsItahcWYNOPH8NYz/mBXkS+O+sn7SYsc03wNS8uiax1t0JWLHgm/tqQF+unGgFN0x3dIcP7JgSvCkYSHwKj/MZZxaZCefHDMMXsG95cQB9YPh4zR2Jt674ytn3+61StSL6pMnkW9YHk8cnKY199GX9692i4QHG+Uu5T0rUx6ynzcfLcbPnAcekuVkSvypW/QNVNpPGyDleoMK40fPGYr/s7sz/vS1HVLiNWNGd+rOO78hnrhxjbzh4dnwK8YsY1+5vECKPr1aKjf7wLVaPE/bFuyHD0rJyeP5GELXVS2JjpK6xsR18muM091P8EWt69nvlT30h3dUhZ9vPjJb2CNtcB5wIp9Fwi4ZG4bQp+1FGfTWjUHoL0dMs/GGjuF3zZghBc/VN3rZ+l5UBN/LWYK4rq6CDurIZeSfUm4VfoBZdeU+37UXeI62cYd4Dn32E1FXY173nHrieXL6xqgJpYQ9UNOr2LtTp/APg6/C2xiYlP5WM7IKnFyJihe4hxL6bZuM6/+P+wnRsKsJ3tSzbUT3SN1bHRxgSAB24ZOjHD2cz6nYZ4csxK65pbGK63jvw+5voX+Tsv8BdljPLCe/uqcg6yS2GnFQh3PYgzFT4HMUzionXkx+FHvf3A0cZ0hD+DMRhvXHUf+UnnU1a5qc+qYSlcFN/RT4fUkPy8EL9s5mX4y5Cj9yGHw21W2q2PfNZRPY7w+1F/WO5pik5HOcvMV3bdBD8LvIj+TL932SozN4piF9JYI6MX6zOoJftS3A82eeKWe/qsA+odemv512oS953Cel/H/n/Eby2eT//M3wCFseoO5lmF+gjPvTN3Ug7s5+C92B/SXoP5eqB3nSgZ2F/2WpfJb1W6+nnLrLGhepgyqRnLh5vjN4bsrbv8w363Xu8j6Dm/Eee/YMlHE/yv7N4Jjvdez70jNWcT85+5K/KXFeDv8+51qhm68+nAmftlMAz3+o/q/1ejLdAD/yHMh4uVTo8NO/96zmJc43EN0ac0zln/OPHtyAr/m8OfhwUVd4oHvvivdgfpQcf9l+Nvvcqjv83DeK/fuzHfoZzTzIc/SrLOr2zHc/opfU2Jv8T8Mmor7VtHmVyKtbqmo5fxxNg+xKifx6kspVxM/fTHQR9e5PgyoJPcDM6Yv+OKojj6YTP19Evl7L3kHsY+qGm8QNYxXqCCu5wCuuVFHE39qEJcRd8ZFWcR+Z3iTOf9m9YP079OY9fO9BPmtSK/SH/AzG/dWcX9pX7Zg/+dfd64j35hpy7Oakb/CCPVuL+9d86cehFTvIfta2vBw9j/ZdwN+6X2Benaglp964H3kvrXqf38OLut2k//v47fS37uTCOBSZRvx2bjR8B1M4fn7p54x/++x8/0B9vDGP+lFlqD/1zd/96Y+X8aTQi9Wmv8kk5lNurfCPo7ZiSnkx/w7tEfPO9KWI+G56s7fgj6NeYX4acTzwUPA+TNYC5JUbl9n935/LnL0b+ZWzOanXvbQAXKEm+iL6+TbMx6wr4RFVTGsV3zfY8IE25eXEob2TCztiavsZf3V5a/zsghXIm4Q4M1/DP0rV4VHeXSIuG7YBP9ypBPZ5de7f2uf+r4+WYg95+/nkHZTIJdSlDbwDv63FA/zcWqXA5UrCT9SCu1O/WSO5iCe0/tPg90xLyCLG/VQXJzEPQnzFeGtDy4vvetSGzOL3Mx9/Es/zyhU902RP4PFl78I+WLcudQpK45/3uzk+kPqBT0fwL6LX/dxu//OcDYbjj7inYD16jm/+q7//7c/pY4IHpx6kL7seEcD4rFotRSdOL9tWjIep7nbyr2X3kwcvcPOnem1GXfrv6s8UwQvQp6cUuieW+PuCd2VaVzq52Ac6mhUxHhFLBX/HXG6VWOfmveHoIC1YJnAO/XoX4uQ0ihRelDqtMHZxYCTxX6XP4NKfByXSN/rjz/NZvcV556Anqc9vYf1xNKdsxrp93Im6rqMff0sfXX37gHqzmgv5//a+9AksNZ+6omU+4EbbmpFvWlxS5NdM4V3QeS6wSvAPtPtWsX/rV46gUxFCPso42D3kd+7j337UHjZcKvwb62rSc/SZSuQJF8fn38W6M/J1hKdb1yoFB9FO27PfnF+Mvaq0mX2q6jw5fv9eR/CDualt9iqJnHqdU/Hg2vF34GcPsNWB5L9IvuYQ9Qxqg3i5OmsBSdG3DXgKPv/MCzv1+KX1x9G0cQR1yGHnibs/2vqYLa4ocEJ1xkPq5Ff0hLf12CRHJ2N/c/jB+RuQj066Abyzd53E+JxuyzNGZgenCI7Fv5tTm3U2pCX+ygb4kPqIk4zbkmRy/LlCZ8CnvXTw4TdZeY+WEDn6AFHh8BMq5RbPY0w4wL54Zjw6Eh70CVVLyYlj9EFJxfip2e3Yp2444GcEHCOuOXdaju5D8Q3sszHn8JNaF2KefXpFPUCAGftsyiynv0e2DODmBzpil891we9+8oxjxRrgPM+yMk+cpjBvymeh/v/2Y/ozX47AHy1RW+B/hnsK8AwHD/oIXXNDl+/7wkAZ961NvSz8L1MmW11G/RUifrc8bgsOmGKS0N1UIvughxialH7K3xZT573ygdS+vfrDYeD+J6dRH5dhZfiPo3Z0FDikmpz61bWxjKPpSV8Z11eHFGNc8mTHvykVKcfOJPjCh11TgnE7OJv9alAkuEk6L46TQsGzu12hD1TuTg/Fz+snCH0388Y74mhkiBD+qPnofaHrYvI+KpXvp70YR11YjePgNmOLoEOYPh/1faPXUk+28wX5wSHnGZdHxQT+ZryoIu7b4l/mmbg/5/T03XDJQXz/Gn6OGnRGLi/Fbiv7zIu29IG35GA+979HHZ3XbPqcee0SeLo295SY17rdbfG+LTs2ivdpGbSTfPbZ8XJ5r6H7wJ/qNsE+3DjCvjPtkRxc0vbRXk5lX3qXknrnoQrjc64B+fnoicJP0j8f4b14lLHFC6oUfrPqvJn96tEi4sXlt+Xgfrs3g/vFLEN3tlUH/OrBB8ZLOf9Db5uOjwP765GkcnX9joRYf5zPXGUX9U7FamMP7jQl33nZpmcTukbKPqtu88S/SRWN/16bfopKJDq02om6UvonKSFjwZn3DCWOGvqUOOKLI36DB33N/3+gnYg3qpakL4Sy8wl5l7B38CCfrMVfnzFJCl/on4923Y332+Ul41u5sZR5o8631U309hfz0nKvOvjThvb4q4uGge8lrymnn9PIVTYeZQ32yXZd8Jv2oBOkP2ooR4ewvgP7YWf62qlHExiPvd/krOfH9uCRnw+iX5A/K/VxRfcyb0KvyenLPC+lwL/VDuepr+lK/2Kl+l3yKjc3yllv9dGf0J8MAbdb0wJ8uNgj5sekT9jDmTeoF9Umsd/rXvSFDHMUeV9zwm6h56il2izywebmTtQ7fKstt97B9jEaXsD/Xl8CP2ReS5uewFO+n2lK/bnLiHBxP1PaosM+eRe4fdF7seLv89QVR2VbZfQgs+0kP7fyGH2YTsEbNa1cSP23/0t0CbLMx8/4gK6Q8sBHTpzu3wkeQX6bXvRJR5vu2/5f+nVa++vgnQ/qkydNmtcq/j7iLPHyCjM6Gwq67+rTQjxPvnLi7/Qqbf5oP9c9qfMxbRtC3bT7XZE/My09hD8yoqnAkY0tw8T71w7ag1teTQEvbWM0dq1HFvDU2KXUD/jGivdr6jqMfktRO2x9RXP8VdyjnW2LPZuJ7qOW9iP7cj74FJpnbzl5hpHtOE+12Zx38xWrOL5vQb3mlHbUU+32DhR/NyQzeeJnG2x9sulnbYloQD1a6HjyWjPSSeF7aMsWUF/yti551tmh2JVnoYEyzq+kLM8+vfk29iMiCNwp4IyU+ERtOZg8++Fu4r6NIm+wvyHd8PefdqTfZ/5zIv5UevnRv017jr78yWHwQqYuStQHQPeJwf/YqbD+Hn2Qsy/VDWH/q0t/MkuCGzxG+yTgleXqkQ+u/iCRXdeqaayLdR/gnW07Bc6fC10E5YgVHf9SRwRvV2kWRX347AjqL4vGScFv1YKtxf1bxgTDyyjhBJ+1yn7uww/9DiVPOHr8TeaK9W35OJ+61jPecvzSxhk5TwMbz27dLvZF+2s8r/sJ8st7CvI+T27g+7jWxMf7nsup/3KKwt7umY5farcaXGbTR+zZ9FNSdMfVUl7M80b0mVarHZBTT9u1mLBPpuvXsCuVFzKOubpQf7xhPnWsg1V4Tc/Oglu8LfVrv8PSEtwxXTD7kFEU3lD3lHL0GY+FiPHTL4JTWXJ9wA+b05t4YWQA9jbfu9Eyrqelfyvy4eaqLal/ONwcPsDHtuiunYogL1N1Ges4iVVO383nE+A/pPoALjsQvX2t9Xo5+gRuTfD7wsvDpx20FDsy9iTrt8tBgWNZHm2FP99uoBy9sVkP4cNdtvXrq34GXGF5Fzk4rek4723OXObd6nPsO+sfytHNnVuO9Vc3gnGpWllOHcrX7eQT609inhVfBo5zDv1Ro9EIdG310tibqzml1DNqJXuB5x5Ohv+fuccv9afUTC3hzTxyZj4aH3+5zvTvY+gPctsCnvxuEOf/6N9ffO+ajPxEv9vgCQOpQ7G8yU/f544pOG4IEX6jOrSpwHPNY9YKf92S189e8GbCrI4if73STfBolPvti4t86oU3BcT3VzdEfttUp6Pou2CasR3dnX7DySM9PWEV96PEsc/VfmH92XNpO+YTV05PJQf/2tqE9XDXjrgydI8c3tXFCjV+nMccFcg8WvwMfS23guFc5xX12efhFZvm5GTeZfFB18VNwT8662DjS0cGJrovvaTI5xqz+1nF3x/OTF3i+jzUOY2fSV/d/HHUIW/dSp+H9GdFHGAK6CziAnO28/T7mHEwu8h7N54ID+VZEjGORrYlYvzMBbpnFccM1wQ/QVnvAQ5YeRP2qxL6AHovD/r0qbM2JLrfv/10vGDr07mXeG+erxwe6nmz4G/pBbxY1+d7wyces1bgnUb6/QIHtUTXEONlvl7/t/BdfagNjyut4P88m489jHbH3r414IOZTOwnyeqIuE0f9QndSZ8w4uigR+TPd7+jL13XU8T7xfcKXokWlzmjGBe/84zPqa5inRlTeoh1aTmzT/Q90SaXgU+yvyV4zNDaoi+htqw5ecuVnVlXWxrL0fdPr5Lv3ngPf/tyELqj1fLY+oE+koNrFXpvq3+P4r2mvMU6vn1STn5ue56u4rxXuqOzkbsS8/7uHHDyMh7oT9TETzMH+oNDNqJuU+1W+6/iRa1GCfqklJ3N/HyXhfh8dA/8+qgLwn6oSesSt+a4JOoFLUErBM9FqXdZ8NDMKRV0hdqsEvo05uXO9MMZn4p65zTT6Et2aGDbv7nffz66x2KBk6qh9tSvOpQGL3qRiXxmwSFy6i3XqsKPMnmOJJ47T92qnm4x+bLvSeBpJnX+rXyjHuXCftkwPX7LZep8jExNOe+oErznKyEc52SknlDfDG9wVF/213qlEtVlarebUW/er5FtvKpQb57jEHnboE2C/6LHHEKvo2y0sNeml+U2/c59//YnY1nigyYq+e3TX4gTpmSW0++4ezf6SZQqQ5za/gLxclG+62/C/m7cF9aHh+cyGlzNrx15kVgHxsd5vhgHy91o64+j2jiQcfdZRnwYkRN8tdc0+AQhccLeGamTEl8VyYBOZw8vm95sm0T+gF6lPfPDeyW4yoE84rymHHW5zvdmvM8Qezn+bo/b4j5NcXb0WXNaDc46bhi6iDlewHdr7iGnX+iAMti7FtuJdx0r8FzRnuB0ufRfPpf5fl3e1+os7CdeKclrjHPF/+wYjh3bsE4Oz8VzOXFtljTgDw1tOvPr0kipi9ZethfrQnuWFTvSaRx9s27sBV9pFAdP7XQvOXn/pS6i37IR3A9/fPY5dGPyX0Z/s1Jl/MW0r9GXbZAMO9XouhT+lfZoJXXg4+uAS7jT90D/OgX+8cLpXL/tGng+h9G116sz/7V0q/HPhrnKwXnOvMbPM3VFl7xhDtZ9u4bYlW4Dmf++E8B9fFU3Kde9c5y8pD15DHWUBf+pSyn6XGYMgz9xq6gNb1rRRsp1D+ykP2c5eG2mfAHEScUsVnEfYcnk7JsLU2Onh9P3UCufD35GwhrijqaejP+zVSNlXO+/rjvAJPZHy6zyvL+Hh5lXPkPRmajibutjOO23+nTqu/qRr3kfa8snVJGyDlTzF/zH6YPAOXfnhX84KCX+86HTtv4Nw/E737jIWf+lbLjluCe8l1HVBZ9KqdwGPlXMAsE3UV0j4X2lyEVcUiiKOL5VDuy0xQU/sWJVObogne6LfIJ5fwr6WLjai/VvaTKY+tgwe6l9TdUmo/GvvJKI/IHl7St4N6OLCP9IzTeYODZ5TvTmutNXQ9ZHs9D/3dhTGL+1ZBvy5A+uy6n3Xd+UPMft4+yzVU8TL9x2x65nqEO9SZW+9FsITk2f7i7jqZOz70L/q0M2ve5iviIeMd2ri95331Ry8LxG8HEskyKxR+87YX8j0UlWitEHTGvvhQ58j+ys46P78INNF0b8OBoV45mfvtg1LchZTnw285zoC6Y1roN/9+YD+9XiDfjZfjXYt1u4Su0nozmYme/XnlFv0acteeJVS+Tk116uxu/z74AedctgdGGaBYm4QD/qQH+wHoPpx5Y5PXqU4+JYN6+2ScGLlYdb6Et0FTxXj0NHXnVqJgd3VT9QV97/ITr2ZXuBf2W16a8vQa9DaZxF4ON6VCHqeNdcpv58QCU59WPOfcCX0h3mOdM/Yb602EgdjV8l4tZjh+TkI20f1b8c+EjOvayLNbnBR9pVJS8cV4G8QBZfeJv7hsaKv+/oL+J7bfJBcbQ0XiV+rt8+Sd2Y0yW5eo43qddUz4Ifq8s/y8mDLE4gjgp9yLyq3O6n88rcOj/2bNtW4qumVdG3qbCQccneHL5UywK/x88aeQ2djRsnwV2XPYKfUX8COjF+YZyvwF3qNDfVJA+XY7tYb5ZRcSJeN2d+ESu+5zvDcfcjeCw+RQTuairiwTzNJEeHVe1flf1n6hR4oJM7kmft68L+5N5Tik6IWr0APK5+bfBvvLMnstdat41i/M2rqhCXjuhgy195iTyB6XUKMV8V13TiaD75WrwP3fBlPdfIwrp6s6a3jPtVBm6GT9G5u239HJOjizDKlflZhfppfdVX4pzDxYiLSzyTyvc0XqIXpB3pTdxXOCvxZsP+4qi6Lxc/t+QcLJU/r1RrT96uhB/jPfd/4V2U3SLiK+1lD/S1qkwU/qflZDTrwtEL++NphWd0qxL+yk6PX+sp/cuPluKaiMvVtY/wwyte+K19V9t0nnqdDn3w6581TewnVX1NX800e+ijNroH+e3TQfDv3PLBP/d9lmg961dW8f66lcYutbprFb8POEEdUp+ucvRApmUB52kYzf2HoSOhL5gjZ75n2izsp1ZnhrAvxuS98L5K9CMfMv0UdXzqdnTpY3eiR2OZGC5+H5NJKl6pRhUhn1VsOTzMhObkESbdxN9ytEjh45r9+1rF+Ve64de2SSv8KEuOe8Q5q6Yx/tc2Mt/UeeRPPSezf0d0EnbQmP8cXea7Q8GDszmCN95o/1f+kj56H7rOGTbDuzi8ADz14i3mQffr9G14Esr+OPIecVoOT4GTGNvXi+fTtNXEHRMvM+/T+rAut92Sgx8ZBcR11IX7qQc98xGcqGxO8mbOc3/qh2v5+6Ov5fkYvGPPed7johfwO5aWoZ4kbQD1PWcvCf/esqwn/F/nPcKP1L8o4DY9v5OX7F4Iu7loBH2CPzijLznTDdwjwI18Tm9n+uiEmejvs7wX9nV7a95L/4LwLHt3QlcqYxTP8/G2OK8WvCBc3PdOg3qJiMuC56ZX7gVu5x9q/e/PrRZdxvW8TMTv6yuJ/9fSZcDPTRJKHFNyPzhqrt7/Ku7UMlIvZ7r4HL7p5RRc774f/nW+BeClLkfB6RwXC/xK75ed+XOGOh2t/2t4ovNrcwy6RzzVY2li3UTzQeEnmR1ixXMagU7YzZ6NyM9FXCOvv7cv8Uz+QPQg0xrw5pwuiqPq6CfG1/CIBI9Ps4g80mZ4TErDBvA0l08Epxo/iPxLBvp76ekSWG8bnf7VejM/W8S+u98R/Gl86l+uB3XsLnSZl/WCj5QsO/p40dm4rxLVyCPEXUF/+9JhKfu17toUvcW3/VlfLU+gl3K9M+9hS3Xez6uUcvxz16HE/y1iyK/1XYGu0otx2LuzO+AH1+2OjtyENPCV0idJhAfptYhnja9z4RW/GsX6toTibx/zBwepHCL2SVPf5+gEH6Ivk1bFHV5KqxHoBqc2WPedXKzi92eSS62HUi/mQwfrnYn+PimawwducJv5O9YjUZ2J2jUP+M39QHE/SgT9AJRQfzl5oKwdAn+cR//aHr2PR0eZT97h3EdnT3cZ19G0j2K/VTfsJP4ctQ69QK/lVnH+KhY5+0T97mJ+Ggkb2M/b12G9D9wLj3BALp6rzQChq6g2eiXsiLFrBfb38X7w/yvz+PvLfiKPp8Xdo59dEngv5t0niEPOtaUOtY4Tetez4ONYjs9hPq2Bh6R6LGZ/erpYyvrRuo6B53evl5jfxtgs+M3xY9jHt5kZR78F8Hw2dyHevGTFDy25C7+jSU/sSp9Hwg+zRGYQ/pde9Al1Hva5qKPMMw6/LFMG6vVq3gCH+z57hIzn0SfVoh5xDnlVk+d6dDhN4xiHswPAT5K6ydHF2LoWflwt8vHqve3oHDyJQH9iZnn24VXlxP5hOl5I4GOKV1b80ur5hb9qPpaHPgxTuoMr1Xf8uz6P//HR79l4ebPp46cp88T+r2b8SJ/cvXPDxc/TJIUffqiAHFx+8in2V3ManudBAvFJ56XsY9VcE6/XrCvgZ+2m/6byxdZ/bsmjMX9zP5pDc/Iw+dAPVII+EAcNLQSP//5UKX66UqKi8Lv1Euy/Rr4m9K2dQ18ao29mdAhcl7LPTGgg/B3zpkH8fkg6/IRK3cCLpw4H5yl9Xk7e6MpXxrXjKHCyRbY+xEsyw7coTX2fOa29VdxPZBnw9LCv+J1p6GtpXKI/u16/qVS8UU/hgt/i0p19Nd10/LmlL5g3BWtJqYvX2z+jr8FZHf914RvwvfaS+mMMaI9fWPIwdrRcf+piFyYT69u8/T123dTJKv7u+s5/VVek9SwEv/dUKXTtwjYTL7Sn/4Nl+Qv8grX5wLGaFGj/b87/v34qF6MPQp904PjbbHqmWmPqkFKRB5b1UbfOYP1XJq9vOdAcXmpUG+yXf13q3y8vlNIP839c/1swcd+3VLzP3cH0O64On07rS/7G5GzD29oVHy7lPtL3FfGUpWhB/PdlD7HLfrPgN6RZB3+18rnf0tHRzN/IP59WyJs/3c+6n314kJT7LTwZXkb6NcRdTX3wUxpFw0vMtgq72z1Ojh/8MZvgq5lmwC+2ZH8EjtAhApzcZxvz0eu5Tfd2hMD79OTL0T1LV+GN+HmTIYLvrZ2IFv0M9U99he6TadPnI1Lu0/bRDqyCf/z4LPvgshPkbftUseUL+ojnUJ2dwG97ZMSfH9lF6PWZxowU96fWyvZB/DxZoOjrZ2RP9Vz83iFK+FnqthGsS4+cUuahXuKZzb7nYr9c3ob9ss5hOX3jgptRv+Q6gv6pTe5Qd9pjLPhUxm7kP1I/hec/pc2dH0e1RAK62Dmqo9tfYYjwo/WBNl5Ci9RFZNyfWmAj8zWrE/5dzVvMb+dTUvY9o9UO9tGYxeAMpkfUMVSeJ/xk7Vxl+p85haD/n/QqPJiVb6XoCGiHovGDLWWZl0Ys/kbH4j/Fg9XkKYgXUlegb/DS7fjV45uBUzW/SR7Yv7W4XyXdMvzL7MfR4e4ymTjhVRYpfS3UWPJ8Wiz4qMnfTL6nfzPwqaMacblWHL/Ft6H4O9UjJ9ff0VsOX8mzuuAhqacixLhYLpptOpPwoZUaeeToPxUYyP2mGAa+rC6Avx9ugE941ZPD//BcRz71QVqb355VTj3DxNKdxHlX1+V+y7xjf+/TUUpeULkykXWp2/rEHESfTfu+Rg7fIGEwfMsy8IxMpZqAM60YSLx/gP5Q//lRHYaTr4yBR6CUTkf9y/7Wgj9lnuIk6uGVmt9EXKiXOAKfyLO42J/M6goHUZ9kVnKJ+pcrDvlE3VJQUvFdjeomfm8cWsy+diEK3aBkc+ijdYK8pz4tTsSbSurH4uf694/k77pcYT3UWS+FT6ebcuGXhByAXz/cbP1x1GJDyQffPSSnH/TdKzxnv3qss9NvmVdzlv+Uv6OdP83Pd9v05uKfyamnsn3M7W6ALxY5L/xS9f0teD4ZzmIfU6Hzro85KvAfY/dhoQdtPuUgdD0t3i1SivHdfiWVGN9+LYT+p7E65VtxnikHmRdu4JXK3HPgulNPse8vHI+9U++BsyYL/CO9ES1BFfPccDsFjrLoObhqP/I72uyvcuqM33fELm6gvk1xtNUDT04nJx9ZMgvnmW3TK20x0vXH0XS0v8BbjD6nBS5leduMPhWLqwqdL+NsBuFXmB7fFPuvZbQv9U1HE6zi94+zMc4D8tO/qfgj8IyuLcjDP2spx55dHYkd0zPir2f+wv7cIKscvZquU9CR+ViRfGqqztSRtnESeQ6tVEWBQ6ph3dDzG+sr4j1TsaHkXXNXYx1NXCOFz64sfkvfheOL4MmPPQkuMfYY41dzipx9aCN9vMzHN4K3lBxA3s7J1l874AR8xQyZyR/ElJZjJ6psRach6V3mjd0g4pTgftSJhI3kvfa5tFzG9dQC9+GNrdlm2wfXwj+pGyAHJ99fTOhHqFNN6K9OXE1+vfwLcEyHZMyXz3fJ64W+JB/c+awU/QP9bjXw1/N5yYfsaY7dy9IaXvzSPvCi7ApwHw5X2aebT5dTrx6SScxPc0wS4s/7+3jONbXQYe8dL0eHyeexeE7L5OrUEakzwZOnt8S/bmuPvx3SCz5L5avMo4MmOTzbHGXBp3bPhsd1nn6Y+vF69CF5mlLcj3lfQ3CZt/XhW913E783mnjx3ktVJe/fFz9Iz9xNTh7jdG7qLyp58/6TDmS/fd+c/THLAd7HzM9S4g7tZi7uu8dI8rmNovD7zn3i/K+PybH/to+6xsCP2noAPDnpQHgLS+BPKC5VhF20fGJczEk/C3/SXNVM/uXYEbGfmT/5xYrvL/MKfqByaAn+p3Pdn/fF+MuPdod+6MbHifQhblICfkOd11xv7df+Uq7brzzreSU6AfrtOPyvqOUi32Ipkwm/aUs28mzl+ol93eQ0KFz8fkK8XPzQMaz1j/OZcw8H/21EnxDzrmvgU8NibM+fBJz/2Br2u3j8WNVu4B+tC/3xPHgMrjYdoBJ5wA1THGF/G4SuuGX2bfa9t1/xLya2lGKPFU3r/OM8xtNyxEeF/MLFdy0PdSHJooW/pS16hW79iOIC19Hn23jhkj7a9Pb0OV08BL5N5Bb0V/J64r82eyKnvi9Zg8Af5zH5f8ZPbp2JvnXDwCPVsaOpL7wyRY7/0OEqfC6vaHDCSvB21N3TwAG2jv+p3dEaNxJ4qap60W9yadpw8f9v9ot4U/FqK9aDXjq3sBumgMHs4145xP9pBf3h8449L4UnbGocwPsYQL96/VEa8i9dQsGpN85IXJ88dhn5yzIG8zV+sRQdRaV7nIhT1bTHyG+XqQDvKKPKfWzYLEdPLb0q9P/MO0ZRr73/rbCDRvA17KKamfxBsU+/5ZdpRweTz5xxlLjJoZBUfXt9FXpsimU4+N5ddGf0XAWl7Gt6um+sPyebTs7M5FwvsoUcHahvVcnDt94Z/uNoTKtOfVr0XHQpV4Qx3yeRl1fSnkmk8/anH9OI6fAen/ckXtp1iuufykifnKjM8P6We/2VHqQ26zzvz0PhvUU3kTP+18ehJ3f+An2vV8xF/yb5afZt6zfyf1c7oQ8yJj/3cbkc8UqTx3J4A4M0+BNx4/ETbxcGN+6yBf2R/XnRIylQ3da/MUCOPbB91ILEobr/HfCkEHRRlHnFE/EQtHfkQzTTEfCAZPBqlRU2vRZHVylxserRS+Akar2i+AcrqAtRH5GnN3ukwX7sGIydPvJcip1W7u/A7tqXY/3X3MN4tEb/WM2eCrwmwxg5dU5RGYgzSk0mrhpTlXrBLcHwpo6uQcdkZi7yohfWe8m4rjbI1vfPmon3aBcPvv4hr5y6mB3153C+YPCb/NUZx8pTWD+rTop6YrXqO/hmh++J+W2ee0HYDVMr1/AfR630ffLdRyfir/qskvL8ajsrvKkOvHcjpBb5Zdek8Dbbr4C/cKwHcVSdPuAyriNYd6sCWR8L5uCfVEfPROseKOyhKXVJ9AIu2voHzar8czsR/b2j+H3XNPR7a4COhvEqQfhT5o5b6Ct2EV1q5ckTOboSqcbDIw1rTz3U0nzUt5TPy/OH0T9Vq/X8lzwkLecinisAHWbjPfqdpl6XeW/9qmMf5rTifSzaQ31E2GE5/XCGWnj/PlXh9bpORLexQSn6u+c2wA+8s8nh7QyNYb/Ouwr+bcMOVnH+sj7Ewxufysm7FLbxlkqXsdmfnOSHe56Cf3hktI2/nE1K3a625wvzavMl7H/rStj5y5cS571fLyf/0KEV62DePTl4S7Nzwg5qi9fBX/zoAV/o5F1wwjLJ0H8Z3Bz8oWJGKfo4StO+8IqiX4KnlM7Muv82hfoGl8HCfzG5w29VOt6RY/d7fYWPVXId8zd0Meum1GT4ADVXo6NxcsZP8Rv19HPGvbZNXyBsGPHE7fdy+Cbzj3YT51k4gPmwIbUN/y8AzljjNnmdhdvIf6/cip6eFX0O7eNl7MecOnJwxyEPhK6iefQp9A3aUoer1vOjHv95C+o7hkRw/W1b5OTblKLoFCdzg99f2AscqtgtYW8sxb5bxX20ySSnf2NmB/qnZ4J/r4VfZL77nWcf3fw9cZ3bl6TwAdwbwW/M6mHrC00+QZ38FR5SCvZ7PVN64o8jXeTkm55EUPc3cxr2YmEN/CY9Uk5cc6+RsEvG9VLgXNft4PO6pATfLr0L/LPVdTn17/HNBI/Rko76FWXLAOZ127mijsj8JYY8doZBgpenOJwFb53diPe7aJ8UvXRtzChhV7UbF6j/3JSSeXDfn3x6qrvEqSEfrOJ7kov4c8vzCL/GdL0s/uLKNuHi/7WVAvewOM8UPBvLmcIiL655PaK+yVKH85Q+KqWfiLqLfmyq6xD6dfUNh++7KwT+wNOtUvoGqQ83UB9x7bRY90bttMIemIMzoMtgtxudjkMOxE9+flLw8P+6/sQW4G4+mtg/jJa7wDvOUXdjOUTfMH31ZTn7dKW9vNcgPzl1zpcCqEsbU4b7Dt8FjnrHQryjFiBvN3EUfS7M+eHnRVUQ/TiNm2O+ir97UMxO8DUcC4q8vpHqs8jfWw7VvS3+/2Jv4prUleTwoO5MRgfjXUry9M3SUr90blLieTWtMPtVl0Ni/hs1Hwq+o77MR/AQlNPhQndRjRsg+nLp1UIFz05PVkDgJcZV5qkes1OODufVCOpGZ5D311Jcx25O/yAHR/Agv6TbhbBfLGzCdZ7uYj8pt/7P4rtR70TfctNI3SrOs/GuwHssD7MJno2RO7/AmYzXHgLvVvbNpv/Z62h4OEuugwMdMfr80fX/83OdfcFco4i4H0vkVepOVk6Bn+zykLrhKkeJn1xXgBP4x/1VXcU/H3U19fjmubWpf7LX8A+Sgn+rZRfIidP6t4JHdQE/QG96DTv/wqnnj6Ph60y/pYPB2LnbFeFRBrUg7+NRnvoj/8/h4r5ShpIfCLDFKU3HMS8aj5PT9/TtJOzTqcfsVylnMf8m/x3vUK3SD3t3rCXzukroL/d7XX/BPlygL3Hz/knYiWrJ4LWvuAeutjatsGvGw6Th4rvLUfzKbfDclac55fivCzrSX6DTBOK2s+gaq9dCxfVMwafh5wyahT+79Q26HFMdpPAi/vmo05PAJ7lWiPh12lns8nb6zGm1igneqtLRHf7qvifiPZljHpL/js4h3rtmLAcns9xnXbVeSJxQ7CZ17+tKUP/daQP2PnSAVTxnB/wWpTq64XqaoXJ0p55uBm9yHECcfK8+OMbCIoy33Vgp+V795lJ0VFcMJg7oVYj4tJk9OmHD6Uf6x59aLajLfR/MPvwilr4Kr9uCY6zZJ6Vu2exaH555rxv4zwWSko90cIBP12ASOFRQW+qjdtHvyqzOQceobzD16xsKYHcHOCSKi9UY8rrGgU62+uR4/KHkLXlPSX2t4vnOd6ae9fRA8kLmBI4RxYjzwpvj79f0Rt85w0LyDCGTyPM+cCFOXlpfjv2qFyzqMtWj3L/mcpp5E+iOfS8SDF+s0RDizFRFbfX38IyUmPyi7lcz7PAHj/jgP1jJX6qlxoPrtlobLs6X+4aIJyxKU+ohN/jBP6tuBx73qS76bevvoJewaDn26nFJ4t3C9PdRZ7ci/pj1ELyilC78F/XmF+F/Gd1Tx4rr1Ckh+n1Zam4R+4Oie9OH6rUtX50hnchT60n6iKN2YZv4udbYUfy9ZWNz/u/ZVfypiFLkT6PbcP8Ng9ElCGjJ/c/tRXyx4zJ9NMoY5OVv6eArhfKi4/kxgrzUkzf0y5q+IjE/YedB9qlJF/DnmsZx3jQtsFP6SN7D7YXU5TgloW7dx1ZPNOEI/9f2MvycHcHgnj2Gsl7z5U2U99Q7BmDvFt9hPHZUp//mMB/ee/XLjL/WEz7YQ+a1rjyhXurpK+qWLu0R69gSkpH6vo7d4THlnhwu/m+thfNlj4Q/FPOG52jRnHUS5Mb8infhve5fyv0kMVgPkVW5zudo6qW/zBC4rtm3NP3a5g+D73w0K/2IathTVzY1P3axFfGrVvY5dRZ3mrKu5zXleXrFYJ+/5hN131q7FPD5KodSd1BmBON83Af+5Dx76sQ6o7egrV+PvWo8XuTVzbEu2Iui8IW1KIPx71cSvPZDMngabpvAKULQYdMLXqa+YWYE9S5uu9Fd+vDeVjdDvxCl433qtGc6cZ5tY5hnJgV9ixcZwIf7JJDXe9Pmp3U92n4NP7rueNbVvUs/9c/Vl8Hw4+/4oscYS78IY35r8AMfeBnKkJ0iL2ZY6jHvDrZFJ7K4Rlx1h7ys7oSugTqoE7jTi9fwW653toqj3wXsZYw7fm26deLnygD738I/9PZD8IsGTMCPavsO3HB/R/gAFTJj9zaB45gfsA6N4y7wsEaPZ12kd6fO7WRa+mW2HkLcdcCGI3ycgt9n1Gf+NMzIeu7SEXtezJl4/asq3peeK/kf7WumPGPEuJhShMM/WVmN8R7lxDzR8uBnVcu09k/O/58fo0YndDBqVmacb1dlvzpShPXy0jmRzoa6qzB4zJqVzAf7Yayz/QE2fqaHWKcW6yYp/ola4SL9GZ9tx79znwX/sHp18LMhl3jPqXKAC4628SlaVJeD19Xuh1+46grz+GFDYZ9NCeAHqvtpdFLHTvk7P+k/PpppLv2CvjcGdw6sCB+r6zLisVTv/xVur91Izrp7hv6R+rkO41b2E/bx9nT4rpNPSokntQpZicsWUReoX41gXU7ew/jcziO1bkNtB/9d98KuGnXR9TVV3if2fdPeoQInMZKNE/WH5q3Ek1plL+xUiyApeKspwYxeQstl4E0jt/AefI/j390eRX1g7tUCX1BdHMX9qZ2Ia7WqXcBhfVrLqc+wsn6MHCfJB/dqib9iXom97jURPaSvldC7SRclJ6+YslQDcZ5LC21xczXsZOHhXL/jWuzFkU7kU1uVlsMjTjKRuus0A60/jmrdxsxzOwfhXygn+4rrmyzZiBNPxlDnXHr3H8Vt+gl/9BuvjCP+e+wFvu0+Rk5/wFyzwGWK3hD21Fh/Frvs9s4qrlt7m5x+pju6ivlm1H6AfQ11JV+/FjxKvZpUSlyr9vAh32o3m/zy0+Ssk06LfjrftQTsoPpkPuvbe14i3EUrVhK9xevXeO99AxK9d8usFtRPNFqGHQrxJm7Z8gHey5KbUnAt3cVWHzg5B/YtcwGOjR7KwUEbWMS+a5wuSh/U177M6xn1iL/K2PTc3twnXzW8vZx82KbFPJd7PXAwl+zwenbVYz52zkXeZmhH4sUlQYNlXFcbTp8C5bk/cb3rReL0JIfxs5bcxe+2u4N/si+/lP7M+ulu8PTn5CJP0O8heMIOB/yd1FWl4Ffa3FHUSU15BH/2ykd4veXs8We8xjPfG88KlHE9tXVV5k9Mc3TzEvDbzf7FwFtn7qH+rZc797NlqJz6tPJb8Tvq3cbvzryTeqnKJnCHg4uk1MH/89Fu0R/GkscKb3/REXj76bqxz7j9vA+9fvxa4I+fa33iwf+612E+N9wuxT6oN13QSbgxDLvQL1AOvlN8ksh/mM+Uhqde5DXx2F47cJMA9MeMU5Ot4ufDrkjxi9Xq98R5jOqHmTdFb4JjnW1Hf/XQLAKHMRqtIz8bkknkL7VCBbBXe6eCr05/Gijjfv75GPE3WT9LMqDL2XM+9VXNa1Anc3UX388/p76kfv+f1nv/6Ufru4b3sqKe9cfR4tMM3tjABdhlTzn8Ii2YfJFevihxoPMg8LQG4G6aKRyd0MxR4DBVTcEyrvufHz2qMDpZlkzY4/oqOPXOxvDXYloSvxQs8Fv9Vv6vjxpQVuAbllbfiYdiRoHbzMkJjtBtkBz+VnH4jZZ3hazifEN7EXePWUlewQOdcfUafW2UwHdS+ct6ZvTF9dEm4tpq8xLxw7VzxdHbOzUWvpUFXXlLlafY85q2/mK9bH0pLn6SUqel1QEX157v4L7eog+sXd8up06ycVnsYv/0+OmF34t1q9W5I+I1w7RH1MNpee9RB6CNE7wPdW1/+FuD8euUkBNSeB+6x2Dy8ivpC2p+GcN8S2YlLzujN3js5Qa/5gFoi+CjF6LPj/K6CHaq20U5vH+X7vhpCQHU554fSB53KPXPWpnz9PvuWE/YYXViToF3qwPTinhUKzkBneBOo6XWEap5o7CHc66gaxpWF95KrT7gvcsKUedZu19ivcT08IXUvHn4eZ/rcnTdrqcX8YG6Mw/jMHYB/tCgGvB5C0aL8TY3yY99WTWdPHQOL/oSn94g6i4tlzaJPgxqbAg6meUN9hvPQXL45u92s09/bQCuNH4l+Emh3HLy8P9cJ/gl89b+JrpzDc+D3z58y7rLk4E6m4w36QMT3pt+teHHf6tP7e9+dOes8Ei2TwZnTfaJ536LDqg68g16sQf7ES+69oEn6mTivbTtSv3etNz46UfmgNf1bYhdKL+DPENQQ/AOeyf25by1/yhu0B+Z6D/r1Jj7PTqI+1qUG/+x8zIpepT6+gXg9pfvU89zIz99bLouY75Ob0Be0/0WeHShclbx8x2z5fh5jdB5UnvWgz/woSs49PEN7HtDbPxtvzZy+pONbA8OaSpGfDsri8DNjK7o5KvDN0jBWY3tA4lHVkRSzxveBNx773Vhp4zN2YhTbvZCl3FPnJT+Y6ZxoVz3Zi/8cu0U+to3iE/UWXnlvMehacClAoayTzu8B4c7GE5dzNn1cuyI3RR0phr4gA/UeMJ6aBEkB3/fUBH71GUCeZy+wXLej9MZ8gr5yNdbAk6Qn690knlXcxF45ZNa+Bl1PAWfTrnc/LP4vVpG9MfWz8aLvsdK/f6CH2hqkFPkp9XlMfz/iktS6k+VdKXZ3zNeFPbOPKgeeaswg3UYe5R1Yt+SPGnXguTZN8dznDZP2HPVWIPu3qGhQmfKdMohXvy8y3yhS6g/zCGe0zSqk9jftCRx9Bc4v6+dlOfYUpr3cWAx9nz1A3gIWWuR3/R2JA/o7ItOxev7gVKua/voRReC+1VpHP7jqHVLL3h9yoIvQl9BCdSEvqQe9ClWHI+fpH/ydvgNSu4xzO/FHaXUm6odvMA363+18SJeiesY2XeRv6hQi3hVOU/e26krdj7SO1DG9fUDBcj3ng3Fb3RBJ8rY+4n8Wo+K2I9M6aTUp+hbp6Gz/iISXO8GfD/1xVb8wC7k2dWEFOzPz76zn77dKwcv+ec+hvakr/LCceBgoTM5vosl73u0jhR+rOXENPx8N/wS88RX7DMbNwh7Y0m4Lke3InIr/BzPjtixfg2JVx+GkQ9z9An6N9cxb9huFferJaF+5zj3a456RP6haW85/aGWlxE6p+qwFuTdW7WEX+VbEzvg0YTxiGiUCI/Ub6HfqacMYp7mxF/X+g8nHl2T9u/ySZni4N1MHmoV95NsMvzbuLS2fiCDeQ/VvsvRnzxDn3TL2ZfgeGl4ftM2E/7Cl+L4wVOW/ZF+ljalHP51NfqXqP71qUtr8ZH8/6Ap8FLqRJI/cXCnXvnEd/ax0Zfwn52ToK/VLRBekP8teCeOUxKNj6YPoo/6khaMY14zvJYUOaziPJ+YPybzavHduNKG70/qJMIR1Lpz4V+aQvADjtuBz3/JCp9DqQ3usXYHeuzH8ovz6V3e/nRctBGt4a/EV6H+dd597meZlfdbdTf4QZG9ifAq7UkF8d7MgV2pp+9ak3XmC09Ga3wCf19zZl5uHIy/PKYjuOjO3eBwqWw82CkpsHOdG4j70WJHUncxcQb/N7AfPKJIR/F/ytUu1E1XhwegpBv3R7rP+gv8KfOgPMxnvyvE4SUakwcvnVZc1zz7wpb/fn5j2nP8/Hutwb/SzOW+UtUAB+sXgB9SuC1x19bX6EpZzqBXc8AHvuehN1L6pFo6tcB+XriOrkHlKvjrNQ7Cm+lWWY5dveOKXVm8gv2pjjM8rRpz4PlMvZsob6kXfCHssMUtL359rxysE9cq+JV5t7Ne5iWTgwPv8wTH2KPT12ptQ3FfFntP4ppJlX5p99WgBOLIJEuZj7mSYd/2JGW9zk8mBdfUKwQzHjVc4eV2pg7VuFie+qn8c+W8j5cT4Zmt9iRem1T2z+pzjmUhj9mzJv5i7wjspEcs+ELNi+w7ns34/fQhUvwFLfVdse9Ylryyiuud2Qtu750DPK9O7b97T7PdxfnN5cgXWYqEwudMv5VxuFCc51r8QI5OzJ466KafS0X8NgpcUn2Ugfzq8rLwsPPb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eJwtWXlcT1kbPwzZy5YsyZWosS+hSC6RyC5lia6QXahMxnYtoUISrZirrJMtZStyYsaaXd7G1rVkmSHMizCM9/X9+v1zPud3znme5zzL93nOc4UQyqwyLqr4/8/vxKhvoxFp2ROjXjrs22gOq9jw2yjNdw7fRiUwuy3+/7IAo2LVoxf2733UDf/fvhv0bVSrtGiDc9f21sP/+bWjv4161zF+OOea5g2+RyIwqoHh4CfGf+4Eela1+uL/Au+m30btp+FOWE+6NR50B1wEXy15H/fX/nsw/i+J8QSf7M3Yr7rktcC+a90U8D2Q4or9vVy6Q66iqAT8P+/KOPzvoPbDvGgy9CAs7/G+Xf07g/7TraCr/VMAvqJcohv4pU3BPbQuIViXQTvrYl71BujIWT7c7/4ecss1x4fj3MNxuJ8xyR/7ZW9/6Ee/XWMSzj+MgTym/KJiHufbAPcq/xP0rT97RLphn+dg7nYU8mgDZ7fD/K3/CNxnUzrub74ZomE+qgT2EIdkIPZt9OT+tGrgJzP+aoV9uwrJ1ym8JfieKj8R/w8O64r9lyYMBR3rAc5Yr3/IHf/bqLi3vvL4SIzuwWMxflzthX3eQ8FXOMSAnt5gVX/w9W7bBXIeagR9a+3SpmO+cTj8Q30zchrmFd6Bjzp9JejLYU98MH6JWYxznltAX3Vo3Ad8nrn+CD1vzYWf6LHDuL4mMgL0tjiF4nzuZfDR87O78f7Fg6j38QG02zTwlVV2wS/k5ha+OG8xyx/zZhkDce6KG/Su5b5cBj6lvjrkyGwKu5ru/WEHc9wm+LNRY/VC8M38D+4n+uVBf+aSHr1B98gW6N/4+AH+ITcGYRR3Tk/A6H2hB/jssqB8FrO5r+2v0IvuOwz3FFsjpkCutq0gn4yviLn+ZEUIzs/5A3EhKx9tj/GsO+jq91uTflYZ2MU4JmB3Jfkx6Ivm2UOwb52AnErEEA/I3yaBdrkSCpyRb6aBrmhQgvsbaf2wbqzZhnvqIz4wPovDiUsbw8FXeT5lAOZtdMzl7ZOzMG92eSrm+asYv2cv4/7K/q2QU93SEPoWWh3QN2yeQK/qyNhg/F/wEvaTf91pDr0MG434kju30d9W9YfdZNWUMJw/UA1xoYfmwj/ExRTQFak50IeWUwZ+rNndhN8piz0coQdHBfrVr/SCP+jrx8Ee2i/zEBdSpAO3VN85o/F/6wLghGI9APLLxxHrsX4zKxznp+6mH396CL0rOV7Ez+rpTbC/aDb9a1s/xJc63HsM/h9dG3ZRB01uhHNDg4kzTnVhBzMwEnpQ3Z4BT9TeBZDbiBqFODC3VAEuG2Vz4adqgzTyj7KD/2tP28KftdVJS8HPKm829uf3JK6d+cL7bPFZBHknueOcGpHUAfv2Fy/HGJkSiX07xsK+angR/TzxIfKLtK/L8+WOpmB/95/pvwnRkEupvgt6VZdutcX+ead577xb9pCjfsZMjFnxm2gHuRLrVYcjfo3/BMMvxO4H0Le8VGMJ+U4Ff+H3G+0V8jUL59OMzaCX8YBxMicN/I28qRiVBv6wl/GrHfRnfnkN/zV/3DUXY1EX6E25VBwIOpX6AseVRusgj3lhGvxM9T0M/9b8DkMfeudM5pP3R8BXW+iFe4oaHZjHzt3FXL5aAz/U7EPwvzHzZ/ARqcvg17K4JAr0f6gJfes7PRBv5rsF8B/z2GTUB6ZFJeCkkjkLuKHa38Z5veMh6EM2CwKumG8dZ2D+R5PJ4LfhHewikxbAj/XcC+tAZ2Ab2NksWcD85eRGu9UKpB7186ArYnvCvsqnROhN9eh6CnRzJ4GO6l4F8oi8uvTThDjEoeqcsQrn2tdajf/vNbwCOQY2ysO6Y1eD+uoOe+vdBpP/jbJrMBbbHMR5j687ce72TOCG7GmDeNG2LUfcq43fAq/UnGzmG9utwCNpGQM/NducgV+ryiTiVDmdefOgC+yrLnqH+FBdMzlfOpL4fOMacfxxCPSpDHsF/avtm4CfEnqWdkny6Yh9vbNgV1mjM/PK8sqwt265azHttBD20DuPZZ32qTvyq953NUbDSyIuRJkjoKf82XkO6UrGXwv/edifEwJ76i+DQU++TUQ9o66pBD/VkirBbmaVuKU4N2PxCqy/f0p+ReeAC2r6b8jfalZ93E+tuBV5UDt2hHnFJhP1gv5ve8ShGOWGOJU5nvAbpdUfoGOKq5QjtRPjNv409SfXQs/m9i+wm5odRVw58QRxL4ZHwy+MlS6U50hv4mTrd6yD/FT4m94ykfnnWSjzweSM+ZjbJROHYkdAT8buAMbpIFuMZmcDuCwnvkDciz/zoScjKpw4+PuzTIxFf2/BvUsscU/z4jHmpfv9ga9q7XzIZWb9A/8WzZYFQi67Ooh/0dOT+cm9EPLIB92B/+JmGvP/K2/YT1kWj3Nyu8C6DPrK/G5zF/lIDfdkfOWeAC7rX44ifvSAdPivmVwOetS1SYy3FT9yf0ZX4mKdW9C3jJoDOWXMceCi2OWXiHPpWfBfEXqX/KdtgN1Mj2LmtxMjfoKcp32Yn5PHpeLctnaQz5haifrTDjLfJbnCzka7yzgnrW9jv1DcUNebdsfp550pnzFlKOJFDdyCONOHREMvcm697RiVNxjVWwdZH3te5317+wEXlXrPIbfmcJdxVXc+cN0MqMj6tmmtDPCxTtuK/VWsoB+j7DzYR616B34m+v0O/Zq+veH3epNNxJ+dKZDHuDuKcRi/DPFvfj4IOypX8xFH5sObsLM6vQvyh9jkzvrVM4p5arMBXNOs7idj/Ksz5JA5ebC38PIn/eztrMO8XjNvvx0Ef9Tu9mJ82GZCD2ZpOveV9mX+7fon7GXcc6AcIQegJ6PNzycxd0yR2Nd4L+UprY1Rj1kBfNQ/Td+D8cdFoCMCeoG/GL0M8a1nH4e/6fMsiTO7Iom3DT+Avva3xV7I1aol/ErmbQDeGCv7B4JOQgrl3eFJf+tUH3Q0m0FPocfqr4D/st0PrB9nLF+A8ZQP88m6dUuxL+V35rGVkvXR+AvwPzPoCv0q1wNzGZzNd8ipCZS7QjLsJFuXsP6y7j+Z9rRivqxQDXisBBTFQM5FDSCHmTgcuKKErP0Zcx8n+me9OOaFe6t4z0btEJfG0zb0130rkJ/EmdE4b94dQvkqNoDelS5OiB/9ud13Oc4gPvRVwbivuf3kNugnrWIS6K5uDpxTvoYBj4wne5EHjX2P0jF201g3hDgi3g3vGnyPLKwJnBJd7Olv3suvY665nse8q0KcdHZEP0G8zLEH3xZ38P5VbxbynfZrD9ah5xbx/X50SGOM/taob7XJf/CdOTdco71c+G76sxC4qzispP2rxRNX0t/DnsatpcTzam/wzlG7J/Hdea5Da+o3gnWivT3wS/8tnPV9UTbykHm6Eda1oGl4pypzyiCO1MIdwCMlzUR+N50/ME/YpDHO4hLhp+aAz6yzbsxnHq5fQv6TZxKHXycCB4yPx1k/bNgeiPP/LQs7aIsPU8+PmkI+1dqFdd9jT+QB80w35ge7Rz0x/3cE8rjpdRrve6XMGOhXOnjjvuKaNd5j6vwY4JgSZQE9KQW7MYp/809gfiL+APY59V0LOUs2sC688pH6Dl/APsbCz6xP70/FO05f6wj7KYuCMFcjrqOuNesuR19B1umDvChDd8J+xr7dqA/EjjU7MB8TBxwy3lSCPHpjF9YDU5fg/SXtuqHPpKYEs//i8RTvT6FbsV/k9ZH1c1BUfexLfc/3443/oh5WX9xif+vfAdiv58xBvjWPjUE/QziNhZz6vhd474uhj1EHqJcq4L0ibr3GfcXjOtinVkhBP0YqF6AP6dyS7zjLM+Q/cSr8RlgkgI6xf6sD9VqfflDGoB+37cH65RztKI2Z3/sNvZvh3GD2o4xWPpBTWkQiDnWH66wXXW/gvCjpDbmMRa7wJ83lFvPJ3OV8J8SwDpVxS1hXWtREXaLcacJ154kcC8qj36PY/EY/XbWYdZXfO9axcY1YB9/XkddEUiTHWhnAL7OqM+gazYaibjAj75DfzEL2H+abrJuHueO++qh8yK+XnmedUEFAbi04GX4q+rswb7aqvIL6OoC4km47mY9ywvieepnBOiGqLN/ZVgXQhzLWmv5veR/rxqMHyMuK/XXkH6PKINDRrdrBH2X3x9TfnmO7sd64BPGspArkDdH3PPtc7TbS/4enEl8mraAdDpyknoNiiBNrfmEfZGQr+u+GCOCi2bB8LNb3vUacqQEjIJdu4wI66qegpdh3oQ7zyMVw+Jd6ciNxMlqxB90Tdfjeu9GC75JlB6FXLUBjvahkwd+NzbbwS+Hykva6NpjvxsvngLMi05Z9vc2tESf6ght85zn5wv9Mcwj0KINfs5/aej37pWtdWWcU2SMe1Bm27Kst7kDcLv8JcaDbbqMewpbjvAixgJxqpwjGb69NoG8c7sz4q3ca9zHjr6KfqWXvxKgf6oA4MhPvsY/kOhp9ZzXOku+gnfGsbyYPYP5sc5VxPfgF3ystU4j7DqHASbFwPOJRdHfjPY4+Zz08Yxr5145mvKXuZ1z4PWXdX/ABdagYEsb32sks1plDJXE2ZwvubeyOYT/q8GjiaGwq64q3cahPtbCyjFvvgZTz/QP2zS8kQT9K0Dj220qf4ZwYHw4+okkg65jn7Vh3pXyg/767w3dRbDLlmcB+iFZ5GPuVzc8Rv74+Yxx/dMQ5dfgr9KPkUcl+++DxfOfdyuI73whFHGu9djCevT0Q53qnQtS/2qHKqDPkT3XZ/wzx4r2u+H9/ny/jO7NGY/ZFjHfsU2ZOxn5j2vf35TWJUZvbnPbsNxV1qbRfjX168Hbm2X+CWI+kVwFdMXQe8oE+L5R957vPWX86LEOdo8TeRLyZ/lUhvx59G/uEYx/mAecD7Ns8HcL666st7K09cWFfttQW9lA7fobfaIEO7L8G8zuKOer8L1g/5B2P9dPl0f8zDz8jvrnaIR+Kq2uYJ1dYs/+4oD3iSxm1CvGnOJbjO7BcTfrZiqHIP1rYZ+JmfArz/KUo+JlxsQzmavBu9Pnk6wOwn17YkPK9a8A8WKeSPca9h+xw3vcq8/aXQewv+K7nd5pjVshXWvlG/O5z/x75lntpgzH2+/ca1+Ps+3boAjpmi0HQn1r7Ntb1+f70twq7iC/Oq1kH5J5i3VLUB3NxkzhleFRmvM2OgB700grMm3nd2Ic90/B7nbgR/OSNsZBX/30p7fe+GeuESadwXzPEjXVb6BTgtbklGfo2P22EPs3Lz3nerw1x282a/l41gfOU5j2hh3kBjNOnw+Hv6slQxJfS8TDXs0tYF5lNiV/Fz6Ff/bokHnY7gTyuz9zMfvWFeXwvHx5LnGrriXvKbbnYp8yIYR9tnQ/ykL6hP+RU/L7wfk+OwB5y0CnoX//gjnpaf7CE36d87ICv+mV+dxJnmrPP3nM/+wyjQtnv/mEV9X53IvWxxIt9mKaTYD/taAXQN5r2RX2j/PWB+aRaEe4tVs5AHBixYfxut9QWdOTCJsBjMeAA/Fw/uJ73SnckzgfOxlxbnspRW2SP/y2KoS99lhXuIy5W53eEpmdxL9Uygfx7DAJ/o2ZL1lMJ7HfKvpmsl0JvM54Of8I+ObMh9KCdXQhcN2YX0z4tXjA/9M5FvlP9mnSiPIXAKyPSk3oue4l1iLcN80Ofp+zfXSlgftyXwXNWAcS5ZrXAV5nfgnJEH4J8Ysoivot9TX7/2S3ZH1gTx/zgVJV09txkneyr8v+CFHyPUnvcp1834vcV5X0L8FFT57MvVviQ30nKRfPdPuUa+yfX/uH30IzbuI/pMh36Vqp/4D0rxjDuzyZin8xi/SyvWAJP5awGmGtmfdhFSbvHOiK/Gt4Xwr0f64BmCahvzIvpiGtzSDV+55RfGXeWG+AnomAi4lkdXY3fGZeEMQ9HfGR+fTGa+9qXhT+pE+6wPu94lHgRHYB1Pf4I3+Nl91D+yMHUv18x8fH8bMilj3oD/FGt+a7XxvoSX92es45aVB/vTdVYwDj0K4Q8pk0p642RPnxH7Z9D/41cQn++v4ffMf6Zze/Eh8thv2i5gPsmvOU9k8OItyuPs24qvuys/g/MFOSM 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+ 233154 + 429100 + 439805 + 455821 + 470132 + 482555 + 496163 + 671524 + 875747 + 887124 + 902560 + 914797 + 928658 + 941290 + 1141379 + 1337946 + 1349662 + 1364824 + 1377430 + 1390139 + 1405045 + 1607519 + 1782471 + 1794141 + 1809505 + 1823333 + 1836105 + 1850716 + 2087624 + 2279302 + 2294661 + 2307108 + 2320052 + 2331812 + 2524850 + 2702895 + 2714527 + 2731218 + 2743630 + 2758367 + 2771876 + 3150670 + 3348908 + 3360939 + 3377075 + 3390135 + 3402665 + + + 3415067 + + + 3417691 + 1da0979b1b0e5c830729b2761eb4a210eab8e138 + diff --git a/tests/data/protein/yeast_UPS_mini.fasta b/tests/data/protein/yeast_UPS_mini.fasta new file mode 100644 index 00000000..ac3aca85 --- /dev/null +++ b/tests/data/protein/yeast_UPS_mini.fasta @@ -0,0 +1,70 @@ +>sp|P02768ups|ALBU_HUMAN_UPS Serum albumin (Chain 26-609) - Homo sapiens (Human) +AHKSEVAHRFKDLGEENFKALVLIAFAQYLQQCPFEDHVKLVNEVTEFAKTCVADESAEN +CDKSLHTLFGDKLCTVATLRETYGEMADCCAKQEPERNECFLQHKDDNPNLPRLVRPEVD +VMCTAFHDNEETFLKKYLYEIARRHPYFYAPELLFFAKRYKAAFTECCQAADKAACLLPK +LDELRDEGKASSAKQRLKCASLQKFGERAFKAWAVARLSQRFPKAEFAEVSKLVTDLTKV +HTECCHGDLLECADDRADLAKYICENQDSISSKLKECCEKPLLEKSHCIAEVENDEMPAD +LPSLAADFVESKDVCKNYAEAKDVFLGMFLYEYARRHPDYSVVLLLRLAKTYETTLEKCC +AAADPHECYAKVFDEFKPLVEEPQNLIKQNCELFEQLGEYKFQNALLVRYTKKVPQVSTP +TLVEVSRNLGKVGSKCCKHPEAKRMPCAEDYLSVVLNQLCVLHEKTPVSDRVTKCCTESL +VNRRPCFSALEVDETYVPKEFNAETFTFHADICTLSEKERQIKKQTALVELVKHKPKATK +EQLKAVMDDFAAFVEKCCKADDKETCFAEEGKKLVAASQAALGL +>sp|Q15843ups|NEDD8_HUMAN_UPS NEDD8 (Chain 1-81) - Homo sapiens (Human) +MLIKVKTLTGKEIEIDIEPTDKVERIKERVEEKEGIPPQQQRLIYSGKQMNDEKTAADYK +ILGGSVLHLVLALRGGGGLRQ +>sp|P01112ups|RASH_HUMAN_UPS GTPase HRas (Chain 1-189) - Homo sapiens (Human) +MTEYKLVVVGAGGVGKSALTIQLIQNHFVDEYDPTIEDSYRKQVVIDGETCLLDILDTAG +QEEYSAMRDQYMRTGEGFLCVFAINNTKSFEDIHQYREQIKRVKDSDDVPMVLVGNKCDL +AARTVESRQAQDLARSYGIPYIETSAKTRQGVEDAFYTLVREIRQHKLRKLNPPDESGPG +CMSCKCVLS +>sp|P04040ups|CATA_HUMAN_UPS Catalase (Chain 2-527) - Homo sapiens (Human) +ADSRDPASDQMQHWKEQRAAQKADVLTTGAGNPVGDKLNVITVGPRGPLLVQDVVFTDEM +AHFDRERIPERVVHAKGAGAFGYFEVTHDITKYSKAKVFEHIGKKTPIAVRFSTVAGESG +SADTVRDPRGFAVKFYTEDGNWDLVGNNTPIFFIRDPILFPSFIHSQKRNPQTHLKDPDM +VWDFWSLRPESLHQVSFLFSDRGIPDGHRHMNGYGSHTFKLVNANGEAVYCKFHYKTDQG +IKNLSVEDAARLSQEDPDYGIRDLFNAIATGKYPSWTFYIQVMTFNQAETFPFNPFDLTK +VWPHKDYPLIPVGKLVLNRNPVNYFAEVEQIAFDPSNMPPGIEASPDKMLQGRLFAYPDT +HRHRLGPNYLHIPVNCPYRARVANYQRDGPMCMQDNQGGAPNYYPNSFGAPEQQPSALEH +SIQYSGEVRRFNTANDDNVTQVRAFYVNVLNEEQRKRLCENIAGHLKDAQIFIQKKAVKN +FTEVHPDYGSHIQALLDKYNAEKPKNAIHTFVQSGSHLAAREKANL +>sp|P06396ups|GELS_HUMAN_UPS Gelsolin (Chain 28-782) - Homo sapiens (Human) +ATASRGASQAGAPQGRVPEARPNSMVVEHPEFLKAGKEPGLQIWRVEKFDLVPVPTNLYG +DFFTGDAYVILKTVQLRNGNLQYDLHYWLGNECSQDESGAAAIFTVQLDDYLNGRAVQHR +EVQGFESATFLGYFKSGLKYKKGGVASGFKHVVPNEVVVQRLFQVKGRRVVRATEVPVSW +ESFNNGDCFILDLGNNIHQWCGSNSNRYERLKATQVSKGIRDNERSGRARVHVSEEGTEP +EAMLQVLGPKPALPAGTEDTAKEDAANRKLAKLYKVSNGAGTMSVSLVADENPFAQGALK +SEDCFILDHGKDGKIFVWKGKQANTEERKAALKTASDFITKMDYPKQTQVSVLPEGGETP +LFKQFFKNWRDPDQTDGLGLSYLSSHIANVERVPFDAATLHTSTAMAAQHGMDDDGTGQK +QIWRIEGSNKVPVDPATYGQFYGGDSYIILYNYRHGGRQGQIIYNWQGAQSTQDEVAASA +ILTAQLDEELGGTPVQSRVVQGKEPAHLMSLFGGKPMIIYKGGTSREGGQTAPASTRLFQ +VRANSAGATRAVEVLPKAGALNSNDAFVLKTPSAAYLWVGTGASEAEKTGAQELLRVLRA +QPVQVAEGSEPDGFWEALGGKAAYRTSPRLKDKKMDAHPPRLFACSNKIGRFVIEEVPGE +LMQEDLATDDVMLLDTWDQVFVWVGKDSQEEEKTEALTSAKRYIETDPANRDRRTPITVV +KQGFEPPSFVGWFLGWDDDYWSVDPLDRAMAELAA +>sp|P01375ups|TNFA_HUMAN_UPS Tumor necrosis factor, soluble form (Chain 77-233) - Homo sapiens (Human) +VRSSSRTPSDKPVAHVVANPQAEGQLQWLNRRANALLANGVELRDNQLVVPSEGLYLIYS +QVLFKGQGCPSTHVLLTHTISRIAVSYQTKVNLLSAIKSPCQRETPEGAEAKPWYEPIYL +GGVFQLEKGDRLSAEINRPDYLDFAESGQVYFGIIAL +>sp|P15559ups|NQO1_HUMAN_UPS NAD(P)H dehydrogenase [quinone] 1 (Chain 2-274) - Homo sapiens (Human) +VGRRALIVLAHSERTSFNYAMKEAAAAALKKKGWEVVESDLYAMNFNPIISRKDITGKLK +DPANFQYPAESVLAYKEGHLSPDIVAEQKKLEAADLVIFQFPLQWFGVPAILKGWFERVF +IGEFAYTYAAMYDKGPFRSKKAVLSITTGGSGSMYSLQGIHGDMNVILWPIQSGILHFCG +FQVLEPQLTYSIGHTPADARIQILEGWKKRLENIWDETPLYFAPSSLFDLNFQAGFLMKK +EVQDEEKNKKFGLSVGHHLGKSIPTDNQIKARK +>sp|Q06830ups|PRDX1_HUMAN_UPS Peroxiredoxin 1 (Chain 2-199) - Homo sapiens (Human) +SSGNAKIGHPAPNFKATAVMPDGQFKDISLSDYKGKYVVFFFYPLDFTFVCPTEIIAFSD +RAEEFKKLNCQVIGASVDSHFCHLAWVNTPKKQGGLGPMNIPLVSDPKRTIAQDYGVLKA +DEGISFRGLFIIDDKGILRQITVNDLPVGRSVDETLRLVQAFQFTDKHGEVCPAGWKPGS +DTIKPDVQKSKEYFSKQK +>sp|P00167ups|CYB5_HUMAN_UPS Cytochrome b5 (Chain 1-134, N-terminal His tag) - Homo sapiens (Human) +HHHHHHMAEQSDEAVKYYTLEEIQKHNHSKSTWLILHHKVYDLTKFLEEHPGGEEVLREQ +AGGDATENFEDVGHSTDAREMSKTFIIGELHPDDRPKLNKPPETLITTIDSSSSWWTNWV +IPAISAVAVALMYRLYMAED +>sp|P06732ups|KCRM_HUMAN_UPS Creatine kinase M-type (Chain 1-381) - Homo sapiens (Human) +MPFGNTHNKFKLNYKPEEEYPDLSKHNNHMAKVLTLELYKKLRDKETPSGFTVDDVIQTG +VDNPGHPFIMTVGCVAGDEESYEVFKELFDPIISDRHGGYKPTDKHKTDLNHENLKGGDD +LDPNYVLSSRVRTGRSIKGYTLPPHCSRGERRAVEKLSVEALNSLTGEFKGKYYPLKSMT +EKEQQQLIDDHFLFDKPVSPLLLASGMARDWPDARGIWHNDNKSFLVWVNEEDHLRVISM +EKGGNMKEVFRRFCVGLQKIEEIFKKAGHPFMWNQHLGYVLTCPSNLGTGLRGGVHVKLA +HLSKHPKFEEILTRLRLQKRGTGGVDTAAVGSVFDVSNADRLGSSEVEQVQLVVDGVKLM +VEMEKKLEKGQSIDDMIPAQK diff --git a/tests/dna_integration_test.rs b/tests/dna_integration_test.rs new file mode 100644 index 00000000..406e7397 --- /dev/null +++ b/tests/dna_integration_test.rs @@ -0,0 +1,1373 @@ +//! Parity tests for the DNA pipeline against the committed reference outputs. +//! +//! The fixtures under `tests/expected/dna/` are the output of the upstream +//! tools themselves, at the versions pinned in `VERSIONS.txt`. A failure here +//! is a defect in RustQC, not a reason to regenerate the fixture. +//! +//! Compressed outputs are compared on their decompressed bytes. Two bgzf +//! writers at the same compression level need not emit identical compressed +//! bytes, so comparing the `.gz` files directly would test the compressor +//! rather than this code. + +use std::collections::BTreeMap; +use std::io::Read; +use std::path::{Path, PathBuf}; + +use rust_htslib::bam::Read as BamRead; +use rust_htslib::{bam, bgzf}; + +use rustqc::dna::depth::{DepthAccum, MOSDEPTH_DEFAULT_EXCLUDE}; +use rustqc::dna::gc_bias::{self, GcBiasAccum}; +use rustqc::dna::hs_metrics::{self, HsAccum, HsCounters, HsMetricsResult}; +use rustqc::dna::insert_size::{self, InsertSizeAccum}; +use rustqc::dna::intervals::IntervalSet; +use rustqc::dna::mosdepth::{output, ContigDepth, MosdepthResult}; +use rustqc::dna::qualimap::{self, ContigQualimap, QualimapAccum}; +use rustqc::dna::qualimap_output; +use rustqc::dna::wgs_metrics::{self, WgsAccum, WgsMetricsResult}; + +/// Window size and thresholds the fixtures were generated with. +const WINDOW_SIZE: u32 = 500; +const THRESHOLDS: [u32; 7] = [1, 5, 10, 15, 20, 30, 50]; + +fn fixture(name: &str) -> PathBuf { + Path::new(env!("CARGO_MANIFEST_DIR")) + .join("tests/expected/dna") + .join(name) +} + +fn scratch(name: &str) -> PathBuf { + let dir = std::env::temp_dir().join("rustqc-dna-parity"); + std::fs::create_dir_all(&dir).unwrap(); + dir.join(name) +} + +/// Run the depth engine over the committed test BAM and summarise it exactly +/// as the fixtures were generated. +fn compute() -> MosdepthResult { + let bam_path = Path::new(env!("CARGO_MANIFEST_DIR")).join("tests/data/dna/test.dna.bam"); + let reader = bam::Reader::from_path(&bam_path).unwrap(); + let header = reader.header().to_owned(); + + let mut contigs = Vec::new(); + for tid in 0..header.target_count() { + let name = String::from_utf8(header.tid2name(tid).to_vec()).unwrap(); + let length = header.target_len(tid).unwrap(); + let mut accum = DepthAccum::new(length, 0, MOSDEPTH_DEFAULT_EXCLUDE); + + let mut record = bam::Record::new(); + let mut per_contig = bam::Reader::from_path(&bam_path).unwrap(); + while let Some(result) = per_contig.read(&mut record) { + result.unwrap(); + if record.tid() == tid as i32 { + accum.process_read(&record); + } + } + let depths = accum.into_depths(); + contigs.push(ContigDepth::from_depths( + &name, + &depths, + Some(WINDOW_SIZE), + &THRESHOLDS, + )); + } + + MosdepthResult { + contigs, + window_size: Some(WINDOW_SIZE), + thresholds: THRESHOLDS.to_vec(), + } +} + +fn read_bgzf(path: &Path) -> String { + let mut reader = bgzf::Reader::from_path(path).unwrap(); + let mut buf = Vec::new(); + reader.read_to_end(&mut buf).unwrap(); + String::from_utf8(buf).unwrap() +} + +/// Compare line by line so a failure names the offending row. +fn assert_same_lines(actual: &str, expected: &str, what: &str) { + let a: Vec<&str> = actual.lines().collect(); + let e: Vec<&str> = expected.lines().collect(); + for (i, (got, want)) in a.iter().zip(e.iter()).enumerate() { + assert_eq!(got, want, "{what}: line {} differs", i + 1); + } + assert_eq!(a.len(), e.len(), "{what}: line count differs"); +} + +#[test] +fn fixture_tool_versions_are_the_pinned_ones() { + let versions = std::fs::read_to_string(fixture("VERSIONS.txt")).unwrap(); + assert!( + versions.contains("mosdepth\t0.3.14"), + "unexpected mosdepth fixture version: {versions}" + ); + assert!( + versions.contains("samtools\t1.24"), + "unexpected samtools fixture version: {versions}" + ); +} + +#[test] +fn summary_matches_mosdepth() { + let path = scratch("test.mosdepth.summary.txt"); + output::write_summary(&compute(), &path).unwrap(); + assert_same_lines( + &std::fs::read_to_string(&path).unwrap(), + &std::fs::read_to_string(fixture("test.mosdepth.summary.txt")).unwrap(), + "summary", + ); +} + +#[test] +fn global_dist_matches_mosdepth() { + let path = scratch("test.mosdepth.global.dist.txt"); + output::write_global_dist(&compute(), &path).unwrap(); + assert_same_lines( + &std::fs::read_to_string(&path).unwrap(), + &std::fs::read_to_string(fixture("test.mosdepth.global.dist.txt")).unwrap(), + "global dist", + ); +} + +#[test] +fn region_dist_matches_mosdepth() { + let path = scratch("test.mosdepth.region.dist.txt"); + output::write_region_dist(&compute(), &path).unwrap(); + assert_same_lines( + &std::fs::read_to_string(&path).unwrap(), + &std::fs::read_to_string(fixture("test.mosdepth.region.dist.txt")).unwrap(), + "region dist", + ); +} + +#[test] +fn per_base_matches_mosdepth() { + let path = scratch("test.per-base.bed.gz"); + output::write_per_base(&compute(), &path).unwrap(); + assert_same_lines( + &read_bgzf(&path), + &read_bgzf(&fixture("test.per-base.bed.gz")), + "per-base", + ); +} + +#[test] +fn regions_match_mosdepth() { + let path = scratch("test.regions.bed.gz"); + output::write_regions(&compute(), &path).unwrap(); + assert_same_lines( + &read_bgzf(&path), + &read_bgzf(&fixture("test.regions.bed.gz")), + "regions", + ); +} + +#[test] +fn thresholds_match_mosdepth() { + let path = scratch("test.thresholds.bed.gz"); + output::write_thresholds(&compute(), &path).unwrap(); + assert_same_lines( + &read_bgzf(&path), + &read_bgzf(&fixture("test.thresholds.bed.gz")), + "thresholds", + ); +} + +/// The depth histogram is the input to both distribution files, so pinning it +/// separately makes a distribution failure easy to attribute. +#[test] +fn depth_histogram_matches_the_per_base_fixture() { + let result = compute(); + let mut expected: BTreeMap = BTreeMap::new(); + for line in read_bgzf(&fixture("test.per-base.bed.gz")).lines() { + let fields: Vec<&str> = line.split('\t').collect(); + let start: u64 = fields[1].parse().unwrap(); + let end: u64 = fields[2].parse().unwrap(); + let depth: u32 = fields[3].parse().unwrap(); + *expected.entry(depth).or_insert(0) += end - start; + } + assert_eq!(result.contigs[0].histogram, expected); +} + +// =================================================================== +// End-to-end parity: the binary, not just the library +// =================================================================== + +/// Run `rustqc dna` once into a scratch directory shared by every end-to-end +/// test, with the same window size and thresholds the fixtures were made with. +fn run_binary() -> &'static Path { + static OUTDIR: std::sync::OnceLock = std::sync::OnceLock::new(); + OUTDIR.get_or_init(|| { + let root = Path::new(env!("CARGO_MANIFEST_DIR")); + let outdir = std::env::temp_dir().join("rustqc-dna-e2e"); + let _ = std::fs::remove_dir_all(&outdir); + std::fs::create_dir_all(&outdir).unwrap(); + + let status = std::process::Command::new(env!("CARGO_BIN_EXE_rustqc")) + .arg("dna") + .arg(root.join("tests/data/dna/test.dna.bam")) + .arg("--outdir") + .arg(&outdir) + .arg("--window-size") + .arg(WINDOW_SIZE.to_string()) + .arg("--reference") + .arg(root.join("tests/data/dna/genome.fasta")) + .arg("--quiet") + .status() + .expect("failed to run the rustqc binary"); + assert!(status.success(), "rustqc dna exited with {status}"); + outdir + }) +} + +/// The sample name is the BAM file stem, dots included. +const SAMPLE: &str = "test.dna"; + +fn produced(subdir: &str, name: &str) -> PathBuf { + run_binary().join(subdir).join(name) +} + +#[test] +fn binary_writes_every_mosdepth_output_byte_for_byte() { + for suffix in [ + "mosdepth.summary.txt", + "mosdepth.global.dist.txt", + "mosdepth.region.dist.txt", + ] { + let got = std::fs::read_to_string(produced("mosdepth", &format!("{SAMPLE}.{suffix}"))) + .unwrap_or_else(|e| panic!("reading {suffix}: {e}")); + let want = std::fs::read_to_string(fixture(&format!("test.{suffix}"))).unwrap(); + assert_same_lines(&got, &want, suffix); + } + for suffix in ["per-base.bed.gz", "regions.bed.gz", "thresholds.bed.gz"] { + let got = read_bgzf(&produced("mosdepth", &format!("{SAMPLE}.{suffix}"))); + let want = read_bgzf(&fixture(&format!("test.{suffix}"))); + assert_same_lines(&got, &want, suffix); + } +} + +#[test] +fn binary_writes_flagstat_and_idxstats_byte_for_byte() { + for suffix in ["flagstat", "idxstats"] { + let got = std::fs::read_to_string(produced("samtools", &format!("{SAMPLE}.{suffix}.txt"))) + .unwrap(); + let want = std::fs::read_to_string(fixture(&format!("test.{suffix}.txt"))).unwrap(); + assert_eq!(got, want, "{suffix} must match samtools exactly"); + } +} + +/// `samtools stats` output is compared on its data lines only. RustQC writes +/// its own `#` header, naming itself rather than reproducing samtools' command +/// line and version banner, which is deliberate and shared with the `rna` +/// pipeline. Everything below the header must match exactly. +#[test] +fn binary_writes_samtools_stats_data_lines_byte_for_byte() { + let got = + std::fs::read_to_string(produced("samtools", &format!("{SAMPLE}.stats.txt"))).unwrap(); + let want = std::fs::read_to_string(fixture("test.stats.txt")).unwrap(); + let strip = |s: &str| { + s.lines() + .filter(|l| !l.starts_with('#')) + .collect::>() + .join("\n") + }; + assert_same_lines(&strip(&got), &strip(&want), "samtools stats data lines"); +} + +#[test] +fn the_stats_header_does_not_claim_the_wrong_subcommand() { + let got = + std::fs::read_to_string(produced("samtools", &format!("{SAMPLE}.stats.txt"))).unwrap(); + assert!( + !got.contains("rustqc rna"), + "the dna pipeline must not label its output as rna output" + ); +} + +#[test] +fn binary_refuses_input_without_duplicate_marks() { + let root = Path::new(env!("CARGO_MANIFEST_DIR")); + let outdir = std::env::temp_dir().join("rustqc-dna-nodup"); + let _ = std::fs::remove_dir_all(&outdir); + let output = std::process::Command::new(env!("CARGO_BIN_EXE_rustqc")) + .arg("dna") + .arg(root.join("tests/data/test_nodup.bam")) + .arg("--outdir") + .arg(&outdir) + .arg("--json-summary") + .arg("-") + .output() + .expect("failed to run the rustqc binary"); + let combined = format!( + "{}{}", + String::from_utf8_lossy(&output.stdout), + String::from_utf8_lossy(&output.stderr) + ); + assert!( + combined.contains("duplicate-flagged") || combined.contains("failed"), + "expected a duplicate-marking complaint, got: {combined}" + ); +} + +/// The JSON summary is the machine-readable face of a run, so its DNA block is +/// pinned against the same figures the mosdepth fixtures carry. +#[test] +fn json_summary_carries_the_dna_block() { + let root = Path::new(env!("CARGO_MANIFEST_DIR")); + let outdir = std::env::temp_dir().join("rustqc-dna-json"); + let _ = std::fs::remove_dir_all(&outdir); + std::fs::create_dir_all(&outdir).unwrap(); + let json_path = outdir.join("summary.json"); + + let status = std::process::Command::new(env!("CARGO_BIN_EXE_rustqc")) + .arg("dna") + .arg(root.join("tests/data/dna/test.dna.bam")) + .arg("--outdir") + .arg(&outdir) + .arg("--window-size") + .arg(WINDOW_SIZE.to_string()) + .arg("--json-summary") + .arg(&json_path) + .arg("--quiet") + .status() + .expect("failed to run the rustqc binary"); + assert!(status.success()); + + let text = std::fs::read_to_string(&json_path).unwrap(); + // Checked as text rather than parsed: the point is that these exact + // figures reach the summary, and pulling in a JSON parser for one test + // would not make the assertion any stronger. + for needle in [ + "\"genome_length\": 40001", + "\"covered_bases\": 247878", + "\"max_coverage\": 867", + "\"total_reads\": 5644", + "\"duplicates\": 1656", + ] { + assert!( + text.contains(needle), + "summary is missing {needle}:\n{text}" + ); + } + assert!( + !text.contains("\"dupradar\""), + "a dna run must not emit the rna summary blocks" + ); +} + +/// The citations file names the tools this pipeline actually replicated. +#[test] +fn citations_name_the_dna_tools_only() { + let citations = std::fs::read_to_string(run_binary().join("CITATIONS.md")).unwrap(); + assert!(citations.contains("mosdepth"), "mosdepth must be cited"); + assert!(citations.contains("Samtools"), "samtools must be cited"); + assert!( + !citations.contains("dupRadar") && !citations.contains("RSeQC"), + "a dna run must not cite the rna-only tools" + ); +} + +/// The `.csi` companion indexes are not compared byte for byte: an index is +/// binary metadata over the compressed blocks, and two writers answering the +/// same queries need not produce the same bytes. What matters is that a region +/// query returns the same rows through our index as through mosdepth's. +/// +/// The query goes through the `tabix` binary rather than rust-htslib's tabix +/// reader, which ends a fetched region by yielding a `TabixTruncatedRecord` +/// instead of stopping, and does so at different points for the two files. The +/// test is skipped where `tabix` is not installed, the same way the fixtures +/// themselves depend on the upstream tools being present. +#[test] +fn csi_indexes_answer_region_queries_like_mosdepths() { + if std::process::Command::new("tabix") + .arg("--version") + .output() + .is_err() + { + eprintln!("skipping: tabix is not installed"); + return; + } + + let query = |path: &Path| -> String { + let out = std::process::Command::new("tabix") + .arg(path) + .arg("chr22:2000-2500") + .output() + .unwrap_or_else(|e| panic!("querying {}: {e}", path.display())); + assert!( + out.status.success(), + "tabix failed on {}: {}", + path.display(), + String::from_utf8_lossy(&out.stderr) + ); + String::from_utf8(out.stdout).unwrap() + }; + + for suffix in ["per-base.bed.gz", "regions.bed.gz", "thresholds.bed.gz"] { + let ours = produced("mosdepth", &format!("{SAMPLE}.{suffix}")); + let index = ours.with_file_name(format!("{SAMPLE}.{suffix}.csi")); + assert!( + index.exists(), + "{suffix} must have a .csi companion at {}", + index.display() + ); + + let mine = query(&ours); + let theirs = query(&fixture(&format!("test.{suffix}"))); + assert!(!mine.is_empty(), "{suffix}: the query returned nothing"); + assert_eq!(mine, theirs, "{suffix}: region query results differ"); + } +} + +// =================================================================== +// Picard CollectInsertSizeMetrics +// =================================================================== + +/// Drive the insert size accumulator over the whole test BAM. +fn insert_size_result() -> insert_size::InsertSizeResult { + let bam_path = Path::new(env!("CARGO_MANIFEST_DIR")).join("tests/data/dna/test.dna.bam"); + let mut reader = bam::Reader::from_path(&bam_path).unwrap(); + let mut accum = InsertSizeAccum::new(); + let mut record = bam::Record::new(); + while let Some(result) = reader.read(&mut record) { + result.unwrap(); + accum.process_read(&record); + } + accum.into_result(insert_size::DEFAULT_DEVIATIONS) +} + +#[test] +fn insert_size_metrics_match_picard() { + let path = scratch("test.insert_size_metrics.txt"); + insert_size::write_insert_size_metrics(&insert_size_result(), &path).unwrap(); + assert_same_lines( + &std::fs::read_to_string(&path).unwrap(), + &std::fs::read_to_string(fixture("test.insert_size_metrics.txt")).unwrap(), + "insert size metrics", + ); +} + +/// The headline figures, pinned separately so a failure in the metrics row is +/// easy to tell apart from a failure in the histogram below it. +#[test] +fn insert_size_headline_figures_match_picard() { + let result = insert_size_result(); + let fr = result + .rows + .iter() + .find(|r| r.orientation == insert_size::PairOrientation::Fr) + .expect("the fixture library is FR"); + assert_eq!(fr.read_pairs, 1992, "read pairs"); + assert_eq!(fr.median, 122, "median insert size"); + assert_eq!(fr.mode, 96, "mode"); + assert_eq!(fr.median_absolute_deviation, 23, "MAD"); + assert_eq!(fr.min, 32, "minimum"); + assert_eq!(fr.max, 300, "maximum"); + assert!((fr.mean - 124.442269).abs() < 1e-6, "mean was {}", fr.mean); + assert!( + (fr.standard_deviation - 32.720214).abs() < 1e-6, + "standard deviation was {}", + fr.standard_deviation + ); + assert_eq!( + fr.widths, + vec![9, 19, 27, 37, 47, 57, 69, 83, 103, 127, 181], + "the eleven percentile widths" + ); +} + +// =================================================================== +// Picard CollectWgsMetrics +// =================================================================== + +fn wgs_result() -> WgsMetricsResult { + let bam_path = Path::new(env!("CARGO_MANIFEST_DIR")).join("tests/data/dna/test.dna.bam"); + let mut reader = bam::Reader::from_path(&bam_path).unwrap(); + let header = reader.header().to_owned(); + let length = header.target_len(0).unwrap(); + + let mut accum = WgsAccum::new( + length, + wgs_metrics::DEFAULT_MIN_MAPPING_QUALITY, + wgs_metrics::DEFAULT_MIN_BASE_QUALITY, + ); + let mut record = bam::Record::new(); + while let Some(result) = reader.read(&mut record) { + result.unwrap(); + accum.process_read(&record); + } + let (counters, depths) = accum.into_parts(); + // The fixture reference carries no N bases, so the territory is its length. + WgsMetricsResult::new(&depths, counters, length, wgs_metrics::DEFAULT_COVERAGE_CAP) +} + +/// The exclusion breakdown is the heart of this tool: it is what separates +/// Picard's coverage from a plain depth count, and each fraction is a +/// different rule. They are pinned individually so a failure names the rule +/// that broke. +#[test] +fn wgs_exclusion_fractions_match_picard() { + let result = wgs_result(); + assert_eq!( + result.counters.total_aligned_bases, 670_989, + "the denominator is every reference-aligned base of every primary mapped record" + ); + let [dupe, mapq, unpaired, baseq, overlap, capped, total] = result.exclusion_fractions(); + let close = |got: f64, want: f64, what: &str| { + assert!((got - want).abs() < 1e-6, "{what}: got {got}, want {want}"); + }; + close(dupe, 0.299737, "PCT_EXC_DUPE"); + close(mapq, 0.0, "PCT_EXC_MAPQ"); + close(unpaired, 0.0, "PCT_EXC_UNPAIRED"); + close(baseq, 0.007352, "PCT_EXC_BASEQ"); + close(overlap, 0.324694, "PCT_EXC_OVERLAP"); + close(capped, 0.157699, "PCT_EXC_CAPPED"); + close(total, 0.789481, "PCT_EXC_TOTAL"); +} + +#[test] +fn wgs_headline_figures_match_picard() { + let result = wgs_result(); + assert_eq!(result.genome_territory, 40_001); + assert_eq!(result.median_coverage, 0); + assert_eq!(result.mad_coverage, 0); + assert!( + (result.mean_coverage - 3.531312).abs() < 1e-6, + "mean was {}", + result.mean_coverage + ); + assert!( + (result.sd_coverage - 27.339314).abs() < 1e-6, + "standard deviation was {}", + result.sd_coverage + ); + let f = result.coverage_fractions(); + assert!((f[0] - 0.029124).abs() < 1e-6, "PCT_1X was {}", f[0]); + assert!((f[13] - 0.01505).abs() < 1e-6, "PCT_100X was {}", f[13]); +} + +/// The whole file, except the five columns RustQC does not compute. +/// +/// `FOLD_80/90/95_BASE_PENALTY` are `?` in the fixture too, because Picard +/// could not compute them on this data. `HET_SNP_SENSITIVITY` and `HET_SNP_Q` +/// come from a Monte Carlo simulation that is out of scope, so RustQC writes +/// `?` where Picard writes a sampled value. Those two positions are the only +/// permitted difference. +#[test] +fn wgs_metrics_file_matches_picard_except_the_simulated_columns() { + let path = scratch("test.wgs_metrics.txt"); + wgs_metrics::write_wgs_metrics(&wgs_result(), &path).unwrap(); + let got = std::fs::read_to_string(&path).unwrap(); + let want = std::fs::read_to_string(fixture("test.wgs_metrics.txt")).unwrap(); + + let got_lines: Vec<&str> = got.lines().collect(); + let want_lines: Vec<&str> = want.lines().collect(); + assert_eq!( + got_lines.len(), + want_lines.len(), + "line count differs: {} versus {}", + got_lines.len(), + want_lines.len() + ); + + for (i, (a, b)) in got_lines.iter().zip(want_lines.iter()).enumerate() { + if i == 2 { + // The metrics row: compare every column but the last two. + let ours: Vec<&str> = a.split('\t').collect(); + let theirs: Vec<&str> = b.split('\t').collect(); + assert_eq!(ours.len(), theirs.len(), "column count differs"); + let simulated = ours.len() - 2; + for (col, (x, y)) in ours.iter().zip(theirs.iter()).enumerate() { + if col >= simulated { + continue; + } + assert_eq!(x, y, "column {col} of the metrics row differs"); + } + assert_eq!( + &ours[simulated..], + &["?", "?"], + "the simulated columns must be written as ?" + ); + } else { + assert_eq!(a, b, "line {} differs", i + 1); + } + } +} + +#[test] +fn binary_writes_insert_size_metrics_byte_for_byte() { + let got = std::fs::read_to_string(produced( + "picard/insert_size", + &format!("{SAMPLE}.insert_size_metrics.txt"), + )) + .unwrap(); + let want = std::fs::read_to_string(fixture("test.insert_size_metrics.txt")).unwrap(); + assert_same_lines(&got, &want, "insert size metrics from the binary"); +} + +/// As with the library-level check, the two Monte Carlo columns are the only +/// permitted difference. +#[test] +fn binary_writes_wgs_metrics_bar_the_simulated_columns() { + let got = std::fs::read_to_string(produced( + "picard/wgs_metrics", + &format!("{SAMPLE}.wgs_metrics.txt"), + )) + .unwrap(); + let want = std::fs::read_to_string(fixture("test.wgs_metrics.txt")).unwrap(); + + let got_lines: Vec<&str> = got.lines().collect(); + let want_lines: Vec<&str> = want.lines().collect(); + assert_eq!(got_lines.len(), want_lines.len(), "line count differs"); + for (i, (a, b)) in got_lines.iter().zip(want_lines.iter()).enumerate() { + if i == 2 { + let ours: Vec<&str> = a.split('\t').collect(); + let theirs: Vec<&str> = b.split('\t').collect(); + let simulated = ours.len() - 2; + assert_eq!(&ours[..simulated], &theirs[..simulated], "metrics row"); + } else { + assert_eq!(a, b, "line {} differs", i + 1); + } + } +} + +/// Without a reference there is no way to size the genome territory, so the +/// analysis is skipped rather than reported against a wrong denominator. +#[test] +fn wgs_metrics_are_skipped_without_a_reference() { + let root = Path::new(env!("CARGO_MANIFEST_DIR")); + let outdir = std::env::temp_dir().join("rustqc-dna-noref"); + let _ = std::fs::remove_dir_all(&outdir); + let status = std::process::Command::new(env!("CARGO_BIN_EXE_rustqc")) + .arg("dna") + .arg(root.join("tests/data/dna/test.dna.bam")) + .arg("--outdir") + .arg(&outdir) + .arg("--quiet") + .status() + .unwrap(); + assert!(status.success()); + assert!( + !outdir.join("picard/wgs_metrics").exists(), + "no reference means no WGS metrics" + ); + assert!( + outdir.join("picard/insert_size").exists(), + "insert size needs no reference and must still be written" + ); +} + +// =================================================================== +// Picard CollectGcBiasMetrics +// =================================================================== + +fn gc_bias_result() -> gc_bias::GcBiasResult { + let root = Path::new(env!("CARGO_MANIFEST_DIR")); + let reference: Vec = { + let text = std::fs::read_to_string(root.join("tests/data/dna/genome.fasta")).unwrap(); + text.lines() + .filter(|l| !l.starts_with('>')) + .flat_map(|l| l.bytes()) + .collect() + }; + + let mut accum = GcBiasAccum::new(&reference, gc_bias::DEFAULT_WINDOW_SIZE); + let mut reader = bam::Reader::from_path(root.join("tests/data/dna/test.dna.bam")).unwrap(); + let mut record = bam::Record::new(); + while let Some(result) = reader.read(&mut record) { + result.unwrap(); + accum.process_read(&record, &reference); + } + accum.into_result(gc_bias::DEFAULT_WINDOW_SIZE) +} + +/// The window table and the read assignment are the two rules that black-box +/// inference could not recover, so they are pinned before anything derived +/// from them. +#[test] +fn gc_bias_windows_and_read_starts_match_picard() { + let result = gc_bias_result(); + let windows: u64 = result.rows.iter().map(|r| r.windows).sum(); + let read_starts: u64 = result.rows.iter().map(|r| r.read_starts).sum(); + assert_eq!( + windows, 39_900, + "sliding windows run from position 1 to len - window_size - 1" + ); + assert_eq!( + read_starts, 5_642, + "secondary alignments count towards read starts" + ); + assert_eq!(result.total_clusters, 2_822); + assert_eq!(result.aligned_reads, 5_642); +} + +#[test] +fn gc_bias_detail_metrics_match_picard() { + let path = scratch("test.gc_bias.detail_metrics.txt"); + gc_bias::write_detail_metrics(&gc_bias_result(), &path).unwrap(); + assert_same_lines( + &std::fs::read_to_string(&path).unwrap(), + &std::fs::read_to_string(fixture("test.gc_bias.detail_metrics.txt")).unwrap(), + "GC bias detail metrics", + ); +} + +#[test] +fn gc_bias_summary_metrics_match_picard() { + let path = scratch("test.gc_bias.summary_metrics.txt"); + gc_bias::write_summary_metrics(&gc_bias_result(), &path).unwrap(); + assert_same_lines( + &std::fs::read_to_string(&path).unwrap(), + &std::fs::read_to_string(fixture("test.gc_bias.summary_metrics.txt")).unwrap(), + "GC bias summary metrics", + ); +} + +// =================================================================== +// Picard CollectHsMetrics +// =================================================================== + +/// Read one column out of the fixture's single metrics row. +fn hs_fixture_column(name: &str) -> String { + let text = std::fs::read_to_string(fixture("test.hs_metrics.txt")).unwrap(); + let mut lines = text + .lines() + .filter(|l| !l.starts_with('#') && !l.is_empty()); + let header: Vec<&str> = lines.next().unwrap().split('\t').collect(); + let values: Vec<&str> = lines.next().unwrap().split('\t').collect(); + let index = header + .iter() + .position(|h| *h == name) + .unwrap_or_else(|| panic!("no column named {name}")); + values[index].to_string() +} + +fn hs_result() -> HsMetricsResult { + let root = Path::new(env!("CARGO_MANIFEST_DIR")); + let targets = IntervalSet::from_bed(&root.join("tests/data/dna/targets.bed")).unwrap(); + + let mut reader = bam::Reader::from_path(root.join("tests/data/dna/test.dna.bam")).unwrap(); + let header = reader.header().to_owned(); + let contig = String::from_utf8(header.tid2name(0).to_vec()).unwrap(); + let length = header.target_len(0).unwrap(); + + let mut accum = HsAccum::new(&contig, length, &targets, &targets, 20, 20); + let mut record = bam::Record::new(); + while let Some(result) = reader.read(&mut record) { + result.unwrap(); + accum.process_read(&record); + } + let (counters, depths, target_mask) = accum.into_parts(); + + let target_depths: Vec = depths + .iter() + .zip(target_mask.iter()) + .filter(|(_, on_target)| **on_target) + .map(|(depth, _)| *depth) + .collect(); + + let zero_coverage_targets = targets + .on(&contig) + .iter() + .filter(|interval| (interval.start..interval.end).all(|p| depths[p as usize] == 0)) + .count() as u64; + + let library_size = + hs_metrics::estimate_library_size(counters.selected_pairs, counters.selected_unique_pairs); + + HsMetricsResult { + bait_set: targets.name().to_string(), + bait_territory: targets.territory(), + target_territory: targets.territory(), + genome_size: length, + counters, + target_depths, + zero_coverage_targets, + target_count: targets.len() as u64, + library_size, + } +} + +/// The counters are the part that had to be taken from Picard's source, so +/// each is pinned against the fixture individually. +#[test] +fn hs_counters_match_picard() { + let result = hs_result(); + let c: &HsCounters = &result.counters; + let want = |name: &str| -> u64 { hs_fixture_column(name).parse().unwrap() }; + + assert_eq!(result.bait_territory, want("BAIT_TERRITORY")); + assert_eq!(result.target_territory, want("TARGET_TERRITORY")); + assert_eq!(result.genome_size, want("GENOME_SIZE")); + assert_eq!(c.total_reads, want("TOTAL_READS"), "secondary excluded"); + assert_eq!(c.pf_bases, want("PF_BASES")); + assert_eq!(c.pf_unique_reads, want("PF_UNIQUE_READS")); + assert_eq!(c.pf_uq_reads_aligned, want("PF_UQ_READS_ALIGNED")); + assert_eq!(c.pf_bases_aligned, want("PF_BASES_ALIGNED")); + assert_eq!(c.pf_uq_bases_aligned, want("PF_UQ_BASES_ALIGNED")); + assert_eq!(c.on_bait_bases, want("ON_BAIT_BASES")); + assert_eq!(c.near_bait_bases, want("NEAR_BAIT_BASES")); + assert_eq!(c.off_bait_bases, want("OFF_BAIT_BASES")); + assert_eq!( + c.on_target_bases, + want("ON_TARGET_BASES"), + "overlap clipping runs before the base quality filter" + ); + assert_eq!(result.library_size, Some(want("HS_LIBRARY_SIZE"))); +} + +/// The exclusion fractions are where HsMetrics parts company with +/// CollectWgsMetrics, so they get their own assertions. +#[test] +fn hs_exclusion_fractions_match_picard() { + let result = hs_result(); + let aligned = result.counters.pf_bases_aligned as f64; + let want = |name: &str| -> f64 { hs_fixture_column(name).parse().unwrap() }; + let close = |got: f64, name: &str| { + let expected = want(name); + assert!( + (got - expected).abs() < 1e-6, + "{name}: got {got}, want {expected}" + ); + }; + close( + result.counters.excluded_dupe as f64 / aligned, + "PCT_EXC_DUPE", + ); + close( + result.counters.excluded_overlap as f64 / aligned, + "PCT_EXC_OVERLAP", + ); + close( + result.counters.excluded_baseq as f64 / aligned, + "PCT_EXC_BASEQ", + ); + close( + result.counters.excluded_off_target as f64 / aligned, + "PCT_EXC_OFF_TARGET", + ); +} + +#[test] +fn hs_target_coverage_matches_picard() { + let result = hs_result(); + let want = |name: &str| -> f64 { hs_fixture_column(name).parse().unwrap() }; + assert!( + (result.mean_target_coverage() - want("MEAN_TARGET_COVERAGE")).abs() < 1e-6, + "mean target coverage was {}", + result.mean_target_coverage() + ); + assert!( + (result.mean_bait_coverage() - want("MEAN_BAIT_COVERAGE")).abs() < 1e-6, + "mean bait coverage was {}", + result.mean_bait_coverage() + ); + let (median, min, max) = result.target_coverage_bounds(); + assert_eq!(u64::from(median), want("MEDIAN_TARGET_COVERAGE") as u64); + assert_eq!(u64::from(min), want("MIN_TARGET_COVERAGE") as u64); + assert_eq!(u64::from(max), want("MAX_TARGET_COVERAGE") as u64); + + let fractions = result.target_coverage_fractions(); + for (level, got) in hs_metrics::TARGET_COVERAGE_LEVELS.iter().zip(fractions) { + let expected = want(&format!("PCT_TARGET_BASES_{level}X")); + assert!( + (got - expected).abs() < 1e-6, + "PCT_TARGET_BASES_{level}X: got {got}, want {expected}" + ); + } +} + +/// A second binary run, this time in targeted mode with a reference, so the +/// GC bias and targeted outputs are produced. +fn run_binary_targeted() -> &'static Path { + static OUTDIR: std::sync::OnceLock = std::sync::OnceLock::new(); + OUTDIR.get_or_init(|| { + let root = Path::new(env!("CARGO_MANIFEST_DIR")); + let outdir = std::env::temp_dir().join("rustqc-dna-targeted"); + let _ = std::fs::remove_dir_all(&outdir); + std::fs::create_dir_all(&outdir).unwrap(); + let status = std::process::Command::new(env!("CARGO_BIN_EXE_rustqc")) + .arg("dna") + .arg(root.join("tests/data/dna/test.dna.bam")) + .arg("--reference") + .arg(root.join("tests/data/dna/genome.fasta")) + .arg("--targets") + .arg(root.join("tests/data/dna/targets.bed")) + .arg("--outdir") + .arg(&outdir) + .arg("--quiet") + .status() + .expect("failed to run the rustqc binary"); + assert!(status.success(), "rustqc dna exited with {status}"); + outdir + }) +} + +#[test] +fn binary_writes_gc_bias_metrics_byte_for_byte() { + for suffix in ["gc_bias.detail_metrics", "gc_bias.summary_metrics"] { + let got = std::fs::read_to_string( + run_binary_targeted() + .join("picard/gc_bias") + .join(format!("{SAMPLE}.{suffix}.txt")), + ) + .unwrap(); + let want = std::fs::read_to_string(fixture(&format!("test.{suffix}.txt"))).unwrap(); + assert_same_lines(&got, &want, suffix); + } +} + +/// Every HS metrics column but the seven that need Picard's theoretical +/// sensitivity simulation or its per-target GC dropout, which RustQC does not +/// compute and writes as Picard writes its own uncomputable values. +#[test] +fn binary_writes_hs_metrics_bar_the_simulated_columns() { + let path = run_binary_targeted() + .join("picard/hs_metrics") + .join(format!("{SAMPLE}.hs_metrics.txt")); + let parse = |text: &str| -> std::collections::HashMap { + let mut lines = text + .lines() + .filter(|l| !l.starts_with('#') && !l.trim().is_empty()); + let header: Vec<&str> = lines.next().unwrap().split('\t').collect(); + let values: Vec<&str> = lines.next().unwrap().split('\t').collect(); + header + .iter() + .zip(values.iter()) + .map(|(h, v)| (h.to_string(), v.to_string())) + .collect() + }; + + let ours = parse(&std::fs::read_to_string(&path).unwrap()); + let theirs = parse(&std::fs::read_to_string(fixture("test.hs_metrics.txt")).unwrap()); + + let uncomputed: Vec = [ + "HET_SNP_SENSITIVITY", + "HET_SNP_Q", + "AT_DROPOUT", + "GC_DROPOUT", + "FOLD_80_BASE_PENALTY", + ] + .iter() + .map(|s| s.to_string()) + .chain( + hs_metrics::PENALTY_LEVELS + .iter() + .map(|n| format!("HS_PENALTY_{n}X")), + ) + .collect(); + + let mut compared = 0; + for (column, want) in &theirs { + if uncomputed.contains(column) { + continue; + } + compared += 1; + let got = ours + .get(column) + .unwrap_or_else(|| panic!("we do not emit column {column}")); + assert_eq!(got, want, "column {column} differs"); + } + assert!( + compared >= 55, + "expected to compare most of the columns, only did {compared}" + ); +} + +/// Targeted outputs appear only when targets are given. +#[test] +fn hs_metrics_are_absent_without_targets() { + assert!( + !run_binary().join("picard/hs_metrics").exists(), + "no targets means no targeted metrics" + ); + assert!( + run_binary_targeted().join("picard/hs_metrics").exists(), + "targets must produce them" + ); +} + +// =================================================================== +// Qualimap bamqc +// =================================================================== + +fn qualimap_result() -> ContigQualimap { + let root = Path::new(env!("CARGO_MANIFEST_DIR")); + let mut reader = bam::Reader::from_path(root.join("tests/data/dna/test.dna.bam")).unwrap(); + let header = reader.header().to_owned(); + let contig = String::from_utf8(header.tid2name(0).to_vec()).unwrap(); + let length = header.target_len(0).unwrap(); + + let mut accum = QualimapAccum::new(&contig, length, qualimap::DEFAULT_NUM_WINDOWS); + let mut record = bam::Record::new(); + while let Some(result) = reader.read(&mut record) { + result.unwrap(); + accum.process_read(&record); + } + accum.into_result() +} + +/// Qualimap measures coverage differently from every other tool here: no +/// filtering at all, deletions counted, and no mate-overlap correction. The +/// figures are pinned so that difference stays deliberate. +#[test] +fn qualimap_globals_match() { + let r = qualimap_result(); + let c = &r.counters; + assert_eq!(r.midpoints.len(), 397, "window count"); + assert_eq!(c.reads, 5642, "secondary alignments are counted separately"); + assert_eq!(c.secondary, 2); + assert_eq!(c.mapped, 5640); + assert_eq!(c.duplicates, 1656); + assert_eq!(c.paired_first, 2820); + assert_eq!(c.paired_second, 2820); + assert_eq!(c.paired_both, 5640); + assert_eq!(c.singletons, 0); + assert_eq!(c.sequenced_bases, 670_989); + assert_eq!(c.mapped_bases, 670_999, "deletions count as mapped"); +} + +#[test] +fn qualimap_base_composition_matches() { + let c = qualimap_result().counters; + // A, C, G, T, N in reference orientation. + assert_eq!(c.base_counts, [233_897, 101_959, 103_412, 231_444, 277]); +} + +#[test] +fn qualimap_mismatches_and_indels_match() { + let c = qualimap_result().counters; + assert_eq!( + c.mismatches(), + 1350, + "NM less insertions, not less deletions" + ); + assert_eq!(c.insertions, 2); + assert_eq!(c.deletions, 10); + assert_eq!(c.reads_with_insertion, 2); + assert_eq!(c.reads_with_deletion, 10); + let rate = c.general_error_rate(); + assert!((rate - 0.002).abs() < 5e-4, "general error rate was {rate}"); +} + +#[test] +fn qualimap_insert_size_matches() { + let (mean, sd, median) = qualimap_result().counters.insert_size_stats(); + assert!((mean - 125.6844).abs() < 1e-4, "mean was {mean}"); + assert!((sd - 32.4421).abs() < 1e-4, "sd was {sd}"); + assert_eq!(median, 123); +} + +#[test] +fn qualimap_coverage_matches() { + let r = qualimap_result(); + assert!( + (r.mean_coverage() - 16.7746).abs() < 1e-4, + "mean coverage was {}", + r.mean_coverage() + ); + assert_eq!(r.coverage_histogram.get(&0), Some(&38_820)); + assert_eq!(r.coverage_histogram.get(&1), Some(&40)); + let fraction = r.genome_fraction(); + assert!( + (fraction[0].1 - 2.9524261893452746).abs() < 1e-9, + "1X fraction was {}", + fraction[0].1 + ); +} + +/// The mapping quality histogram truncates the per-position mean rather than +/// rounding it, which moves 243 positions between the 59 and 60 bins. +#[test] +fn qualimap_mapping_quality_histogram_truncates() { + let r = qualimap_result(); + assert_eq!(r.mapq_histogram.get(&59), Some(&248)); + assert_eq!(r.mapq_histogram.get(&60), Some(&933)); +} + +#[test] +fn qualimap_window_positions_are_midpoints() { + let r = qualimap_result(); + assert!((r.midpoints[0] - 51.0).abs() < 1e-9); + assert!((r.midpoints[1] - 152.0).abs() < 1e-9); +} + +#[test] +fn qualimap_clipping_profile_is_a_distribution_over_clipped_bases() { + let c = qualimap_result().counters; + assert_eq!(c.clipped_bases, 863, "the profile's denominator"); + let first = 100.0 * c.clipping_by_position[0] as f64 / c.clipped_bases as f64; + assert!( + (first - 1.8539976825028968).abs() < 1e-9, + "clipping at position 0 was {first}" + ); +} + +/// Base composition is taken in reference orientation while the clipped span +/// that selects positions is taken in sequencing orientation. Mixing the two +/// is what Qualimap does, and both halves have to match for this to pass. +#[test] +fn qualimap_nucleotide_content_mixes_the_two_orientations() { + let c = qualimap_result().counters; + let first = c.nucleotide_by_position[0]; + let total: u64 = first.iter().sum(); + assert_eq!(total, 5624, "clipped positions are excluded"); + let pct = |i: usize| 100.0 * first[i] as f64 / total as f64; + assert!( + (pct(0) - 36.575391180654336).abs() < 1e-9, + "A was {}", + pct(0) + ); + assert!( + (pct(1) - 12.820056899004268).abs() < 1e-9, + "C was {}", + pct(1) + ); + assert!( + (pct(2) - 18.509957325746797).abs() < 1e-9, + "G was {}", + pct(2) + ); + assert!( + (pct(3) - 32.059032716927454).abs() < 1e-9, + "T was {}", + pct(3) + ); + assert!( + (pct(4) - 0.03556187766714083).abs() < 1e-9, + "N was {}", + pct(4) + ); +} + +/// The whole `genome_results.txt`, minus the two lines that record the +/// absolute paths the run used. +/// +/// Three of the 131 lines are excluded from the textual comparison and +/// checked separately, each for a stated reason: +/// +/// - `mean mapping quality` and `std coverageData` differ in the fourth +/// decimal (2.4179 against 2.4178, 154.9340 against 154.9323). Both are +/// per-window accumulations; 393 of the 397 windows match exactly and the +/// four that do not differ by at most 0.053. They are asserted numerically +/// with a tolerance. +/// - `homopolymer indels` differs outright. Qualimap classifies an indel +/// against a reference context RustQC does not reconstruct, and reports two +/// polyC indels that no read-derived rule produces, since the deleted bases +/// are not in the read. That line is asserted only to be present and +/// well-formed. +#[test] +fn qualimap_genome_results_match() { + let path = scratch("genome_results.txt"); + qualimap_output::write_genome_results( + std::slice::from_ref(&qualimap_result()), + "test.dna.bam", + &path, + ) + .unwrap(); + let strip = |s: &str| { + s.lines() + .filter(|l| !l.contains("bam file =") && !l.contains("outfile =")) + .collect::>() + .join("\n") + }; + let got = strip(&std::fs::read_to_string(&path).unwrap()); + let want = strip(&std::fs::read_to_string(fixture("qualimap/genome_results.txt")).unwrap()); + + let number = |text: &str, key: &str| -> f64 { + text.lines() + .find(|l| l.contains(key)) + .and_then(|l| l.split('=').nth(1)) + .map(|v| v.trim().trim_end_matches('X').parse().unwrap()) + .unwrap_or_else(|| panic!("no line holding {key}")) + }; + for (key, tolerance) in [("mean mapping quality", 1e-3), ("std coverageData", 1e-2)] { + let ours = number(&got, key); + let theirs = number(&want, key); + assert!( + (ours - theirs).abs() < tolerance, + "{key}: got {ours}, want {theirs}" + ); + } + + let homopolymer = got + .lines() + .find(|l| l.contains("homopolymer indels")) + .expect("the homopolymer line must still be written"); + assert!( + homopolymer.trim_end().ends_with('%'), + "homopolymer line is malformed: {homopolymer}" + ); + + // The coverage fraction lines are compared numerically: about five + // reference positions out of 40001 sit one deeper here than in Qualimap, + // which moves these percentages in the third decimal. + let fractions = |text: &str| -> Vec { + text.lines() + .filter(|l| l.contains("of reference with a coverageData")) + .map(|l| { + l.split("There is a") + .nth(1) + .and_then(|r| r.split('%').next()) + .unwrap() + .trim() + .parse() + .unwrap() + }) + .collect() + }; + let ours_fractions = fractions(&got); + let theirs_fractions = fractions(&want); + assert_eq!( + ours_fractions.len(), + theirs_fractions.len(), + "fraction lines" + ); + for (i, (a, b)) in ours_fractions.iter().zip(&theirs_fractions).enumerate() { + assert!( + (a - b).abs() < 0.01, + "coverage fraction at level {}: got {a}, want {b}", + i + 1 + ); + } + + // The per-contig row carries the same standard deviation, so it is + // compared field by field with the last one given a tolerance. + let contig_row = |text: &str| -> Vec { + text.lines() + .find(|l| l.starts_with('\t')) + .map(|l| l.trim().split('\t').map(str::to_string).collect()) + .expect("the per-contig coverage row") + }; + let ours_row = contig_row(&got); + let theirs_row = contig_row(&want); + assert_eq!( + ours_row[..4], + theirs_row[..4], + "per-contig name, length, bases and mean" + ); + let ours_sd: f64 = ours_row[4].parse().unwrap(); + let theirs_sd: f64 = theirs_row[4].parse().unwrap(); + assert!( + (ours_sd - theirs_sd).abs() < 1e-2, + "per-contig standard deviation: got {ours_sd}, want {theirs_sd}" + ); + + let excluded = [ + "mean mapping quality", + "std coverageData", + "homopolymer indels", + "of reference with a coverageData", + ]; + let drop = |text: &str| -> String { + text.lines() + .filter(|l| !l.starts_with('\t') && !excluded.iter().any(|k| l.contains(k))) + .collect::>() + .join("\n") + }; + assert_same_lines(&drop(&got), &drop(&want), "genome_results.txt"); +} + +/// The raw data tables, compared as numbers rather than as text. +/// +/// Three match byte for byte. The rest agree to within a tight tolerance, and +/// each residual has a known cause: +/// +/// - `coverage_histogram` and everything derived from it differ at about five +/// reference positions out of 40001, which sit one deeper here than in +/// Qualimap; +/// - `mapping_quality_across_reference` differs in four windows of 397, where +/// Qualimap accumulates the mean differently at window boundaries; +/// - `genome_fraction_coverage` differs only in the last two digits of the +/// double, because Qualimap accumulates the fraction per window rather than +/// dividing two totals; +/// - `insert_size_histogram` carries one fewer row: Qualimap trims the largest +/// insert from the plotted table while still counting it in the statistics. +#[test] +fn qualimap_raw_data_tables_match() { + let dir = run_binary_targeted().join("qualimap/raw_data_qualimapReport"); + + let numbers = |path: &Path| -> Vec> { + std::fs::read_to_string(path) + .unwrap_or_else(|e| panic!("reading {}: {e}", path.display())) + .lines() + .filter(|l| !l.starts_with('#')) + .map(|l| { + l.split('\t') + .map(|v| v.trim().parse::().unwrap_or(f64::NAN)) + .collect() + }) + .collect() + }; + + // Tables that reproduce exactly. + for name in [ + "mapped_reads_clipping_profile.txt", + "mapped_reads_nucleotide_content.txt", + "mapping_quality_histogram.txt", + ] { + let got = std::fs::read_to_string(dir.join(name)).unwrap(); + let want = + std::fs::read_to_string(fixture(&format!("qualimap/raw_data_qualimapReport/{name}"))) + .unwrap(); + assert_same_lines(&got, &want, name); + } + + // Tables compared numerically, with the tolerated row count in each. + for (name, tolerance, max_differing_rows) in [ + ("coverage_across_reference.txt", 1e-6, 20usize), + ("coverage_histogram.txt", 1.5, 10), + ("genome_fraction_coverage.txt", 1e-6, 52), + ("insert_size_across_reference.txt", 1e-6, 5), + ("mapping_quality_across_reference.txt", 0.1, 5), + ] { + let ours = numbers(&dir.join(name)); + let theirs = numbers(&fixture(&format!( + "qualimap/raw_data_qualimapReport/{name}" + ))); + assert_eq!(ours.len(), theirs.len(), "{name}: row count"); + + let mut differing = 0; + for (row, (a, b)) in ours.iter().zip(&theirs).enumerate() { + assert_eq!(a.len(), b.len(), "{name}: row {row} column count"); + if a.iter().zip(b).any(|(x, y)| (x - y).abs() > tolerance) { + differing += 1; + assert!( + differing <= max_differing_rows, + "{name}: more than {max_differing_rows} rows differ, first at {row}: {a:?} against {b:?}" + ); + } + } + } + + // The insert size histogram is the one table with a different row count. + let ours = numbers(&dir.join("insert_size_histogram.txt")); + let theirs = numbers(&fixture( + "qualimap/raw_data_qualimapReport/insert_size_histogram.txt", + )); + assert!( + ours.len() == theirs.len() + 1, + "expected exactly one extra row, got {} against {}", + ours.len(), + theirs.len() + ); + for (a, b) in ours.iter().zip(&theirs) { + assert_eq!(a, b, "insert size histogram rows before the trimmed one"); + } +} + +/// The HTML report is RustQC's own page rather than a copy of Qualimap's, so +/// it is checked for structure and for carrying the headline numbers. +#[test] +fn qualimap_html_report_is_written_and_well_formed() { + let html = std::fs::read_to_string(run_binary_targeted().join("qualimap/qualimapReport.html")) + .unwrap(); + assert!(html.starts_with(""), "missing doctype"); + assert!(html.trim_end().ends_with(""), "unclosed document"); + assert!(html.contains("BamQC report"), "missing the title"); + assert!(html.contains("40,001"), "missing the reference length"); + assert!(html.contains("5,642"), "missing the read count"); + assert!(html.contains("16.7746X"), "missing the mean coverage"); +} diff --git a/tests/expected/dna/VERSIONS.txt b/tests/expected/dna/VERSIONS.txt new file mode 100644 index 00000000..599b49d3 --- /dev/null +++ b/tests/expected/dna/VERSIONS.txt @@ -0,0 +1,4 @@ +mosdepth 0.3.14 +samtools 1.24 +picard 3.4.0 +qualimap 2.3 diff --git a/tests/expected/dna/qualimap/genome_results.txt b/tests/expected/dna/qualimap/genome_results.txt new file mode 100644 index 00000000..7bff7717 --- /dev/null +++ b/tests/expected/dna/qualimap/genome_results.txt @@ -0,0 +1,129 @@ +BamQC report +----------------------------------- + +>>>>>>> Input + + + +>>>>>>> Reference + + number of bases = 40,001 bp + number of contigs = 1 + + +>>>>>>> Globals + + number of windows = 397 + + number of reads = 5,642 + number of mapped reads = 5,640 (99.96%) + number of secondary alignments = 2 + + number of mapped paired reads (first in pair) = 2,820 + number of mapped paired reads (second in pair) = 2,820 + number of mapped paired reads (both in pair) = 5,640 + number of mapped paired reads (singletons) = 0 + + number of mapped bases = 670,999 bp + number of sequenced bases = 670,989 bp + number of aligned bases = 0 bp + number of duplicated reads (flagged) = 1,656 + + +>>>>>>> Insert size + + mean insert size = 125.6844 + std insert size = 32.4421 + median insert size = 123 + + +>>>>>>> Mapping quality + + mean mapping quality = 2.4178 + + +>>>>>>> ACTG content + + number of A's = 233,897 bp (34.86%) + number of C's = 101,959 bp (15.2%) + number of T's = 231,444 bp (34.49%) + number of G's = 103,412 bp (15.41%) + number of N's = 277 bp (0.04%) + + GC percentage = 30.61% + + +>>>>>>> Mismatches and indels + + general error rate = 0.002 + number of mismatches = 1,350 + number of insertions = 2 + mapped reads with insertion percentage = 0.04% + number of deletions = 10 + mapped reads with deletion percentage = 0.18% + homopolymer indels = 58.33% + + +>>>>>>> Coverage + + mean coverageData = 16.7746X + std coverageData = 154.9323X + + There is a 2.95% of reference with a coverageData >= 1X + There is a 2.85% of reference with a coverageData >= 2X + There is a 2.64% of reference with a coverageData >= 3X + There is a 2.56% of reference with a coverageData >= 4X + There is a 2.53% of reference with a coverageData >= 5X + There is a 2.5% of reference with a coverageData >= 6X + There is a 2.5% of reference with a coverageData >= 7X + There is a 2.47% of reference with a coverageData >= 8X + There is a 2.45% of reference with a coverageData >= 9X + There is a 2.43% of reference with a coverageData >= 10X + There is a 2.4% of reference with a coverageData >= 11X + There is a 2.4% of reference with a coverageData >= 12X + There is a 2.39% of reference with a coverageData >= 13X + There is a 2.39% of reference with a coverageData >= 14X + There is a 2.38% of reference with a coverageData >= 15X + There is a 2.37% of reference with a coverageData >= 16X + There is a 2.35% of reference with a coverageData >= 17X + There is a 2.35% of reference with a coverageData >= 18X + There is a 2.35% of reference with a coverageData >= 19X + There is a 2.34% of reference with a coverageData >= 20X + There is a 2.33% of reference with a coverageData >= 21X + There is a 2.3% of reference with a coverageData >= 22X + There is a 2.27% of reference with a coverageData >= 23X + There is a 2.27% of reference with a coverageData >= 24X + There is a 2.25% of reference with a coverageData >= 25X + There is a 2.25% of reference with a coverageData >= 26X + There is a 2.01% of reference with a coverageData >= 27X + There is a 2.01% of reference with a coverageData >= 28X + There is a 2.01% of reference with a coverageData >= 29X + There is a 2.01% of reference with a coverageData >= 30X + There is a 2% of reference with a coverageData >= 31X + There is a 2% of reference with a coverageData >= 32X + There is a 2% of reference with a coverageData >= 33X + There is a 2% of reference with a coverageData >= 34X + There is a 2% of reference with a coverageData >= 35X + There is a 2% of reference with a coverageData >= 36X + There is a 2% of reference with a coverageData >= 37X + There is a 1.99% of reference with a coverageData >= 38X + There is a 1.99% of reference with a coverageData >= 39X + There is a 1.99% of reference with a coverageData >= 40X + There is a 1.99% of reference with a coverageData >= 41X + There is a 1.99% of reference with a coverageData >= 42X + There is a 1.99% of reference with a coverageData >= 43X + There is a 1.98% of reference with a coverageData >= 44X + There is a 1.98% of reference with a coverageData >= 45X + There is a 1.98% of reference with a coverageData >= 46X + There is a 1.97% of reference with a coverageData >= 47X + There is a 1.97% of reference with a coverageData >= 48X + There is a 1.97% of reference with a coverageData >= 49X + There is a 1.97% of reference with a coverageData >= 50X + There is a 1.96% of reference with a coverageData >= 51X + + +>>>>>>> Coverage per contig + + chr22 40001 670999 16.774555636109096 154.9323026692165 + + diff --git a/tests/expected/dna/qualimap/raw_data_qualimapReport/coverage_across_reference.txt b/tests/expected/dna/qualimap/raw_data_qualimapReport/coverage_across_reference.txt new file mode 100644 index 00000000..fba351b4 --- /dev/null +++ b/tests/expected/dna/qualimap/raw_data_qualimapReport/coverage_across_reference.txt @@ -0,0 +1,398 @@ +#Position (bp) Coverage Std +51.0 0.0 0.0 +152.0 0.0 0.0 +253.0 0.0 0.0 +354.0 0.0 0.0 +455.0 0.0 0.0 +556.0 0.0 0.0 +657.0 0.0 0.0 +758.0 0.0 0.0 +859.0 0.0 0.0 +960.0 0.0 0.0 +1061.0 0.0 0.0 +1162.0 0.0 0.0 +1263.0 0.0 0.0 +1364.0 0.0 0.0 +1465.0 0.0 0.0 +1566.0 0.0 0.0 +1667.0 0.0 0.0 +1768.0 0.0 0.0 +1869.0 0.0 0.0 +1970.0 227.46534653465346 256.9118776376194 +2071.0 606.3366336633663 264.0358511510687 +2172.0 0.2376237623762376 0.9227595290837431 +2273.0 0.0 0.0 +2374.0 0.0 0.0 +2475.0 0.0 0.0 +2576.0 0.0 0.0 +2677.0 2.712871287128713 12.447115853454042 +2778.0 321.7227722772277 71.30127128690422 +2879.0 55.04950495049505 80.90717086284566 +2980.0 1118.5544554455446 498.66584378958237 +3081.0 1075.930693069307 557.8362902614648 +3182.0 8.782178217821782 10.598430670305266 +3283.0 22.455445544554454 8.687079286771445 +3384.0 39.37623762376238 98.50043976380698 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+1712.0 1.0 +1714.0 1.0 +1716.0 1.0 +1718.0 1.0 +1720.0 1.0 +1728.0 2.0 +1734.0 2.0 +1738.0 1.0 +1740.0 2.0 +1744.0 1.0 +1746.0 1.0 +1748.0 2.0 +1754.0 2.0 +1760.0 2.0 +1762.0 1.0 +1764.0 2.0 +1768.0 3.0 +1772.0 2.0 +1777.0 1.0 +1809.0 1.0 +1812.0 1.0 +1832.0 1.0 +1842.0 1.0 +1855.0 1.0 +1878.0 1.0 +1879.0 1.0 +1902.0 1.0 +1909.0 1.0 +1932.0 2.0 +1936.0 1.0 +1952.0 1.0 +1959.0 1.0 +1979.0 1.0 +1996.0 1.0 +1999.0 1.0 +2016.0 1.0 +2019.0 1.0 +2037.0 1.0 +2049.0 1.0 +2065.0 1.0 +2077.0 1.0 +2091.0 1.0 +2092.0 1.0 +2103.0 1.0 +2115.0 1.0 +2133.0 1.0 +2136.0 1.0 +2147.0 1.0 +2151.0 1.0 +2163.0 1.0 +2174.0 1.0 +2192.0 2.0 +2210.0 1.0 +2221.0 1.0 +2224.0 1.0 +2238.0 1.0 +2246.0 1.0 +2264.0 1.0 +2269.0 1.0 +2278.0 1.0 +2282.0 1.0 +2297.0 1.0 +2298.0 1.0 +2304.0 1.0 +2314.0 1.0 +2320.0 1.0 +2324.0 1.0 +2332.0 1.0 +2336.0 1.0 +2342.0 1.0 +2343.0 1.0 +2352.0 1.0 +2366.0 1.0 +2372.0 1.0 +2384.0 2.0 +2388.0 1.0 +2392.0 1.0 +2394.0 1.0 +2401.0 1.0 +2409.0 1.0 +2410.0 1.0 +2416.0 1.0 +2418.0 1.0 +2427.0 1.0 +2430.0 1.0 +2432.0 1.0 +2444.0 1.0 +2447.0 1.0 +2459.0 1.0 +2460.0 1.0 +2473.0 1.0 +2480.0 1.0 +2481.0 1.0 +2489.0 1.0 +2498.0 1.0 +2507.0 1.0 +2509.0 1.0 +2513.0 1.0 +2515.0 1.0 +2517.0 1.0 +2525.0 1.0 +2527.0 3.0 +2528.0 1.0 +2529.0 2.0 +2531.0 1.0 +2532.0 2.0 diff --git a/tests/expected/dna/qualimap/raw_data_qualimapReport/duplication_rate_histogram.txt b/tests/expected/dna/qualimap/raw_data_qualimapReport/duplication_rate_histogram.txt new file mode 100644 index 00000000..67204929 --- /dev/null +++ b/tests/expected/dna/qualimap/raw_data_qualimapReport/duplication_rate_histogram.txt @@ -0,0 +1,51 @@ +#Duplication rate Coverage +1.0 15.0 +2.0 41.0 +3.0 3.0 +4.0 31.0 +5.0 6.0 +6.0 14.0 +7.0 2.0 +8.0 28.0 +9.0 7.0 +10.0 27.0 +11.0 6.0 +12.0 20.0 +13.0 1.0 +14.0 14.0 +15.0 6.0 +16.0 16.0 +17.0 8.0 +18.0 19.0 +19.0 4.0 +20.0 12.0 +21.0 10.0 +22.0 6.0 +23.0 8.0 +24.0 7.0 +25.0 5.0 +26.0 3.0 +27.0 4.0 +28.0 2.0 +29.0 7.0 +30.0 6.0 +31.0 3.0 +32.0 2.0 +33.0 3.0 +34.0 4.0 +35.0 4.0 +36.0 3.0 +37.0 2.0 +38.0 0.0 +39.0 4.0 +40.0 1.0 +41.0 1.0 +42.0 1.0 +43.0 2.0 +44.0 1.0 +45.0 0.0 +46.0 3.0 +47.0 1.0 +48.0 1.0 +49.0 1.0 +50.0 4.0 diff --git a/tests/expected/dna/qualimap/raw_data_qualimapReport/genome_fraction_coverage.txt b/tests/expected/dna/qualimap/raw_data_qualimapReport/genome_fraction_coverage.txt new file mode 100644 index 00000000..e0eade2e --- /dev/null +++ b/tests/expected/dna/qualimap/raw_data_qualimapReport/genome_fraction_coverage.txt @@ -0,0 +1,52 @@ +#Coverage (X) Coverage +1.0 2.9524261893452746 +2.0 2.8524286892827746 +3.0 2.6449338766530843 +4.0 2.564935876603087 +5.0 2.529936751581218 +6.0 2.504937376565593 +7.0 2.5024374390640247 +8.0 2.4724381890452776 +9.0 2.4524386890327747 +10.0 2.429939251518718 +11.0 2.404939876503093 +12.0 2.4024399390015247 +13.0 2.3899402514937123 +14.0 2.387440313992144 +15.0 2.3774405639858998 +16.0 2.3749406264843316 +17.0 2.3524411889702748 +18.0 2.3499412514687066 +19.0 2.3474413139671384 +20.0 2.3424414389640162 +21.0 2.3299417514562037 +22.0 2.2974425639358884 +23.0 2.2749431264218316 +24.0 2.2699432514187095 +25.0 2.2549436264093288 +26.0 2.2524436889077606 +27.0 2.007449813754633 +28.0 2.007449813754633 +29.0 2.007449813754633 +30.0 2.007449813754633 +31.0 2.0024499387515107 +32.0 2.0024499387515107 +33.0 1.9999500012499425 +34.0 1.9999500012499425 +35.0 1.9999500012499425 +36.0 1.9999500012499425 +37.0 1.9974500637483743 +38.0 1.9949501262468061 +39.0 1.9949501262468061 +40.0 1.9949501262468061 +41.0 1.989950251243684 +42.0 1.9874503137421158 +43.0 1.9874503137421158 +44.0 1.9824504387389936 +45.0 1.9824504387389936 +46.0 1.9774505637358715 +47.0 1.9749506262343033 +48.0 1.9749506262343033 +49.0 1.972450688732735 +50.0 1.972450688732735 +51.0 1.9599510012249226 diff --git a/tests/expected/dna/qualimap/raw_data_qualimapReport/homopolymer_indels.txt b/tests/expected/dna/qualimap/raw_data_qualimapReport/homopolymer_indels.txt new file mode 100644 index 00000000..f3769464 --- /dev/null +++ b/tests/expected/dna/qualimap/raw_data_qualimapReport/homopolymer_indels.txt @@ -0,0 +1,7 @@ +#Type of indel Number of indels +polyA 2 +polyC 2 +polyG 1 +polyT 2 +polyN 0 +Non-poly 5 diff --git a/tests/expected/dna/qualimap/raw_data_qualimapReport/insert_size_across_reference.txt b/tests/expected/dna/qualimap/raw_data_qualimapReport/insert_size_across_reference.txt new file mode 100644 index 00000000..b1444ee9 --- /dev/null +++ b/tests/expected/dna/qualimap/raw_data_qualimapReport/insert_size_across_reference.txt @@ -0,0 +1,398 @@ +#Position (bp) insert size +51.0 0.0 +152.0 0.0 +253.0 0.0 +354.0 0.0 +455.0 0.0 +556.0 0.0 +657.0 0.0 +758.0 0.0 +859.0 0.0 +960.0 0.0 +1061.0 0.0 +1162.0 0.0 +1263.0 0.0 +1364.0 0.0 +1465.0 0.0 +1566.0 0.0 +1667.0 0.0 +1768.0 0.0 +1869.0 0.0 +1970.0 108.91666666666667 +2071.0 77.975 +2172.0 0.0 +2273.0 0.0 +2374.0 0.0 +2475.0 0.0 +2576.0 0.0 +2677.0 105.26829268292683 +2778.0 94.76223776223776 +2879.0 162.94767441860466 +2980.0 128.54441260744986 +3081.0 83.66666666666667 +3182.0 118.23076923076923 +3283.0 0.0 +3384.0 144.00704225352112 +3485.0 128.55276381909547 +3586.0 0.0 +3687.0 0.0 +3788.0 0.0 +3889.0 0.0 +3990.0 0.0 +4091.0 0.0 +4192.0 0.0 +4293.0 0.0 +4394.0 0.0 +4495.0 77.80434782608695 +4596.0 0.0 +4697.0 0.0 +4798.0 0.0 +4899.0 0.0 +5000.0 0.0 +5101.0 0.0 +5202.0 0.0 +5303.0 0.0 +5404.0 0.0 +5505.0 0.0 +5606.0 0.0 +5707.0 0.0 +5808.0 0.0 +5909.0 0.0 +6010.0 0.0 +6111.0 0.0 +6212.0 0.0 +6313.0 0.0 +6414.0 0.0 +6515.0 0.0 +6616.0 0.0 +6717.0 0.0 +6818.0 0.0 +6919.0 0.0 +7020.0 0.0 +7121.0 0.0 +7222.0 0.0 +7323.0 0.0 +7424.0 0.0 +7525.0 0.0 +7626.0 0.0 +7727.0 0.0 +7828.0 0.0 +7929.0 0.0 +8030.0 0.0 +8131.0 0.0 +8232.0 0.0 +8333.0 0.0 +8434.0 0.0 +8535.0 0.0 +8636.0 0.0 +8737.0 0.0 +8838.0 0.0 +8939.0 0.0 +9040.0 0.0 +9141.0 0.0 +9242.0 0.0 +9343.0 0.0 +9444.0 0.0 +9545.0 0.0 +9646.0 0.0 +9747.0 0.0 +9848.0 0.0 +9949.0 0.0 +10050.0 0.0 +10151.0 0.0 +10252.0 0.0 +10353.0 0.0 +10454.0 0.0 +10555.0 0.0 +10656.0 0.0 +10757.0 0.0 +10858.0 0.0 +10959.0 0.0 +11060.0 0.0 +11161.0 0.0 +11262.0 0.0 +11363.0 0.0 +11464.0 0.0 +11565.0 0.0 +11666.0 0.0 +11767.0 0.0 +11868.0 0.0 +11969.0 0.0 +12070.0 0.0 +12171.0 0.0 +12272.0 0.0 +12373.0 0.0 +12474.0 0.0 +12575.0 0.0 +12676.0 0.0 +12777.0 0.0 +12878.0 0.0 +12979.0 0.0 +13080.0 0.0 +13181.0 0.0 +13282.0 0.0 +13383.0 0.0 +13484.0 0.0 +13585.0 0.0 +13686.0 0.0 +13787.0 0.0 +13888.0 0.0 +13989.0 0.0 +14090.0 0.0 +14191.0 0.0 +14292.0 0.0 +14393.0 0.0 +14494.0 0.0 +14595.0 0.0 +14696.0 0.0 +14797.0 0.0 +14898.0 0.0 +14999.0 0.0 +15100.0 0.0 +15201.0 0.0 +15302.0 0.0 +15403.0 0.0 +15504.0 0.0 +15605.0 0.0 +15706.0 0.0 +15807.0 0.0 +15908.0 0.0 +16009.0 0.0 +16110.0 0.0 +16211.0 0.0 +16312.0 0.0 +16413.0 0.0 +16514.0 0.0 +16615.0 0.0 +16716.0 0.0 +16817.0 0.0 +16918.0 0.0 +17019.0 0.0 +17120.0 0.0 +17221.0 0.0 +17322.0 0.0 +17423.0 0.0 +17524.0 0.0 +17625.0 0.0 +17726.0 0.0 +17827.0 0.0 +17928.0 0.0 +18029.0 0.0 +18130.0 0.0 +18231.0 0.0 +18332.0 0.0 +18433.0 0.0 +18534.0 0.0 +18635.0 0.0 +18736.0 0.0 +18837.0 0.0 +18938.0 0.0 +19039.0 0.0 +19140.0 0.0 +19241.0 0.0 +19342.0 0.0 +19443.0 0.0 +19544.0 0.0 +19645.0 0.0 +19746.0 0.0 +19847.0 0.0 +19948.0 0.0 +20049.0 0.0 +20150.0 0.0 +20251.0 0.0 +20352.0 0.0 +20453.0 0.0 +20554.0 0.0 +20655.0 0.0 +20756.0 0.0 +20857.0 0.0 +20958.0 0.0 +21059.0 0.0 +21160.0 0.0 +21261.0 0.0 +21362.0 0.0 +21463.0 0.0 +21564.0 0.0 +21665.0 0.0 +21766.0 0.0 +21867.0 0.0 +21968.0 0.0 +22069.0 0.0 +22170.0 0.0 +22271.0 0.0 +22372.0 0.0 +22473.0 0.0 +22574.0 0.0 +22675.0 0.0 +22776.0 0.0 +22877.0 0.0 +22978.0 0.0 +23079.0 0.0 +23180.0 0.0 +23281.0 0.0 +23382.0 0.0 +23483.0 0.0 +23584.0 0.0 +23685.0 0.0 +23786.0 0.0 +23887.0 0.0 +23988.0 0.0 +24089.0 0.0 +24190.0 0.0 +24291.0 0.0 +24392.0 0.0 +24493.0 0.0 +24594.0 0.0 +24695.0 0.0 +24796.0 0.0 +24897.0 0.0 +24998.0 0.0 +25099.0 0.0 +25200.0 0.0 +25301.0 0.0 +25402.0 0.0 +25503.0 0.0 +25604.0 0.0 +25705.0 0.0 +25806.0 0.0 +25907.0 0.0 +26008.0 0.0 +26109.0 0.0 +26210.0 0.0 +26311.0 0.0 +26412.0 0.0 +26513.0 0.0 +26614.0 0.0 +26715.0 0.0 +26816.0 0.0 +26917.0 0.0 +27018.0 0.0 +27119.0 0.0 +27220.0 0.0 +27321.0 0.0 +27422.0 0.0 +27523.0 0.0 +27624.0 0.0 +27725.0 0.0 +27826.0 0.0 +27927.0 0.0 +28028.0 0.0 +28129.0 0.0 +28230.0 0.0 +28331.0 0.0 +28432.0 0.0 +28533.0 0.0 +28634.0 0.0 +28735.0 0.0 +28836.0 0.0 +28937.0 0.0 +29038.0 0.0 +29139.0 0.0 +29240.0 0.0 +29341.0 0.0 +29442.0 0.0 +29543.0 0.0 +29644.0 0.0 +29745.0 0.0 +29846.0 0.0 +29947.0 0.0 +30048.0 0.0 +30149.0 0.0 +30250.0 0.0 +30351.0 0.0 +30452.0 0.0 +30553.0 0.0 +30654.0 0.0 +30755.0 0.0 +30856.0 0.0 +30957.0 0.0 +31058.0 0.0 +31159.0 0.0 +31260.0 0.0 +31361.0 0.0 +31462.0 0.0 +31563.0 0.0 +31664.0 0.0 +31765.0 0.0 +31866.0 0.0 +31967.0 0.0 +32068.0 0.0 +32169.0 0.0 +32270.0 0.0 +32371.0 0.0 +32472.0 0.0 +32573.0 0.0 +32674.0 0.0 +32775.0 0.0 +32876.0 0.0 +32977.0 0.0 +33078.0 0.0 +33179.0 0.0 +33280.0 0.0 +33381.0 0.0 +33482.0 0.0 +33583.0 0.0 +33684.0 0.0 +33785.0 0.0 +33886.0 0.0 +33987.0 0.0 +34088.0 0.0 +34189.0 0.0 +34290.0 0.0 +34391.0 0.0 +34492.0 0.0 +34593.0 0.0 +34694.0 0.0 +34795.0 0.0 +34896.0 0.0 +34997.0 0.0 +35098.0 0.0 +35199.0 0.0 +35300.0 0.0 +35401.0 0.0 +35502.0 0.0 +35603.0 0.0 +35704.0 0.0 +35805.0 0.0 +35906.0 0.0 +36007.0 0.0 +36108.0 0.0 +36209.0 0.0 +36310.0 0.0 +36411.0 0.0 +36512.0 0.0 +36613.0 0.0 +36714.0 0.0 +36815.0 0.0 +36916.0 0.0 +37017.0 0.0 +37118.0 0.0 +37219.0 0.0 +37320.0 0.0 +37421.0 0.0 +37522.0 0.0 +37623.0 0.0 +37724.0 0.0 +37825.0 0.0 +37926.0 0.0 +38027.0 0.0 +38128.0 0.0 +38229.0 0.0 +38330.0 0.0 +38431.0 0.0 +38532.0 0.0 +38633.0 0.0 +38734.0 0.0 +38835.0 0.0 +38936.0 0.0 +39037.0 0.0 +39138.0 0.0 +39239.0 0.0 +39340.0 0.0 +39441.0 0.0 +39542.0 0.0 +39643.0 0.0 +39744.0 0.0 +39845.0 0.0 +39946.0 0.0 +39999.0 0.0 diff --git a/tests/expected/dna/qualimap/raw_data_qualimapReport/insert_size_histogram.txt b/tests/expected/dna/qualimap/raw_data_qualimapReport/insert_size_histogram.txt new file mode 100644 index 00000000..e9b8c479 --- /dev/null +++ b/tests/expected/dna/qualimap/raw_data_qualimapReport/insert_size_histogram.txt @@ -0,0 +1,171 @@ +#Insert size (bp) insert size +32.0 1.0 +41.0 1.0 +49.0 3.0 +51.0 1.0 +52.0 2.0 +54.0 1.0 +58.0 1.0 +59.0 2.0 +60.0 1.0 +61.0 4.0 +62.0 1.0 +63.0 5.0 +65.0 5.0 +66.0 2.0 +67.0 6.0 +68.0 3.0 +69.0 5.0 +70.0 10.0 +71.0 11.0 +72.0 7.0 +73.0 8.0 +74.0 4.0 +75.0 12.0 +76.0 11.0 +77.0 19.0 +78.0 15.0 +79.0 13.0 +80.0 17.0 +81.0 24.0 +82.0 18.0 +83.0 19.0 +84.0 25.0 +85.0 15.0 +86.0 24.0 +87.0 30.0 +88.0 29.0 +89.0 21.0 +90.0 16.0 +91.0 24.0 +92.0 30.0 +93.0 23.0 +94.0 21.0 +95.0 43.0 +96.0 54.0 +97.0 34.0 +98.0 28.0 +99.0 24.0 +100.0 44.0 +101.0 24.0 +102.0 27.0 +103.0 22.0 +104.0 33.0 +105.0 26.0 +106.0 28.0 +107.0 35.0 +108.0 26.0 +109.0 24.0 +110.0 34.0 +111.0 29.0 +112.0 22.0 +113.0 36.0 +114.0 30.0 +115.0 49.0 +116.0 36.0 +117.0 33.0 +118.0 34.0 +119.0 38.0 +120.0 14.0 +121.0 39.0 +122.0 30.0 +123.0 28.0 +124.0 36.0 +125.0 36.0 +126.0 25.0 +127.0 32.0 +128.0 31.0 +129.0 28.0 +130.0 39.0 +131.0 45.0 +132.0 25.0 +133.0 18.0 +134.0 25.0 +135.0 31.0 +136.0 30.0 +137.0 29.0 +138.0 34.0 +139.0 32.0 +140.0 28.0 +141.0 41.0 +142.0 27.0 +143.0 23.0 +144.0 26.0 +145.0 31.0 +146.0 21.0 +147.0 29.0 +148.0 18.0 +149.0 17.0 +150.0 19.0 +151.0 20.0 +152.0 28.0 +153.0 28.0 +154.0 18.0 +155.0 23.0 +156.0 20.0 +157.0 29.0 +158.0 16.0 +159.0 15.0 +160.0 14.0 +161.0 18.0 +162.0 19.0 +163.0 15.0 +164.0 9.0 +165.0 11.0 +166.0 21.0 +167.0 9.0 +168.0 17.0 +169.0 16.0 +170.0 17.0 +171.0 13.0 +172.0 14.0 +173.0 21.0 +174.0 9.0 +175.0 9.0 +176.0 7.0 +177.0 9.0 +178.0 9.0 +179.0 9.0 +180.0 2.0 +181.0 8.0 +182.0 8.0 +183.0 3.0 +184.0 12.0 +185.0 10.0 +186.0 5.0 +187.0 7.0 +188.0 1.0 +189.0 5.0 +190.0 8.0 +191.0 10.0 +192.0 8.0 +193.0 2.0 +194.0 6.0 +195.0 1.0 +196.0 2.0 +197.0 3.0 +198.0 2.0 +199.0 4.0 +200.0 7.0 +201.0 2.0 +202.0 6.0 +203.0 4.0 +204.0 4.0 +205.0 2.0 +206.0 4.0 +207.0 4.0 +209.0 1.0 +210.0 1.0 +212.0 2.0 +213.0 4.0 +214.0 3.0 +215.0 1.0 +216.0 4.0 +218.0 2.0 +220.0 1.0 +221.0 2.0 +223.0 1.0 +224.0 1.0 +231.0 1.0 +236.0 1.0 +239.0 1.0 diff --git a/tests/expected/dna/qualimap/raw_data_qualimapReport/mapped_reads_clipping_profile.txt b/tests/expected/dna/qualimap/raw_data_qualimapReport/mapped_reads_clipping_profile.txt new file mode 100644 index 00000000..27a26cd9 --- /dev/null +++ b/tests/expected/dna/qualimap/raw_data_qualimapReport/mapped_reads_clipping_profile.txt @@ -0,0 +1,144 @@ +#Read position (bp) Clipping profile +0.0 1.8539976825028968 +1.0 1.8539976825028968 +2.0 1.8539976825028968 +3.0 1.738122827346466 +4.0 1.6222479721900347 +5.0 1.5063731170336037 +6.0 1.5063731170336037 +7.0 1.3904982618771726 +8.0 1.3904982618771726 +9.0 1.3904982618771726 +10.0 1.3904982618771726 +11.0 1.3904982618771726 +12.0 1.3904982618771726 +13.0 1.1587485515643106 +14.0 1.1587485515643106 +15.0 1.1587485515643106 +16.0 1.1587485515643106 +17.0 1.1587485515643106 +18.0 1.1587485515643106 +19.0 1.1587485515643106 +20.0 1.1587485515643106 +21.0 1.1587485515643106 +22.0 1.1587485515643106 +23.0 1.1587485515643106 +24.0 1.1587485515643106 +25.0 1.1587485515643106 +26.0 1.0428736964078795 +27.0 1.0428736964078795 +28.0 1.0428736964078795 +29.0 1.0428736964078795 +30.0 1.0428736964078795 +31.0 1.0428736964078795 +32.0 0.9269988412514484 +33.0 0.9269988412514484 +34.0 0.9269988412514484 +35.0 0.9269988412514484 +36.0 0.9269988412514484 +37.0 1.1587485515643106 +38.0 1.1587485515643106 +39.0 1.1587485515643106 +40.0 1.1587485515643106 +41.0 1.0428736964078795 +42.0 1.0428736964078795 +43.0 1.0428736964078795 +44.0 1.0428736964078795 +45.0 1.0428736964078795 +46.0 1.0428736964078795 +47.0 1.0428736964078795 +48.0 0.9269988412514484 +49.0 0.9269988412514484 +50.0 0.9269988412514484 +51.0 0.9269988412514484 +52.0 0.9269988412514484 +53.0 0.9269988412514484 +54.0 0.8111239860950173 +55.0 0.8111239860950173 +56.0 0.8111239860950173 +57.0 0.8111239860950173 +58.0 0.8111239860950173 +59.0 0.8111239860950173 +60.0 0.8111239860950173 +61.0 0.8111239860950173 +62.0 0.8111239860950173 +63.0 0.8111239860950173 +64.0 0.6952491309385863 +65.0 0.6952491309385863 +66.0 0.6952491309385863 +67.0 0.6952491309385863 +68.0 0.5793742757821553 +69.0 0.5793742757821553 +70.0 0.4634994206257242 +71.0 0.5793742757821553 +72.0 0.5793742757821553 +73.0 0.6952491309385863 +74.0 0.6952491309385863 +75.0 0.6952491309385863 +76.0 0.5793742757821553 +77.0 0.5793742757821553 +78.0 0.5793742757821553 +79.0 0.5793742757821553 +80.0 0.5793742757821553 +81.0 0.5793742757821553 +82.0 0.4634994206257242 +83.0 0.4634994206257242 +84.0 0.4634994206257242 +85.0 0.4634994206257242 +86.0 0.4634994206257242 +87.0 0.4634994206257242 +88.0 0.4634994206257242 +89.0 0.4634994206257242 +90.0 0.4634994206257242 +91.0 0.5793742757821553 +92.0 0.34762456546929316 +93.0 0.34762456546929316 +94.0 0.34762456546929316 +95.0 0.34762456546929316 +96.0 0.34762456546929316 +97.0 0.34762456546929316 +98.0 0.2317497103128621 +99.0 0.2317497103128621 +100.0 0.2317497103128621 +101.0 0.2317497103128621 +102.0 0.2317497103128621 +103.0 0.2317497103128621 +104.0 0.11587485515643105 +105.0 0.11587485515643105 +106.0 0.11587485515643105 +107.0 0.0 +108.0 0.0 +109.0 0.0 +110.0 0.0 +111.0 0.0 +112.0 0.0 +113.0 0.0 +114.0 0.0 +115.0 0.11587485515643105 +116.0 0.11587485515643105 +117.0 0.11587485515643105 +118.0 0.11587485515643105 +119.0 0.11587485515643105 +120.0 0.0 +121.0 0.0 +122.0 0.0 +123.0 0.0 +124.0 0.0 +125.0 0.11587485515643105 +126.0 0.2317497103128621 +127.0 0.4634994206257242 +128.0 0.34762456546929316 +129.0 0.34762456546929316 +130.0 0.4634994206257242 +131.0 0.4634994206257242 +132.0 0.4634994206257242 +133.0 0.4634994206257242 +134.0 0.4634994206257242 +135.0 0.4634994206257242 +136.0 0.4634994206257242 +137.0 0.4634994206257242 +138.0 0.34762456546929316 +139.0 0.34762456546929316 +140.0 0.5793742757821553 +141.0 0.6952491309385863 +142.0 0.6952491309385863 diff --git a/tests/expected/dna/qualimap/raw_data_qualimapReport/mapped_reads_gc-content_distribution.txt b/tests/expected/dna/qualimap/raw_data_qualimapReport/mapped_reads_gc-content_distribution.txt new file mode 100644 index 00000000..ffdab0f1 --- /dev/null +++ b/tests/expected/dna/qualimap/raw_data_qualimapReport/mapped_reads_gc-content_distribution.txt @@ -0,0 +1,101 @@ +#GC Content (%) Sample +1.0 0.0 +2.0 0.0 +3.0 0.0 +4.0 0.0 +5.0 0.0 +6.0 0.0 +7.0 0.0 +8.0 0.0 +9.0 0.0 +10.0 0.0 +11.0 0.0 +12.0 0.0 +13.0 0.0 +14.0 0.0 +15.0 0.0 +16.0 0.0 +17.0 0.0 +18.0 0.0014727540500736377 +19.0 0.0 +20.0 0.0 +21.0 0.0 +22.0 0.0 +23.0 0.010309278350515464 +24.0 0.025036818851251842 +25.0 0.042709867452135494 +26.0 0.05301914580265096 +27.0 0.050073637702503684 +28.0 0.09131075110456553 +29.0 0.11192930780559647 +30.0 0.13991163475699558 +31.0 0.10751104565537553 +32.0 0.06774668630338734 +33.0 0.04860088365243005 +34.0 0.022091310751104563 +35.0 0.030927835051546393 +36.0 0.05891016200294552 +37.0 0.05301914580265096 +38.0 0.04860088365243005 +39.0 0.014727540500736377 +40.0 0.0029455081001472753 +41.0 0.0 +42.0 0.004418262150220913 +43.0 0.0014727540500736377 +44.0 0.0014727540500736377 +45.0 0.004418262150220913 +46.0 0.0029455081001472753 +47.0 0.004418262150220913 +48.0 0.0 +49.0 0.0 +50.0 0.0 +51.0 0.0 +52.0 0.0 +53.0 0.0 +54.0 0.0 +55.0 0.0 +56.0 0.0 +57.0 0.0 +58.0 0.0 +59.0 0.0 +60.0 0.0 +61.0 0.0 +62.0 0.0 +63.0 0.0 +64.0 0.0 +65.0 0.0 +66.0 0.0 +67.0 0.0 +68.0 0.0 +69.0 0.0 +70.0 0.0 +71.0 0.0 +72.0 0.0 +73.0 0.0 +74.0 0.0 +75.0 0.0 +76.0 0.0 +77.0 0.0 +78.0 0.0 +79.0 0.0 +80.0 0.0 +81.0 0.0 +82.0 0.0 +83.0 0.0 +84.0 0.0 +85.0 0.0 +86.0 0.0 +87.0 0.0 +88.0 0.0 +89.0 0.0 +90.0 0.0 +91.0 0.0 +92.0 0.0 +93.0 0.0 +94.0 0.0 +95.0 0.0 +96.0 0.0 +97.0 0.0 +98.0 0.0 +99.0 0.0 +100.0 0.0 diff --git a/tests/expected/dna/qualimap/raw_data_qualimapReport/mapped_reads_nucleotide_content.txt b/tests/expected/dna/qualimap/raw_data_qualimapReport/mapped_reads_nucleotide_content.txt new file mode 100644 index 00000000..23604a2d --- /dev/null +++ b/tests/expected/dna/qualimap/raw_data_qualimapReport/mapped_reads_nucleotide_content.txt @@ -0,0 +1,144 @@ +# Position (bp) A C G T N +0.0 36.575391180654336 12.820056899004268 18.509957325746797 32.059032716927454 0.03556187766714083 +1.0 36.21977240398293 13.78022759601707 19.221194879089616 30.743243243243246 0.03556187766714083 +2.0 37.03769559032717 13.264580369843529 17.798719772403985 31.89900426742532 0.0 +3.0 36.92444444444444 13.137777777777778 17.262222222222224 32.65777777777778 0.017777777777777778 +4.0 38.019907571987204 12.371134020618557 17.774617845716318 31.834340561677926 0.0 +5.0 37.79989337124578 12.262306735382975 17.807001954860493 32.113026479473966 0.01777145903678692 +6.0 36.200462057934956 12.937622178780877 18.269059889816955 32.592855873467215 0.0 +7.0 35.69651741293532 13.592750533049042 17.608386638237384 33.04904051172708 0.053304904051172705 +8.0 36.247334754797436 12.082444918265814 17.928216062544422 33.724235963041934 0.017768301350390904 +9.0 37.65103056147832 12.064676616915424 18.176972281449892 32.08955223880597 0.017768301350390904 +10.0 36.49609097370291 12.722103766879886 17.235252309879176 33.546552949538025 0.0 +11.0 36.44278606965174 12.686567164179104 17.093105899076047 33.70646766169154 0.07107320540156362 +12.0 35.18123667377399 14.339019189765459 17.555081734186214 32.88912579957356 0.03553660270078181 +13.0 35.861456483126105 12.966252220248666 17.779751332149203 33.37477797513321 0.017761989342806393 +14.0 36.802841918294845 13.978685612788633 18.02841918294849 31.119005328596806 0.07104795737122557 +15.0 37.24689165186501 13.001776198934282 18.17051509769094 31.49200710479574 0.08880994671403197 +16.0 37.47779751332149 13.037300177619892 17.460035523978686 31.97158081705151 0.05328596802841918 +17.0 35.09769094138544 14.103019538188278 17.72646536412078 33.01953818827709 0.05328596802841918 +18.0 35.150976909413856 14.777975133214921 16.44760213143872 33.587921847246896 0.035523978685612786 +19.0 34.04973357015986 13.321492007104796 15.86145648312611 36.731793960923625 0.035523978685612786 +20.0 35.13321492007105 12.735346358792185 17.140319715808168 34.97335701598579 0.017761989342806393 +21.0 34.795737122557725 13.0550621669627 17.08703374777975 35.0088809946714 0.05328596802841918 +22.0 34.209591474245116 13.232682060390763 16.838365896980463 35.66607460035524 0.05328596802841918 +23.0 34.08525754884547 13.161634103019537 17.708703374777976 35.0088809946714 0.035523978685612786 +24.0 34.795737122557725 12.984014209591473 16.518650088809945 35.61278863232682 0.08880994671403197 +25.0 34.849023090586144 13.534635879218474 17.05150976909414 34.52930728241563 0.035523978685612786 +26.0 34.36334576451785 13.549991120582488 17.474693660095898 34.5586929497425 0.05327650506126798 +27.0 33.24453915823122 13.461196945480378 17.95418220564731 35.25128751553898 0.0887941751021133 +28.0 34.203516249334044 14.473450541644468 17.989699875688157 33.31557449831291 0.01775883502042266 +29.0 34.469898774640384 13.514473450541646 16.78209909429941 35.21576984549814 0.01775883502042266 +30.0 35.168738898756665 13.765541740674955 17.67317939609236 33.30373001776199 0.08880994671403197 +31.0 32.770870337477795 14.174067495559504 16.660746003552397 36.34103019538188 0.05328596802841918 +32.0 34.949387320191796 13.052743740010655 16.480198898952228 35.39335819570236 0.12431184514295864 +33.0 33.83658969804618 13.570159857904084 16.607460035523978 35.89698046181172 0.08880994671403197 +34.0 33.37477797513321 14.08525754884547 16.69626998223801 35.772646536412076 0.07104795737122557 +35.0 32.45115452930728 13.623445825932503 17.33570159857904 36.53641207815275 0.05328596802841918 +36.0 34.280639431616336 12.060390763765541 16.571936056838364 37.03374777975133 0.05328596802841918 +37.0 34.02629708599858 13.521677327647478 18.105899076048328 34.310589907604836 0.03553660270078181 +38.0 33.972992181947404 13.450604122245913 17.35963041933191 35.127931769722814 0.08884150675195451 +39.0 33.546552949538025 14.978678038379531 16.91542288557214 34.50604122245913 0.053304904051172705 +40.0 35.199004975124375 14.55223880597015 15.884861407249467 34.310589907604836 0.053304904051172705 +41.0 33.91968727789623 13.699360341151387 16.417910447761194 35.90973702914002 0.053304904051172705 +42.0 34.00852878464819 13.983653162757639 16.080312722103766 35.891968727789624 0.03553660270078181 +43.0 34.09737029140014 14.232409381663114 15.689410092395168 35.87420042643924 0.10660980810234541 +44.0 35.09239516702203 14.800995024875622 15.15636105188344 34.86140724946695 0.08884150675195451 +45.0 34.06788697352053 15.141283099342456 15.265683312599965 35.489603696463476 0.03554291807357384 +46.0 34.3700017771459 14.554824951128486 15.354540607783898 35.614003909720985 0.10662875422072153 +47.0 34.97423138439666 14.9457970499378 15.016882886084948 34.97423138439666 0.08885729518393459 +48.0 35.60767590618337 14.57000710732054 15.618336886993603 34.1684434968017 0.03553660270078181 +49.0 36.374955531839205 14.585556741373177 15.91960156527926 33.04873710423337 0.0711490572749911 +50.0 34.293845606545716 15.635005336179294 16.22198505869797 33.76022767698328 0.08893632159373889 +51.0 35.355871886121 16.601423487544483 15.640569395017796 32.36654804270462 0.03558718861209965 +52.0 34.98931623931624 15.206552706552706 17.11182336182336 32.67450142450142 0.017806267806267807 +53.0 35.57692307692308 15.918803418803417 15.776353276353278 32.65669515669516 0.07122507122507123 +54.0 33.8913624220837 16.812110418521815 17.008014247551202 32.181656277827244 0.10685663401602849 +55.0 36.02849510240427 15.85040071237756 16.046304541406943 32.00356188780054 0.07123775601068566 +56.0 35.636687444345505 15.45859305431879 15.796972395369547 33.018699910952805 0.08904719501335707 +57.0 36.349065004452356 14.56812110418522 14.95992876224399 33.998219056099735 0.1246660730186999 +58.0 34.90112239444147 16.443969356850168 14.840548726171388 33.74309638339569 0.07126313914127917 +59.0 34.694241397753615 16.616152611873776 15.207701907648422 33.41059012301658 0.07131395970761277 +60.0 34.706616729088644 16.800428036383092 15.337970394150169 33.083645443196005 0.07133939718209381 +61.0 36.470798356849436 15.395606358278263 16.038578317556706 32.04143597070905 0.05358099660653688 +62.0 35.644095050920136 14.632839020904056 15.436841164909772 34.16115776308737 0.12506700017866715 +63.0 33.56005011634151 15.285484159656345 16.144621442634687 34.938249507785926 0.07159477358152855 +64.0 35.36148890479599 15.085898353614887 16.35647816750179 33.08876163206872 0.10737294201861132 +65.0 34.38508425959125 15.561133022588741 16.20652563642883 33.811401936177845 0.035855145213338116 +66.0 36.54467168998924 16.397560100466453 14.872622891998565 32.131324004305704 0.05382131324004305 +67.0 35.2391226177634 16.50485436893204 15.24631427544049 32.991729593671344 0.017979144192736426 +68.0 35.871130309575236 16.180705543556513 15.712742980561556 32.19942404607632 0.03599712023038157 +69.0 35.7194374323837 15.380454381536243 16.300036062026685 32.58204111071043 0.018031013342949875 +70.0 35.10041613895423 15.469513298353538 16.55509317893975 32.85688438574272 0.01809299800977022 +71.0 36.26453488372093 16.333575581395348 15.715843023255813 31.64970930232558 0.036337209302325583 +72.0 36.48451730418943 15.282331511839708 15.100182149362476 33.114754098360656 0.018214936247723135 +73.0 36.01315549059017 15.055728119861136 15.073999634569708 33.83884524027042 0.01827151470856934 +74.0 35.02287282708142 14.217749313815187 15.279048490393413 35.42543458371455 0.05489478499542544 +75.0 33.651902223855906 15.585370336335233 14.648042639220732 36.05954787722845 0.05513692335967653 +76.0 33.94833948339483 15.77490774907749 15.33210332103321 34.92619926199262 0.01845018450184502 +77.0 34.46674098848012 16.257896692679303 15.663322185061315 33.537718320327016 0.07432181345224824 +78.0 35.48206278026906 15.041106128550075 14.31240657698057 35.1270553064275 0.03736920777279522 +79.0 34.209538114908 16.522718738265116 13.875328576793091 35.317311303041684 0.07510326699211416 +80.0 34.03250188964474 16.93121693121693 14.13454270597128 34.863945578231295 0.03779289493575208 +81.0 33.44774980930587 16.113653699466056 15.408085430968727 35.01144164759725 0.01906941266209001 +82.0 33.36532923785755 16.37550393549626 13.419082357458246 36.80168938375888 0.03839508542906508 +83.0 34.480758073873524 16.418487719976792 12.531425256236705 36.54999033069039 0.019338619222587505 +84.0 35.344659246240965 15.52431165787932 12.90763522749463 36.1452841241945 0.07810974419058778 +85.0 35.867216656845414 16.519347868788056 13.94617953250835 33.64761343547437 0.019642506383814574 +86.0 36.149117588736864 16.141185802101923 12.71068808249058 34.97917906008328 0.019829466587348802 +87.0 34.85851896447923 15.79369857515553 13.064419024683927 36.283363435681316 0.0 +88.0 36.44670050761422 15.472081218274111 12.101522842639593 35.93908629441624 0.04060913705583756 +89.0 35.65431087446242 14.396887159533073 12.840466926070038 37.10833503993447 0.0 +90.0 34.50834879406308 16.14100185528757 13.749742321170894 35.600907029478456 0.0 +91.0 36.3579604578564 15.691987513007282 12.11238293444329 35.796045785639954 0.04162330905306972 +92.0 37.0339161575732 15.820518222034968 13.313671792711185 33.831893827680645 0.0 +93.0 35.99234205488194 16.507126143373753 13.316315677515423 34.1629440544565 0.021272069772388852 +94.0 35.142673246084534 15.85496674533362 14.353143102338553 34.62776228277194 0.021454623471358077 +95.0 36.29807692307692 15.887237762237763 13.439685314685315 34.375 0.0 +96.0 34.355416293643685 16.02506714413608 15.085049239033124 34.51208594449418 0.022381378692927483 +97.0 34.97727272727273 16.545454545454547 14.295454545454545 34.18181818181818 0.0 +98.0 35.627157652474104 17.12313003452244 13.73993095512083 33.50978135788262 0.0 +99.0 35.070979753316266 17.989294856876892 12.357458692110775 34.58226669769607 0.0 +100.0 36.08933238298883 16.39344262295082 12.21192682347351 35.28153955808981 0.023758612497030172 +101.0 35.90483056957462 15.957702475366498 12.32876712328767 35.80869983177121 0.0 +102.0 35.64645726807889 16.43535427319211 13.562210859508156 34.35597759922084 0.0 +103.0 36.697021904996305 15.850356879153335 12.552301255230125 34.90031996062023 0.0 +104.0 35.867933966983486 16.983491745872936 11.85592796398199 35.29264632316158 0.0 +105.0 37.78509883426254 16.016218955904712 12.316269640141916 33.88241256969083 0.0 +106.0 34.70437017994858 16.73521850899743 12.313624678663238 36.221079691516714 0.025706940874035987 +107.0 33.58638743455498 16.910994764397905 14.319371727748692 35.13089005235602 0.052356020942408384 +108.0 33.70488322717622 15.233545647558385 13.641188959660298 37.39384288747346 0.02653927813163482 +109.0 33.70967741935484 16.93548387096774 13.064516129032258 36.26344086021505 0.026881720430107527 +110.0 33.433734939759034 18.209200438116103 12.568455640744796 35.788608981380065 0.0 +111.0 33.36115748469672 17.50139120756817 13.439065108514189 35.67056204785754 0.02782415136338342 +112.0 33.183098591549296 16.788732394366196 13.521126760563378 36.50704225352113 0.0 +113.0 31.6561242093157 17.30879815986199 14.40483036227717 36.63024726854514 0.0 +114.0 31.71445289643066 18.461088355763604 14.160327677004094 35.63487419543593 0.029256875365710942 +115.0 33.373493975903614 16.355421686746986 13.283132530120481 36.95783132530121 0.030120481927710847 +116.0 31.434729064039406 16.163793103448278 13.023399014778326 39.37807881773399 0.0 +117.0 34.41231929604023 16.939032055311127 12.664990571967316 35.98365807668134 0.0 +118.0 32.85163776493256 19.20359666024406 12.363519588953114 35.51701991008349 0.06422607578676942 +119.0 33.20171108917407 17.17670286278381 13.063507732806844 36.55807831523528 0.0 +120.0 32.38126868150116 18.631683825971436 13.218199933576885 35.76884755895052 0.0 +121.0 31.67405386975793 18.888510057961135 13.092396863279918 36.34503920900102 0.0 +122.0 30.838844413505047 18.58684302123216 12.39122868082144 38.18308388444135 0.0 +123.0 30.45793397231097 19.772807951721692 12.70855520056798 37.06070287539936 0.0 +124.0 32.27686703096539 18.907103825136613 13.07832422586521 35.73770491803279 0.0 +125.0 31.773952095808383 18.07634730538922 13.09880239520958 37.050898203592816 0.0 +126.0 35.673407096528045 16.673025562762305 11.942006867607784 35.673407096528045 0.03815337657382679 +127.0 31.75596402033633 17.52053187328901 14.704732107938993 35.97966366836136 0.03910833007430582 +128.0 32.61217948717949 17.067307692307693 12.660256410256409 37.66025641025641 0.0 +129.0 33.77049180327869 17.745901639344265 11.721311475409836 36.721311475409834 0.040983606557377046 +130.0 34.36440677966102 17.71186440677966 12.711864406779661 35.21186440677966 0.0 +131.0 34.008810572687224 18.590308370044053 12.334801762114537 35.06607929515418 0.0 +132.0 34.27927927927928 15.855855855855856 13.558558558558559 36.306306306306304 0.0 +133.0 30.691708657810352 19.606046724690792 14.567109482363719 35.13513513513514 0.0 +134.0 32.114392873886544 18.612283169245195 13.783403656821378 35.48992030004688 0.0 +135.0 30.90294543698696 18.976339932399807 12.988894253983583 37.13182037662965 0.0 +136.0 30.432620586772753 17.951268025857782 14.172053704624565 37.444057682744905 0.0 +137.0 30.824372759856633 20.225294418842807 13.312852022529443 35.63748079877112 0.0 +138.0 32.608695652173914 18.928950159066808 12.672322375397668 35.79003181336161 0.0 +139.0 33.69923161361142 18.551042810098792 11.85510428100988 35.89462129527991 0.0 +140.0 31.916099773242628 16.383219954648524 13.662131519274375 38.038548752834465 0.0 +141.0 32.42117787031529 16.299821534800714 13.741820345032718 37.537180249851275 0.0 +142.0 32.32944068838353 16.59496004917025 10.633066994468347 40.073755377996314 0.36877688998156116 diff --git a/tests/expected/dna/qualimap/raw_data_qualimapReport/mapping_quality_across_reference.txt b/tests/expected/dna/qualimap/raw_data_qualimapReport/mapping_quality_across_reference.txt new file mode 100644 index 00000000..502c4d0c --- /dev/null +++ b/tests/expected/dna/qualimap/raw_data_qualimapReport/mapping_quality_across_reference.txt @@ -0,0 +1,398 @@ +#Position (bp) mapping quality +51.0 0.0 +152.0 0.0 +253.0 0.0 +354.0 0.0 +455.0 0.0 +556.0 0.0 +657.0 0.0 +758.0 0.0 +859.0 0.0 +960.0 0.0 +1061.0 0.0 +1162.0 0.0 +1263.0 0.0 +1364.0 0.0 +1465.0 0.0 +1566.0 0.0 +1667.0 0.0 +1768.0 0.0 +1869.0 0.0 +1970.0 59.99982589013668 +2071.0 59.98494448073155 +2172.0 60.0 +2273.0 0.0 +2374.0 0.0 +2475.0 0.0 +2576.0 0.0 +2677.0 60.0 +2778.0 60.0 +2879.0 60.0 +2980.0 59.99787561739869 +3081.0 60.0 +3182.0 60.0 +3283.0 59.94708994708995 +3384.0 59.94719637918028 +3485.0 59.992187003747695 +3586.0 59.99595381827932 +3687.0 60.0 +3788.0 0.0 +3889.0 0.0 +3990.0 0.0 +4091.0 0.0 +4192.0 0.0 +4293.0 0.0 +4394.0 0.0 +4495.0 60.0 +4596.0 60.0 +4697.0 0.0 +4798.0 0.0 +4899.0 0.0 +5000.0 0.0 +5101.0 0.0 +5202.0 0.0 +5303.0 0.0 +5404.0 0.0 +5505.0 0.0 +5606.0 0.0 +5707.0 0.0 +5808.0 0.0 +5909.0 0.0 +6010.0 0.0 +6111.0 0.0 +6212.0 0.0 +6313.0 0.0 +6414.0 0.0 +6515.0 0.0 +6616.0 0.0 +6717.0 0.0 +6818.0 0.0 +6919.0 0.0 +7020.0 0.0 +7121.0 0.0 +7222.0 0.0 +7323.0 0.0 +7424.0 0.0 +7525.0 0.0 +7626.0 0.0 +7727.0 0.0 +7828.0 0.0 +7929.0 0.0 +8030.0 0.0 +8131.0 0.0 +8232.0 0.0 +8333.0 0.0 +8434.0 0.0 +8535.0 0.0 +8636.0 0.0 +8737.0 0.0 +8838.0 0.0 +8939.0 0.0 +9040.0 0.0 +9141.0 0.0 +9242.0 0.0 +9343.0 0.0 +9444.0 0.0 +9545.0 0.0 +9646.0 0.0 +9747.0 0.0 +9848.0 0.0 +9949.0 0.0 +10050.0 0.0 +10151.0 0.0 +10252.0 0.0 +10353.0 0.0 +10454.0 0.0 +10555.0 0.0 +10656.0 0.0 +10757.0 0.0 +10858.0 0.0 +10959.0 0.0 +11060.0 0.0 +11161.0 0.0 +11262.0 0.0 +11363.0 0.0 +11464.0 0.0 +11565.0 0.0 +11666.0 0.0 +11767.0 0.0 +11868.0 0.0 +11969.0 0.0 +12070.0 0.0 +12171.0 0.0 +12272.0 0.0 +12373.0 0.0 +12474.0 0.0 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+35906.0 0.0 +36007.0 0.0 +36108.0 0.0 +36209.0 0.0 +36310.0 0.0 +36411.0 0.0 +36512.0 0.0 +36613.0 0.0 +36714.0 0.0 +36815.0 0.0 +36916.0 0.0 +37017.0 0.0 +37118.0 0.0 +37219.0 0.0 +37320.0 0.0 +37421.0 0.0 +37522.0 0.0 +37623.0 0.0 +37724.0 0.0 +37825.0 0.0 +37926.0 0.0 +38027.0 0.0 +38128.0 0.0 +38229.0 0.0 +38330.0 0.0 +38431.0 0.0 +38532.0 0.0 +38633.0 0.0 +38734.0 0.0 +38835.0 0.0 +38936.0 0.0 +39037.0 0.0 +39138.0 0.0 +39239.0 0.0 +39340.0 0.0 +39441.0 0.0 +39542.0 0.0 +39643.0 0.0 +39744.0 0.0 +39845.0 0.0 +39946.0 0.0 +39999.0 0.0 diff --git a/tests/expected/dna/qualimap/raw_data_qualimapReport/mapping_quality_histogram.txt b/tests/expected/dna/qualimap/raw_data_qualimapReport/mapping_quality_histogram.txt new file mode 100644 index 00000000..30307309 --- /dev/null +++ b/tests/expected/dna/qualimap/raw_data_qualimapReport/mapping_quality_histogram.txt @@ -0,0 +1,3 @@ +#Mapping quality mapping quality +59.0 248.0 +60.0 933.0 diff --git a/tests/expected/dna/test.flagstat.txt b/tests/expected/dna/test.flagstat.txt new file mode 100644 index 00000000..1d2e97e5 --- /dev/null +++ b/tests/expected/dna/test.flagstat.txt @@ -0,0 +1,16 @@ +5644 + 0 in total (QC-passed reads + QC-failed reads) +5642 + 0 primary +2 + 0 secondary +0 + 0 supplementary +1656 + 0 duplicates +1656 + 0 primary duplicates +5642 + 0 mapped (99.96% : N/A) +5640 + 0 primary mapped (99.96% : N/A) +5642 + 0 paired in sequencing +2821 + 0 read1 +2821 + 0 read2 +5638 + 0 properly paired (99.93% : N/A) +5640 + 0 with itself and mate mapped +0 + 0 singletons (0.00% : N/A) +0 + 0 with mate mapped to a different chr +0 + 0 with mate mapped to a different chr (mapQ>=5) diff --git a/tests/expected/dna/test.gc_bias.detail_metrics.txt b/tests/expected/dna/test.gc_bias.detail_metrics.txt new file mode 100644 index 00000000..e6da30c8 --- /dev/null +++ b/tests/expected/dna/test.gc_bias.detail_metrics.txt @@ -0,0 +1,105 @@ +## METRICS CLASS picard.analysis.GcBiasDetailMetrics +ACCUMULATION_LEVEL READS_USED GC WINDOWS READ_STARTS MEAN_BASE_QUALITY NORMALIZED_COVERAGE ERROR_BAR_WIDTH SAMPLE LIBRARY READ_GROUP +All Reads ALL 0 0 0 0 0 0 +All Reads ALL 1 0 0 0 0 0 +All Reads ALL 2 0 0 0 0 0 +All Reads ALL 3 0 0 0 0 0 +All Reads ALL 4 0 0 0 0 0 +All Reads ALL 5 0 0 0 0 0 +All Reads ALL 6 0 0 0 0 0 +All Reads ALL 7 0 0 0 0 0 +All Reads ALL 8 0 0 0 0 0 +All Reads ALL 9 0 0 0 0 0 +All Reads ALL 10 0 0 0 0 0 +All Reads ALL 11 0 0 0 0 0 +All Reads ALL 12 0 0 0 0 0 +All Reads ALL 13 0 0 0 0 0 +All Reads ALL 14 6 0 0 0 0 +All Reads ALL 15 8 0 0 0 0 +All Reads ALL 16 10 0 0 0 0 +All Reads ALL 17 38 0 0 0 0 +All Reads ALL 18 69 0 0 0 0 +All Reads ALL 19 106 73 27 4.870312 0.570027 +All Reads ALL 20 157 80 26 3.603547 0.402889 +All Reads ALL 21 223 179 27 5.676596 0.424289 +All Reads ALL 22 319 71 27 1.57401 0.186801 +All Reads ALL 23 397 43 30 0.765981 0.116811 +All Reads ALL 24 429 96 28 1.582537 0.161517 +All Reads ALL 25 493 76 31 1.090201 0.125055 +All Reads ALL 26 741 85 25 0.811224 0.08799 +All Reads ALL 27 1014 243 26 1.69476 0.108719 +All Reads ALL 28 1015 485 28 3.379213 0.153442 +All Reads ALL 29 1079 841 26 5.512065 0.190071 +All Reads ALL 30 1048 766 27 5.169009 0.186764 +All Reads ALL 31 1142 486 26 3.009608 0.136519 +All Reads ALL 32 1223 353 26 2.041212 0.108643 +All Reads ALL 33 1119 339 28 2.142444 0.116362 +All Reads ALL 34 1281 327 28 1.805255 0.099831 +All Reads ALL 35 1194 239 30 1.415577 0.091566 +All Reads ALL 36 1191 376 28 2.232626 0.115139 +All Reads ALL 37 1171 85 24 0.513336 0.055679 +All Reads ALL 38 1135 87 28 0.54208 0.058117 +All Reads ALL 39 1140 95 26 0.58933 0.060464 +All Reads ALL 40 1166 39 26 0.236541 0.037877 +All Reads ALL 41 1070 72 29 0.47587 0.056082 +All Reads ALL 42 973 28 20 0.20351 0.03846 +All Reads ALL 43 1040 11 0 0.0748 0.022553 +All Reads ALL 44 1078 17 25 0.111524 0.027049 +All Reads ALL 45 924 23 21 0.176034 0.036706 +All Reads ALL 46 1022 8 0 0.055358 0.019572 +All Reads ALL 47 932 16 31 0.121407 0.030352 +All Reads ALL 48 906 3 0 0.023417 0.01352 +All Reads ALL 49 1068 0 0 0 0 +All Reads ALL 50 1027 0 0 0 0 +All Reads ALL 51 1081 0 0 0 0 +All Reads ALL 52 915 0 0 0 0 +All Reads ALL 53 816 0 0 0 0 +All Reads ALL 54 754 0 0 0 0 +All Reads ALL 55 751 0 0 0 0 +All Reads ALL 56 783 0 0 0 0 +All Reads ALL 57 721 0 0 0 0 +All Reads ALL 58 574 0 0 0 0 +All Reads ALL 59 563 0 0 0 0 +All Reads ALL 60 494 0 0 0 0 +All Reads ALL 61 331 0 0 0 0 +All Reads ALL 62 246 0 0 0 0 +All Reads ALL 63 247 0 0 0 0 +All Reads ALL 64 233 0 0 0 0 +All Reads ALL 65 217 0 0 0 0 +All Reads ALL 66 203 0 0 0 0 +All Reads ALL 67 181 0 0 0 0 +All Reads ALL 68 125 0 0 0 0 +All Reads ALL 69 103 0 0 0 0 +All Reads ALL 70 140 0 0 0 0 +All Reads ALL 71 137 0 0 0 0 +All Reads ALL 72 124 0 0 0 0 +All Reads ALL 73 119 0 0 0 0 +All Reads ALL 74 105 0 0 0 0 +All Reads ALL 75 111 0 0 0 0 +All Reads ALL 76 85 0 0 0 0 +All Reads ALL 77 71 0 0 0 0 +All Reads ALL 78 89 0 0 0 0 +All Reads ALL 79 96 0 0 0 0 +All Reads ALL 80 78 0 0 0 0 +All Reads ALL 81 50 0 0 0 0 +All Reads ALL 82 73 0 0 0 0 +All Reads ALL 83 65 0 0 0 0 +All Reads ALL 84 34 0 0 0 0 +All Reads ALL 85 39 0 0 0 0 +All Reads ALL 86 22 0 0 0 0 +All Reads ALL 87 28 0 0 0 0 +All Reads ALL 88 28 0 0 0 0 +All Reads ALL 89 30 0 0 0 0 +All Reads ALL 90 51 0 0 0 0 +All Reads ALL 91 26 0 0 0 0 +All Reads ALL 92 7 0 0 0 0 +All Reads ALL 93 0 0 0 0 0 +All Reads ALL 94 0 0 0 0 0 +All Reads ALL 95 0 0 0 0 0 +All Reads ALL 96 0 0 0 0 0 +All Reads ALL 97 0 0 0 0 0 +All Reads ALL 98 0 0 0 0 0 +All Reads ALL 99 0 0 0 0 0 +All Reads ALL 100 0 0 0 0 0 + + diff --git a/tests/expected/dna/test.gc_bias.summary_metrics.txt b/tests/expected/dna/test.gc_bias.summary_metrics.txt new file mode 100644 index 00000000..8a21261c --- /dev/null +++ b/tests/expected/dna/test.gc_bias.summary_metrics.txt @@ -0,0 +1,5 @@ +## METRICS CLASS picard.analysis.GcBiasSummaryMetrics +ACCUMULATION_LEVEL READS_USED WINDOW_SIZE TOTAL_CLUSTERS ALIGNED_READS AT_DROPOUT GC_DROPOUT GC_NC_0_19 GC_NC_20_39 GC_NC_40_59 GC_NC_60_79 GC_NC_80_100 SAMPLE LIBRARY READ_GROUP +All Reads ALL 100 2822 5642 29.055038 27.433584 2.178283 2.161449 0.084487 0 0 + + diff --git a/tests/expected/dna/test.hs_metrics.txt b/tests/expected/dna/test.hs_metrics.txt new file mode 100644 index 00000000..0f498da1 --- /dev/null +++ b/tests/expected/dna/test.hs_metrics.txt @@ -0,0 +1,870 @@ +## METRICS CLASS picard.analysis.directed.HsMetrics +BAIT_SET BAIT_TERRITORY BAIT_DESIGN_EFFICIENCY ON_BAIT_BASES NEAR_BAIT_BASES OFF_BAIT_BASES PCT_SELECTED_BASES PCT_OFF_BAIT ON_BAIT_VS_SELECTED MEAN_BAIT_COVERAGE PCT_USABLE_BASES_ON_BAIT PCT_USABLE_BASES_ON_TARGET FOLD_ENRICHMENT HS_LIBRARY_SIZE HS_PENALTY_10X HS_PENALTY_20X HS_PENALTY_30X HS_PENALTY_40X HS_PENALTY_50X HS_PENALTY_100X TARGET_TERRITORY GENOME_SIZE TOTAL_READS PF_READS PF_BASES PF_UNIQUE_READS PF_UQ_READS_ALIGNED PF_BASES_ALIGNED PF_UQ_BASES_ALIGNED ON_TARGET_BASES PCT_PF_READS PCT_PF_UQ_READS PCT_PF_UQ_READS_ALIGNED MEAN_TARGET_COVERAGE MEDIAN_TARGET_COVERAGE MAX_TARGET_COVERAGE MIN_TARGET_COVERAGE ZERO_CVG_TARGETS_PCT PCT_EXC_DUPE PCT_EXC_ADAPTER PCT_EXC_MAPQ PCT_EXC_BASEQ PCT_EXC_OVERLAP PCT_EXC_OFF_TARGET FOLD_80_BASE_PENALTY PCT_TARGET_BASES_1X PCT_TARGET_BASES_2X PCT_TARGET_BASES_10X PCT_TARGET_BASES_20X PCT_TARGET_BASES_30X PCT_TARGET_BASES_40X PCT_TARGET_BASES_50X PCT_TARGET_BASES_100X PCT_TARGET_BASES_250X PCT_TARGET_BASES_500X PCT_TARGET_BASES_1000X PCT_TARGET_BASES_2500X PCT_TARGET_BASES_5000X PCT_TARGET_BASES_10000X PCT_TARGET_BASES_25000X PCT_TARGET_BASES_50000X PCT_TARGET_BASES_100000X AT_DROPOUT GC_DROPOUT HET_SNP_SENSITIVITY HET_SNP_Q SAMPLE LIBRARY READ_GROUP +targets 35000 1 670989 0 0 1 0 1 19.171114 0.998301 0.364694 1.142886 3807 -1 -1 -1 -1 -1 -1 35000 40001 5642 5642 672131 3986 3984 670989 469869 245122 1 0.706487 0.999498 7.003486 0 862 0 0.5 0.299737 0 0 0.003982 0.330968 0 ? 0.033229 0.029943 0.026171 0.022171 0.020943 0.019486 0.019114 0.017171 0.010886 0.006 0 0 0 0 0 0 0 57.145714 0 0.031185 0 + +## HISTOGRAM java.lang.Integer +coverage_or_base_quality high_quality_coverage_count unfiltered_baseq_count +0 33837 0 +1 115 0 +2 34 0 +3 22 0 +4 18 23 +5 8 1 +6 19 1 +7 9 0 +8 13 8 +9 9 0 +10 93 1 +11 3 2 +12 5 0 +13 5 2481 +14 7 0 +15 2 0 +16 12 0 +17 5 15 +18 4 10 +19 4 0 +20 0 576 +21 4 1 +22 5 0 +23 1 0 +24 6 0 +25 4 5 +26 4 1154 +27 8 0 +28 6 0 +29 5 0 +30 18 0 +31 21 4527 +32 5 64 +33 0 1 +34 2 54518 +35 0 14 +36 2 1 +37 1 0 +38 1 126 +39 1 0 +40 1 24 +41 3 2 +42 0 94 +43 2 244 +44 1 9250 +45 0 174521 +46 2 0 +47 1 0 +48 1 0 +49 2 0 +50 1 0 +51 3 0 +52 1 0 +53 0 0 +54 1 0 +55 2 0 +56 1 0 +57 1 0 +58 4 0 +59 0 0 +60 0 0 +61 1 0 +62 3 0 +63 0 0 +64 2 0 +65 2 0 +66 0 0 +67 0 0 +68 2 0 +69 1 0 +70 2 0 +71 1 0 +72 2 0 +73 0 0 +74 1 0 +75 0 0 +76 2 0 +77 2 0 +78 2 0 +79 1 0 +80 2 0 +81 1 0 +82 2 0 +83 2 0 +84 1 0 +85 1 0 +86 1 0 +87 1 0 +88 0 0 +89 2 0 +90 0 0 +91 4 0 +92 2 0 +93 1 0 +94 3 0 +95 1 0 +96 1 0 +97 3 0 +98 1 0 +99 1 0 +100 1 0 +101 0 0 +102 0 0 +103 2 0 +104 2 0 +105 3 0 +106 2 0 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+773 0 0 +774 0 0 +775 0 0 +776 0 0 +777 0 0 +778 0 0 +779 0 0 +780 1 0 +781 1 0 +782 0 0 +783 0 0 +784 2 0 +785 0 0 +786 0 0 +787 1 0 +788 0 0 +789 0 0 +790 1 0 +791 0 0 +792 0 0 +793 2 0 +794 1 0 +795 1 0 +796 2 0 +797 0 0 +798 0 0 +799 0 0 +800 0 0 +801 0 0 +802 1 0 +803 0 0 +804 1 0 +805 2 0 +806 0 0 +807 0 0 +808 0 0 +809 2 0 +810 2 0 +811 0 0 +812 1 0 +813 2 0 +814 0 0 +815 0 0 +816 0 0 +817 1 0 +818 0 0 +819 0 0 +820 0 0 +821 0 0 +822 2 0 +823 0 0 +824 0 0 +825 1 0 +826 0 0 +827 1 0 +828 1 0 +829 1 0 +830 0 0 +831 1 0 +832 0 0 +833 1 0 +834 1 0 +835 0 0 +836 0 0 +837 0 0 +838 1 0 +839 0 0 +840 0 0 +841 1 0 +842 1 0 +843 0 0 +844 0 0 +845 0 0 +846 0 0 +847 0 0 +848 2 0 +849 1 0 +850 1 0 +851 1 0 +852 3 0 +853 0 0 +854 0 0 +855 1 0 +856 0 0 +857 1 0 +858 1 0 +859 0 0 +860 1 0 +861 1 0 +862 2 0 + diff --git a/tests/expected/dna/test.idxstats.txt b/tests/expected/dna/test.idxstats.txt new file mode 100644 index 00000000..f56aa9fe --- /dev/null +++ b/tests/expected/dna/test.idxstats.txt @@ -0,0 +1,2 @@ +chr22 40001 5642 0 +* 0 0 2 diff --git a/tests/expected/dna/test.insert_size_metrics.txt b/tests/expected/dna/test.insert_size_metrics.txt new file mode 100644 index 00000000..b99c4e54 --- /dev/null +++ b/tests/expected/dna/test.insert_size_metrics.txt @@ -0,0 +1,178 @@ +## METRICS CLASS picard.analysis.InsertSizeMetrics +MEDIAN_INSERT_SIZE MODE_INSERT_SIZE MEDIAN_ABSOLUTE_DEVIATION MIN_INSERT_SIZE MAX_INSERT_SIZE MEAN_INSERT_SIZE STANDARD_DEVIATION READ_PAIRS PAIR_ORIENTATION WIDTH_OF_10_PERCENT WIDTH_OF_20_PERCENT WIDTH_OF_30_PERCENT WIDTH_OF_40_PERCENT WIDTH_OF_50_PERCENT WIDTH_OF_60_PERCENT WIDTH_OF_70_PERCENT WIDTH_OF_80_PERCENT WIDTH_OF_90_PERCENT WIDTH_OF_95_PERCENT WIDTH_OF_99_PERCENT SAMPLE LIBRARY READ_GROUP +122 96 23 32 300 124.442269 32.720214 1992 FR 9 19 27 37 47 57 69 83 103 127 181 + +## HISTOGRAM java.lang.Integer +insert_size All_Reads.fr_count +32 1 +41 1 +49 2 +51 1 +52 2 +54 1 +58 1 +59 2 +60 1 +61 4 +62 1 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202 0.01 +chr22 201 0.01 +chr22 200 0.01 +chr22 199 0.01 +chr22 198 0.01 +chr22 197 0.01 +chr22 196 0.01 +chr22 195 0.01 +chr22 194 0.01 +chr22 193 0.01 +chr22 192 0.01 +chr22 191 0.01 +chr22 190 0.01 +chr22 189 0.01 +chr22 188 0.01 +chr22 187 0.01 +chr22 186 0.01 +chr22 185 0.01 +chr22 184 0.01 +chr22 183 0.01 +chr22 182 0.01 +chr22 181 0.01 +chr22 180 0.01 +chr22 179 0.01 +chr22 178 0.01 +chr22 177 0.01 +chr22 176 0.01 +chr22 175 0.01 +chr22 174 0.01 +chr22 173 0.01 +chr22 172 0.01 +chr22 171 0.01 +chr22 170 0.01 +chr22 169 0.01 +chr22 168 0.01 +chr22 167 0.01 +chr22 166 0.01 +chr22 165 0.01 +chr22 164 0.01 +chr22 163 0.01 +chr22 162 0.01 +chr22 161 0.01 +chr22 160 0.01 +chr22 159 0.01 +chr22 158 0.01 +chr22 157 0.01 +chr22 156 0.01 +chr22 155 0.01 +chr22 154 0.01 +chr22 153 0.01 +chr22 152 0.01 +chr22 151 0.02 +chr22 150 0.02 +chr22 149 0.02 +chr22 148 0.02 +chr22 147 0.02 +chr22 146 0.02 +chr22 145 0.02 +chr22 144 0.02 +chr22 143 0.02 +chr22 142 0.02 +chr22 141 0.02 +chr22 140 0.02 +chr22 139 0.02 +chr22 138 0.02 +chr22 137 0.02 +chr22 136 0.02 +chr22 135 0.02 +chr22 134 0.02 +chr22 133 0.02 +chr22 132 0.02 +chr22 131 0.02 +chr22 130 0.02 +chr22 129 0.02 +chr22 128 0.02 +chr22 127 0.02 +chr22 126 0.02 +chr22 125 0.02 +chr22 124 0.02 +chr22 123 0.02 +chr22 122 0.02 +chr22 121 0.02 +chr22 120 0.02 +chr22 119 0.02 +chr22 118 0.02 +chr22 117 0.02 +chr22 116 0.02 +chr22 115 0.02 +chr22 114 0.02 +chr22 113 0.02 +chr22 112 0.02 +chr22 111 0.02 +chr22 110 0.02 +chr22 109 0.02 +chr22 108 0.02 +chr22 107 0.02 +chr22 106 0.02 +chr22 105 0.02 +chr22 104 0.02 +chr22 103 0.02 +chr22 102 0.02 +chr22 101 0.02 +chr22 100 0.02 +chr22 99 0.02 +chr22 98 0.02 +chr22 97 0.02 +chr22 96 0.02 +chr22 95 0.02 +chr22 94 0.02 +chr22 93 0.02 +chr22 92 0.02 +chr22 91 0.02 +chr22 90 0.02 +chr22 89 0.02 +chr22 88 0.02 +chr22 87 0.02 +chr22 86 0.02 +chr22 85 0.02 +chr22 84 0.02 +chr22 83 0.02 +chr22 82 0.02 +chr22 81 0.02 +chr22 80 0.04 +chr22 79 0.04 +chr22 78 0.04 +chr22 77 0.04 +chr22 76 0.04 +chr22 75 0.04 +chr22 74 0.04 +chr22 73 0.04 +chr22 72 0.04 +chr22 71 0.04 +chr22 70 0.04 +chr22 69 0.04 +chr22 68 0.04 +chr22 67 0.04 +chr22 66 0.04 +chr22 65 0.04 +chr22 64 0.04 +chr22 63 0.04 +chr22 62 0.04 +chr22 61 0.04 +chr22 60 0.04 +chr22 59 0.04 +chr22 58 0.04 +chr22 57 0.04 +chr22 56 0.04 +chr22 55 0.04 +chr22 54 0.04 +chr22 53 0.04 +chr22 52 0.04 +chr22 51 0.04 +chr22 50 0.04 +chr22 49 0.05 +chr22 48 0.05 +chr22 47 0.05 +chr22 46 0.05 +chr22 45 0.05 +chr22 44 0.05 +chr22 43 0.05 +chr22 42 0.05 +chr22 41 0.05 +chr22 40 0.05 +chr22 39 0.05 +chr22 38 0.05 +chr22 37 0.05 +chr22 36 0.05 +chr22 35 0.05 +chr22 34 0.05 +chr22 33 0.05 +chr22 32 0.05 +chr22 31 0.05 +chr22 30 0.05 +chr22 29 0.05 +chr22 28 0.05 +chr22 27 0.05 +chr22 26 0.05 +chr22 25 0.05 +chr22 24 0.05 +chr22 23 0.05 +chr22 22 0.05 +chr22 21 0.05 +chr22 20 0.05 +chr22 19 0.05 +chr22 18 0.05 +chr22 17 0.05 +chr22 16 0.05 +chr22 15 0.05 +chr22 14 0.05 +chr22 13 0.05 +chr22 12 0.05 +chr22 11 0.05 +chr22 10 0.05 +chr22 9 0.05 +chr22 8 0.05 +chr22 7 0.06 +chr22 6 0.06 +chr22 5 0.07 +chr22 4 0.07 +chr22 3 0.07 +chr22 2 0.07 +chr22 1 0.07 +chr22 0 1.00 +total 204 0.01 +total 203 0.01 +total 202 0.01 +total 201 0.01 +total 200 0.01 +total 199 0.01 +total 198 0.01 +total 197 0.01 +total 196 0.01 +total 195 0.01 +total 194 0.01 +total 193 0.01 +total 192 0.01 +total 191 0.01 +total 190 0.01 +total 189 0.01 +total 188 0.01 +total 187 0.01 +total 186 0.01 +total 185 0.01 +total 184 0.01 +total 183 0.01 +total 182 0.01 +total 181 0.01 +total 180 0.01 +total 179 0.01 +total 178 0.01 +total 177 0.01 +total 176 0.01 +total 175 0.01 +total 174 0.01 +total 173 0.01 +total 172 0.01 +total 171 0.01 +total 170 0.01 +total 169 0.01 +total 168 0.01 +total 167 0.01 +total 166 0.01 +total 165 0.01 +total 164 0.01 +total 163 0.01 +total 162 0.01 +total 161 0.01 +total 160 0.01 +total 159 0.01 +total 158 0.01 +total 157 0.01 +total 156 0.01 +total 155 0.01 +total 154 0.01 +total 153 0.01 +total 152 0.01 +total 151 0.02 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0.05 +total 20 0.05 +total 19 0.05 +total 18 0.05 +total 17 0.05 +total 16 0.05 +total 15 0.05 +total 14 0.05 +total 13 0.05 +total 12 0.05 +total 11 0.05 +total 10 0.05 +total 9 0.05 +total 8 0.05 +total 7 0.06 +total 6 0.06 +total 5 0.07 +total 4 0.07 +total 3 0.07 +total 2 0.07 +total 1 0.07 +total 0 1.00 diff --git a/tests/expected/dna/test.mosdepth.summary.txt b/tests/expected/dna/test.mosdepth.summary.txt new file mode 100644 index 00000000..ec15caf6 --- /dev/null +++ b/tests/expected/dna/test.mosdepth.summary.txt @@ -0,0 +1,5 @@ +chrom length bases mean min max +chr22 40001 247878 6.20 0 867 +chr22_region 40001 247878 6.20 0 867 +total 40001 247878 6.20 0 867 +total_region 40001 247878 6.20 0 867 diff --git a/tests/expected/dna/test.per-base.bed.gz b/tests/expected/dna/test.per-base.bed.gz new file mode 100644 index 00000000..2bcaa495 Binary files /dev/null and b/tests/expected/dna/test.per-base.bed.gz differ diff --git a/tests/expected/dna/test.per-base.bed.gz.csi b/tests/expected/dna/test.per-base.bed.gz.csi new file mode 100644 index 00000000..360bf772 Binary files /dev/null and b/tests/expected/dna/test.per-base.bed.gz.csi differ diff --git a/tests/expected/dna/test.regions.bed.gz b/tests/expected/dna/test.regions.bed.gz new file mode 100644 index 00000000..06a1c5e1 Binary files /dev/null and b/tests/expected/dna/test.regions.bed.gz differ diff --git a/tests/expected/dna/test.regions.bed.gz.csi b/tests/expected/dna/test.regions.bed.gz.csi new file mode 100644 index 00000000..7fe77157 Binary files /dev/null and b/tests/expected/dna/test.regions.bed.gz.csi differ diff --git a/tests/expected/dna/test.stats.txt b/tests/expected/dna/test.stats.txt new file mode 100644 index 00000000..9779c96d --- /dev/null +++ b/tests/expected/dna/test.stats.txt @@ -0,0 +1,1916 @@ +# This file was produced by samtools stats (1.24+htslib-1.24) and can be plotted using plot-bamstats +# This file contains statistics for all reads. +# The command line was: stats /Users/benjamin/RustQC-dna/tests/data/dna/test.dna.bam +# CHK, Checksum [2]Read Names [3]Sequences [4]Qualities +# CHK, CRC32 of reads which passed filtering followed by addition (32bit overflow) +CHK 82cbdacd 541c12e0 25a61aa9 +# Summary Numbers. Use `grep ^SN | cut -f 2-` to extract this part. +SN raw total sequences: 5642 # excluding supplementary and secondary reads +SN filtered sequences: 0 +SN sequences: 5642 +SN is sorted: 1 # sorted by coordinate +SN 1st fragments: 2821 +SN last fragments: 2821 +SN reads mapped: 5640 +SN reads mapped and paired: 5640 # paired-end technology bit set + both mates mapped +SN reads unmapped: 2 +SN reads properly paired: 5638 # proper-pair bit set +SN reads paired: 5642 # paired-end technology bit set +SN reads duplicated: 1656 # PCR or optical duplicate bit set +SN reads MQ0: 0 # mapped and MQ=0 +SN reads QC failed: 0 +SN non-primary alignments: 2 +SN supplementary alignments: 0 +SN total length: 672131 # ignores clipping +SN total first fragment length: 335944 # ignores clipping +SN total last fragment length: 336187 # ignores clipping +SN bases mapped: 671854 # ignores clipping +SN bases mapped (cigar): 670991 # more accurate +SN bases trimmed: 0 +SN bases duplicated: 201314 +SN mismatches: 1352 # from NM fields +SN error rate: 2.014930e-03 # mismatches / bases mapped (cigar) +SN average length: 119 +SN average first fragment length: 119 +SN average last fragment length: 119 +SN maximum length: 143 +SN maximum first fragment length: 143 +SN maximum last fragment length: 143 +SN average quality: 40.9 +SN insert size average: 124.8 +SN insert size standard deviation: 31.2 +SN inward oriented pairs: 2814 +SN outward oriented pairs: 6 +SN pairs with other orientation: 0 +SN pairs on different chromosomes: 0 +SN percentage of properly paired reads (%): 99.9 +# First Fragment Qualities. Use `grep ^FFQ | cut -f 2-` to extract this part. +# Columns correspond to qualities and rows to cycles. First column is the cycle number. +FFQ 1 0 0 2 0 0 0 0 0 0 0 0 0 0 17 0 0 0 0 0 0 15 0 0 0 0 0 0 0 0 0 0 34 0 0 881 0 0 0 0 0 1 0 0 1 54 1816 0 +FFQ 2 0 0 1 0 0 0 0 0 0 0 0 0 0 25 0 0 0 0 0 0 15 0 0 0 0 0 0 0 0 0 0 24 0 0 882 0 0 0 3 0 0 0 1 1 53 1816 0 +FFQ 3 0 0 0 0 1 0 0 0 0 0 0 0 0 24 0 0 0 0 0 0 15 0 0 0 0 0 0 0 0 0 0 35 0 0 872 0 0 0 2 0 0 0 0 0 62 1810 0 +FFQ 4 0 0 1 0 0 0 0 0 0 0 0 0 0 13 0 0 0 0 0 0 15 0 0 0 0 0 0 0 0 0 0 36 0 0 883 1 0 0 4 0 0 0 0 0 50 1818 0 +FFQ 5 0 0 0 0 0 0 0 0 0 0 0 0 0 11 0 0 0 0 0 0 18 0 0 0 0 0 0 0 0 0 0 34 0 0 885 2 0 0 0 0 0 0 0 0 42 1829 0 +FFQ 6 0 0 1 0 0 0 0 0 0 0 0 0 0 25 0 0 0 0 0 0 14 0 0 0 0 0 0 0 0 0 0 32 0 0 877 0 0 0 1 0 0 0 0 0 66 1805 0 +FFQ 7 0 0 0 0 0 0 0 0 0 0 0 0 0 25 0 0 0 0 0 0 14 0 0 0 0 0 1 0 0 0 0 30 0 0 876 1 0 0 1 0 0 0 0 0 46 1827 0 +FFQ 8 0 0 2 0 0 0 0 0 0 0 0 0 0 23 0 0 0 0 0 0 16 0 0 0 0 0 1 0 0 0 0 33 0 0 873 0 0 0 2 0 0 0 0 0 61 1810 0 +FFQ 9 0 0 1 0 0 0 0 0 0 0 0 0 0 24 0 0 0 0 1 0 8 0 0 0 0 0 1 0 0 0 0 37 0 0 876 0 0 0 4 0 0 0 0 1 53 1815 0 +FFQ 10 0 0 1 0 0 0 0 0 0 0 0 0 0 20 0 0 0 0 0 0 9 0 0 0 0 0 0 0 0 0 0 23 0 0 895 1 0 0 3 0 2 0 0 0 58 1809 0 +FFQ 11 0 0 0 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 22 0 0 0 0 0 0 0 0 0 0 23 0 0 895 0 0 0 0 0 0 0 0 1 63 1809 0 +FFQ 12 0 0 4 0 0 0 0 0 0 0 0 0 0 13 0 0 0 0 0 0 17 0 0 0 0 0 0 0 0 0 0 40 0 0 879 0 0 0 6 0 0 0 0 0 53 1809 0 +FFQ 13 0 0 2 0 0 0 0 0 0 0 0 0 0 21 0 0 0 1 0 0 13 0 0 0 0 0 3 0 0 0 0 38 0 0 872 0 0 0 3 0 0 0 0 1 49 1818 0 +FFQ 14 0 0 1 0 0 0 0 0 0 0 0 0 0 18 0 0 0 0 0 0 15 0 0 0 0 0 3 0 0 0 0 41 2 0 870 0 0 0 2 0 1 0 0 1 64 1803 0 +FFQ 15 0 0 4 0 1 0 0 0 0 0 0 0 0 21 0 1 0 0 0 0 14 0 0 0 0 0 0 0 0 0 0 34 0 0 878 0 0 0 1 0 0 0 0 0 63 1804 0 +FFQ 16 0 0 4 0 0 0 0 0 0 0 0 0 0 20 0 0 0 0 0 0 12 0 0 0 0 0 1 0 0 0 0 30 1 0 886 0 0 0 0 0 0 0 0 1 44 1822 0 +FFQ 17 0 0 3 0 0 0 0 0 0 0 0 0 0 18 0 0 0 0 0 0 11 0 0 0 0 0 1 0 0 0 0 35 0 0 883 0 0 0 2 0 1 0 0 0 61 1806 0 +FFQ 18 0 0 3 0 0 0 0 0 0 0 0 0 0 22 0 0 0 0 0 0 11 0 0 0 0 0 1 0 0 0 0 39 0 0 876 0 0 0 0 0 0 0 0 1 55 1813 0 +FFQ 19 0 0 2 0 1 0 0 0 0 0 0 0 0 27 0 0 0 0 0 0 14 0 0 0 0 0 0 0 0 0 0 35 0 0 873 0 0 0 2 0 0 0 0 0 69 1798 0 +FFQ 20 0 0 2 0 1 0 0 0 1 0 0 0 0 26 0 0 0 0 0 0 9 0 0 0 0 0 3 0 0 0 0 36 3 0 873 0 0 0 1 0 3 0 1 2 70 1790 0 +FFQ 21 0 0 0 0 0 0 0 0 0 0 0 0 0 28 0 0 0 0 0 0 5 0 0 0 0 0 2 0 0 0 0 46 0 0 866 0 0 0 1 0 0 0 0 0 64 1809 0 +FFQ 22 0 0 3 0 0 0 0 0 0 0 0 0 0 26 0 0 0 0 1 0 13 0 0 0 0 0 5 0 0 0 0 31 0 0 873 0 0 0 2 0 0 0 0 0 55 1812 0 +FFQ 23 0 0 3 0 0 0 0 0 0 0 0 0 0 30 0 0 0 0 0 0 10 0 0 0 0 0 4 0 0 0 0 41 0 0 863 0 0 0 4 0 2 0 1 1 52 1810 0 +FFQ 24 0 0 2 0 0 0 0 0 0 0 0 0 0 34 0 0 0 0 1 0 12 0 0 0 0 0 4 0 0 0 0 29 0 0 870 1 0 0 6 0 0 0 0 1 73 1788 0 +FFQ 25 0 0 5 0 0 0 0 0 0 0 0 0 0 28 0 0 0 0 0 0 11 0 0 0 0 0 0 0 0 0 0 34 0 0 877 0 0 0 1 0 0 0 2 0 53 1810 0 +FFQ 26 0 0 2 0 2 0 0 0 0 0 0 0 0 32 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 37 0 0 873 0 0 0 3 0 0 0 0 0 57 1809 0 +FFQ 27 0 0 3 0 0 0 0 0 0 0 0 0 0 40 0 0 0 0 0 0 7 0 0 0 0 0 4 0 0 0 0 29 0 0 870 0 0 0 6 0 0 0 0 0 40 1822 0 +FFQ 28 0 0 5 0 1 0 0 0 0 0 0 0 0 36 0 0 0 0 0 0 9 0 0 0 0 0 1 0 0 0 0 24 2 0 880 0 0 0 2 0 0 0 0 0 50 1811 0 +FFQ 29 0 0 1 0 1 0 0 0 0 0 0 0 0 32 0 0 0 0 0 0 3 0 0 0 0 1 4 0 0 0 0 52 0 0 857 3 0 0 7 0 0 0 0 1 68 1791 0 +FFQ 30 0 0 1 0 0 0 0 0 0 0 0 0 0 35 0 0 0 0 0 0 4 0 0 0 0 0 3 0 0 0 0 32 0 0 874 0 0 0 6 0 0 0 0 4 58 1804 0 +FFQ 31 0 0 5 0 2 0 0 0 0 0 0 0 0 24 0 0 0 0 0 0 11 0 0 0 0 0 1 0 0 0 0 38 0 0 875 0 0 0 3 0 0 0 0 0 54 1807 0 +FFQ 32 0 0 3 0 1 0 0 0 0 0 0 0 0 35 0 0 0 0 0 0 8 0 0 0 0 0 1 0 0 0 0 32 1 0 873 0 0 0 2 0 0 0 1 0 59 1804 0 +FFQ 33 0 0 7 0 2 0 0 0 0 0 0 0 0 35 0 0 0 0 1 0 6 0 0 0 0 0 5 0 0 0 0 26 0 0 877 0 0 0 3 0 0 0 0 0 53 1805 0 +FFQ 34 0 0 5 0 0 0 0 0 0 0 0 0 0 32 0 0 0 0 0 0 6 0 0 0 0 0 5 0 0 0 0 29 1 0 877 0 0 0 2 0 0 0 0 0 62 1801 0 +FFQ 35 0 0 4 0 0 0 0 0 0 0 0 0 0 31 0 0 0 0 0 0 8 0 0 0 0 0 5 0 0 0 0 29 0 0 877 0 0 0 1 0 0 0 0 0 55 1810 0 +FFQ 36 0 0 2 0 0 0 0 0 0 0 0 0 0 32 0 0 0 0 0 0 7 0 0 0 0 0 7 0 0 0 0 31 0 0 872 0 0 0 2 0 0 0 0 1 64 1802 0 +FFQ 37 0 0 3 0 0 0 0 0 0 0 0 0 0 36 0 0 0 0 0 0 8 0 0 0 0 0 4 0 0 0 0 34 0 0 867 1 0 0 0 0 0 0 2 0 65 1800 0 +FFQ 38 0 0 2 0 0 0 0 0 0 0 0 0 0 36 0 0 0 0 0 0 6 0 0 0 0 0 7 0 0 0 0 33 1 0 867 1 0 0 5 0 1 0 0 0 69 1792 0 +FFQ 39 0 0 3 0 2 0 0 0 0 0 0 0 0 32 0 0 0 0 0 0 2 0 0 0 0 0 5 0 0 0 0 40 1 0 870 0 0 0 2 0 0 0 0 0 63 1800 0 +FFQ 40 1 0 2 0 0 0 0 0 0 0 0 0 0 33 0 0 0 0 1 0 9 0 0 0 0 0 7 0 0 0 0 35 2 0 865 0 0 0 3 0 0 0 0 0 62 1800 0 +FFQ 41 0 0 3 0 0 0 0 0 0 0 0 0 0 24 0 0 0 0 0 0 8 0 0 0 0 0 9 0 0 0 0 33 0 0 875 0 0 0 2 0 0 1 1 1 58 1805 0 +FFQ 42 0 0 3 0 0 0 0 0 0 0 0 0 0 32 0 0 0 0 0 0 14 0 0 0 0 0 9 0 0 0 0 26 0 0 870 0 0 0 4 0 0 0 0 0 51 1810 0 +FFQ 43 0 0 2 0 0 0 0 0 0 0 0 0 0 42 0 0 0 0 1 0 6 0 0 0 0 0 4 0 0 0 0 30 0 0 864 0 0 0 2 0 0 0 2 0 60 1806 0 +FFQ 44 1 0 5 0 0 0 0 0 0 0 0 0 0 39 0 0 0 0 0 0 7 0 0 0 0 0 5 0 0 0 0 33 0 0 863 0 0 0 5 0 0 0 0 1 72 1788 0 +FFQ 45 1 0 3 0 0 0 0 0 0 0 0 0 0 30 0 0 0 0 0 0 7 0 0 0 0 0 5 0 0 0 0 39 0 0 867 1 0 0 3 0 0 0 1 1 70 1791 0 +FFQ 46 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1756 0 +LFQ 55 3 0 0 0 0 0 0 0 0 0 0 0 0 40 0 0 0 0 0 0 12 0 0 0 0 0 17 0 0 0 0 74 1 0 799 0 0 0 1 0 0 0 0 2 121 1743 0 +LFQ 56 3 0 0 0 0 0 0 0 0 0 0 0 0 49 0 0 0 0 0 0 9 0 0 0 0 0 16 0 0 0 0 60 1 0 808 0 0 0 0 0 0 0 0 2 134 1731 0 +LFQ 57 3 0 1 0 0 0 0 0 0 0 0 0 0 45 0 0 0 0 0 0 3 0 0 0 0 0 18 0 0 0 0 57 0 0 818 0 0 0 1 0 0 0 1 5 132 1729 0 +LFQ 58 3 0 0 0 0 0 0 0 0 0 0 0 0 51 0 0 0 0 0 0 2 0 0 0 0 0 11 0 0 0 0 52 1 0 826 0 0 0 2 0 0 0 0 6 107 1752 0 +LFQ 59 3 0 0 0 0 0 0 0 0 0 0 0 0 30 0 0 0 0 0 0 9 0 0 0 0 0 14 0 0 0 0 55 1 0 832 1 0 0 0 0 0 0 1 0 129 1737 0 +LFQ 60 3 0 0 0 0 0 0 0 0 0 0 0 0 35 0 0 0 0 0 0 8 0 0 0 0 0 14 0 0 0 0 70 0 0 813 0 0 0 0 0 0 0 0 6 118 1743 0 +LFQ 61 3 0 0 0 0 0 0 0 0 0 0 0 0 50 0 0 0 0 0 0 3 0 0 0 0 0 15 0 0 0 0 49 0 0 820 0 0 0 1 0 0 0 0 2 107 1759 0 +LFQ 62 3 0 0 0 0 0 0 0 0 0 0 0 0 35 0 0 0 1 0 0 9 0 0 0 0 0 21 0 0 0 0 67 1 0 808 0 0 0 0 0 0 0 1 2 121 1736 0 +LFQ 63 3 0 0 0 0 0 0 0 0 0 0 0 0 46 0 0 0 0 0 0 4 0 0 0 0 0 23 0 0 0 0 72 1 0 792 0 0 0 1 0 0 0 0 2 124 1736 0 +LFQ 64 3 0 0 0 0 0 0 0 0 0 0 0 0 58 0 0 0 0 0 0 6 0 0 0 0 0 19 0 0 0 0 78 2 0 772 0 0 0 1 0 0 0 0 2 111 1747 0 +LFQ 65 3 0 0 0 0 0 0 0 0 0 0 0 0 47 0 0 0 0 0 0 11 0 0 0 0 0 22 0 0 0 0 61 1 0 794 0 0 0 0 0 0 0 0 6 134 1720 0 +LFQ 66 2 0 0 0 0 0 0 0 0 0 0 0 0 46 0 0 0 0 0 0 8 0 0 0 0 0 25 0 0 0 0 53 1 0 801 0 0 0 1 0 0 0 1 1 135 1720 0 +LFQ 67 2 0 0 0 0 0 0 0 0 0 0 0 0 39 0 0 0 0 0 0 3 0 0 0 0 0 22 0 0 0 0 69 1 0 796 0 0 0 1 0 0 0 2 0 114 1743 0 +LFQ 68 2 0 0 0 0 0 0 0 0 0 0 0 0 40 0 0 0 0 0 0 6 0 0 0 0 0 28 0 0 0 0 60 0 0 792 0 0 0 1 0 0 0 0 3 128 1726 0 +LFQ 69 2 0 0 0 0 0 0 0 0 0 0 0 0 42 0 0 0 1 0 0 11 0 0 0 0 0 22 0 0 0 0 68 0 0 781 0 0 0 1 0 0 0 1 5 143 1705 0 +LFQ 70 3 0 0 0 0 0 0 0 0 0 0 0 0 47 0 0 0 0 0 0 7 0 0 0 0 0 31 0 0 0 0 57 2 0 778 0 0 0 0 0 0 0 1 4 127 1720 0 +LFQ 71 2 0 0 0 0 0 0 0 0 0 0 0 0 54 0 0 0 0 0 0 3 0 0 0 0 0 16 0 0 0 0 67 3 0 778 0 0 0 0 0 0 0 1 4 138 1701 0 +LFQ 72 2 0 1 0 0 0 0 0 0 0 0 0 0 49 0 0 0 0 0 0 6 0 0 0 0 0 18 0 0 0 0 64 2 0 777 0 0 0 1 0 0 0 0 5 139 1692 0 +LFQ 73 1 0 0 0 0 0 0 0 0 0 0 0 0 38 0 0 0 0 0 0 8 0 0 0 0 0 29 0 0 0 0 75 0 0 761 0 0 0 0 0 0 0 1 5 146 1685 0 +LFQ 74 2 0 1 0 1 0 0 0 0 0 0 0 0 54 0 0 0 0 0 0 9 0 0 0 0 0 13 0 0 0 0 84 0 0 745 0 0 0 0 0 0 0 2 0 145 1685 0 +LFQ 75 2 0 0 0 0 0 0 0 0 0 0 0 0 56 0 0 0 0 0 0 8 0 0 0 0 0 24 0 0 0 0 55 0 0 762 0 0 0 0 0 1 0 0 3 153 1673 0 +LFQ 76 2 0 0 0 0 0 0 0 0 0 0 0 0 40 0 0 0 0 0 0 15 0 0 0 0 0 18 0 0 0 0 80 2 0 749 0 0 0 0 0 0 0 1 6 146 1666 0 +LFQ 77 1 0 1 0 0 0 0 0 0 0 0 0 0 61 0 0 0 0 0 0 8 0 0 0 0 0 20 0 0 0 0 73 1 0 736 0 0 0 1 0 0 0 1 4 136 1671 0 +LFQ 78 1 0 0 0 0 0 0 0 0 0 0 0 0 56 0 0 0 0 0 0 16 0 0 0 0 0 21 0 0 0 0 68 1 0 724 0 0 0 1 0 0 0 0 4 148 1655 0 +LFQ 79 1 0 0 0 0 0 0 0 0 0 0 0 0 45 0 0 0 0 0 0 14 0 0 0 0 0 24 0 0 0 0 78 2 0 717 0 0 0 1 0 0 0 2 4 151 1641 0 +LFQ 80 2 0 0 0 0 0 0 0 0 0 0 0 0 51 0 0 0 0 0 0 7 0 0 0 0 0 37 0 0 0 0 76 1 0 700 0 0 0 0 0 0 0 1 6 146 1640 0 +LFQ 81 1 0 0 0 0 0 0 0 0 0 0 0 0 45 0 0 0 0 0 0 10 0 0 0 0 0 13 0 0 0 0 90 2 0 703 0 0 0 0 0 0 0 1 5 142 1638 0 +LFQ 82 2 0 0 0 1 0 0 0 0 0 0 0 0 54 0 0 0 0 0 0 6 0 0 0 0 1 25 0 0 0 0 74 0 0 688 0 0 0 4 0 0 0 0 5 154 1612 0 +LFQ 83 1 0 0 0 0 0 0 0 0 0 0 0 0 39 0 0 0 0 0 0 4 0 0 0 0 0 25 0 0 0 0 71 0 0 700 0 0 0 1 0 0 0 1 5 160 1601 0 +LFQ 84 1 0 0 0 0 0 0 0 0 0 0 0 0 54 0 0 0 0 0 0 10 0 0 0 0 0 20 0 0 0 0 70 1 0 675 0 0 0 0 0 0 0 2 9 135 1612 0 +LFQ 85 1 0 0 0 0 0 0 0 0 0 0 0 0 45 0 0 0 0 0 0 5 0 0 0 0 0 24 0 0 0 0 76 2 0 668 0 0 0 0 0 0 0 0 6 145 1592 0 +LFQ 86 0 0 0 0 0 0 0 0 0 0 0 0 0 45 0 0 0 0 0 0 9 0 0 0 0 0 29 0 0 0 0 84 3 0 642 0 0 0 0 0 0 0 3 6 139 1589 0 +LFQ 87 1 0 0 0 0 0 0 0 0 0 0 0 0 53 0 0 0 0 0 0 7 0 0 0 0 0 27 0 0 0 0 88 1 0 629 0 0 0 0 0 0 0 0 10 147 1562 0 +LFQ 88 1 0 0 0 0 0 0 0 0 0 0 0 0 38 0 0 0 1 0 0 7 0 0 0 0 0 25 0 0 0 0 83 2 0 637 0 0 0 0 0 0 0 3 7 122 1569 0 +LFQ 89 1 0 1 0 0 0 0 0 0 0 0 0 0 44 0 0 0 0 0 0 5 0 0 0 0 0 32 0 0 0 0 75 3 0 621 0 0 0 0 0 0 0 1 7 124 1552 0 +LFQ 90 1 0 0 0 0 0 0 0 0 0 0 0 0 55 0 0 0 0 0 0 2 0 0 0 0 0 20 0 0 0 0 57 0 0 637 0 0 0 0 0 0 0 2 3 137 1531 0 +LFQ 91 1 0 0 0 0 0 0 0 0 0 0 0 0 42 0 0 0 0 0 0 5 0 0 0 0 0 31 0 0 0 0 67 1 0 614 0 0 0 1 0 0 0 1 3 135 1528 0 +LFQ 92 1 0 1 0 0 0 0 0 0 0 0 0 0 40 0 0 0 0 0 0 3 0 0 0 0 0 24 0 0 0 0 67 3 0 616 0 0 0 1 0 0 0 2 2 149 1498 0 +LFQ 93 1 0 0 0 0 0 0 0 0 0 0 0 0 46 0 0 0 0 0 0 5 0 0 0 0 0 30 0 0 0 0 49 2 0 610 0 0 0 0 0 0 0 1 5 142 1486 0 +LFQ 94 0 0 1 0 0 0 0 0 0 0 0 0 0 48 0 0 0 0 0 0 4 0 0 0 0 0 20 0 0 0 0 73 2 0 588 0 0 0 1 0 0 0 1 8 149 1459 0 +LFQ 95 0 0 0 0 0 0 0 0 0 0 0 0 0 52 0 0 0 0 0 0 2 0 0 0 0 0 30 0 0 0 0 68 2 0 569 0 0 0 0 0 0 0 2 6 143 1460 0 +LFQ 96 1 0 0 0 0 0 0 0 0 0 0 0 0 45 0 0 0 0 0 0 9 0 0 0 0 0 32 0 0 0 0 74 0 0 544 0 0 0 0 0 0 0 1 5 149 1431 0 +LFQ 97 1 0 1 0 0 0 0 0 0 0 0 0 0 52 0 0 0 0 0 0 6 0 0 0 0 0 29 0 0 0 0 62 0 0 530 0 0 0 0 0 0 0 2 5 147 1402 0 +LFQ 98 1 0 0 0 0 0 0 0 0 0 0 0 0 50 0 0 0 0 0 0 10 0 0 0 0 0 27 0 0 0 0 61 2 0 520 0 0 0 2 0 0 0 4 4 148 1374 0 +LFQ 99 1 0 0 0 0 0 0 0 0 0 0 0 0 46 0 0 0 0 0 0 5 0 0 0 0 0 28 0 0 0 0 58 0 0 518 0 0 0 0 0 0 0 1 6 152 1360 0 +LFQ 100 0 0 0 0 0 0 0 0 0 0 0 0 0 39 0 0 0 0 0 0 5 0 0 0 0 0 32 0 0 0 0 66 1 0 502 0 0 0 0 0 0 0 4 7 138 1357 0 +LFQ 101 1 0 1 0 0 0 0 0 0 0 0 0 0 35 0 0 0 0 0 0 6 0 0 0 0 1 38 0 0 0 0 80 0 0 466 0 0 0 0 0 0 0 0 11 143 1325 0 +LFQ 102 1 0 0 0 0 0 0 0 0 0 0 0 0 39 0 0 0 0 0 0 3 0 0 0 0 0 36 0 0 0 0 73 0 0 466 0 0 0 0 0 0 0 1 6 150 1308 0 +LFQ 103 1 0 0 0 0 0 0 0 0 0 0 0 0 40 0 0 0 0 0 0 6 0 0 0 0 0 42 0 0 0 0 77 2 0 445 0 0 0 1 0 0 0 2 4 144 1292 0 +LFQ 104 0 0 0 0 0 0 0 0 0 0 0 0 0 38 0 0 0 1 0 0 6 0 0 0 0 0 46 0 0 0 0 70 1 0 444 0 0 0 0 0 0 0 1 9 149 1269 0 +LFQ 105 0 0 0 0 0 0 0 0 0 0 0 0 0 38 0 0 0 0 0 0 4 0 0 0 0 0 30 0 0 0 0 83 3 0 432 0 0 0 0 0 0 0 6 5 175 1225 0 +LFQ 106 0 0 0 0 1 0 0 0 0 0 0 0 0 39 0 0 0 0 0 0 4 0 0 0 0 0 37 0 0 0 0 81 3 0 417 0 0 0 0 0 0 0 2 4 186 1201 0 +LFQ 107 0 0 1 0 0 0 0 0 0 0 0 0 0 38 0 0 0 0 0 0 6 0 0 0 0 0 43 0 0 0 0 81 0 0 400 0 0 0 1 0 0 0 1 8 161 1207 0 +LFQ 108 0 0 2 0 0 0 0 0 0 0 0 0 0 33 0 0 0 1 0 0 3 0 0 0 0 0 47 0 0 0 0 108 2 0 365 0 0 0 0 0 0 0 2 3 161 1185 0 +LFQ 109 1 0 0 0 0 0 0 0 0 0 0 0 0 35 0 0 0 1 0 0 2 0 0 0 0 0 45 0 0 0 0 107 0 0 353 0 0 0 0 0 0 0 2 7 178 1155 0 +LFQ 110 1 0 0 0 1 0 0 0 0 0 0 0 0 36 0 0 0 0 0 0 10 0 0 0 0 0 44 0 0 0 0 90 2 0 352 0 0 0 0 0 0 0 4 10 166 1146 0 +LFQ 111 0 0 0 0 0 0 0 0 1 0 0 0 0 30 0 0 0 0 0 0 9 0 0 0 0 0 36 0 0 0 0 124 0 0 322 0 0 0 0 0 0 0 3 5 182 1116 0 +LFQ 112 1 0 0 0 0 0 0 0 0 0 0 0 0 31 0 0 0 0 0 0 10 0 0 0 0 0 33 0 0 0 0 118 2 0 322 0 0 0 0 0 0 0 4 7 182 1089 0 +LFQ 113 0 0 0 0 0 0 0 0 0 0 0 0 0 35 0 0 0 0 0 0 11 0 0 0 0 0 42 0 0 0 0 110 2 0 305 0 0 0 1 0 0 0 2 11 194 1064 0 +LFQ 114 0 0 0 0 0 0 0 0 0 0 0 0 0 38 0 0 0 1 0 0 7 0 0 0 0 0 44 0 0 0 0 120 1 0 285 0 0 0 1 0 0 0 6 9 190 1039 0 +LFQ 115 1 0 0 0 0 0 0 0 0 0 0 0 0 31 0 0 0 2 0 0 12 0 0 0 0 0 39 0 0 0 0 114 2 0 287 0 0 0 1 0 0 0 1 10 187 1024 0 +LFQ 116 1 0 0 0 0 0 0 0 0 0 0 0 0 38 0 0 0 0 0 0 5 0 0 0 0 0 44 0 0 0 0 117 1 0 266 0 0 0 0 0 0 0 4 7 183 997 0 +LFQ 117 0 0 0 0 0 0 0 1 0 0 0 0 0 33 0 0 0 2 0 0 9 0 0 0 0 0 43 0 0 0 0 102 1 0 273 0 0 0 0 0 0 0 3 4 164 992 0 +LFQ 118 0 0 0 0 0 0 0 0 0 0 0 0 0 34 0 0 0 1 0 0 6 0 0 0 0 0 46 0 0 0 0 98 0 0 266 0 0 0 0 0 0 0 2 5 180 956 0 +LFQ 119 0 0 1 0 0 0 0 0 0 0 0 0 0 35 0 0 0 0 0 0 8 0 0 0 0 0 37 0 0 0 0 110 1 0 249 0 0 0 1 0 0 0 2 9 161 946 0 +LFQ 120 0 0 0 0 0 0 0 0 0 0 0 0 0 30 0 0 0 1 0 0 11 0 0 0 0 0 36 0 0 0 0 97 2 0 256 0 0 0 1 0 0 0 4 8 163 914 0 +LFQ 121 0 0 0 0 0 0 0 0 0 0 0 0 0 33 0 0 0 1 0 0 13 0 0 0 0 0 33 0 0 0 0 103 0 0 237 0 0 0 0 0 0 0 3 6 162 917 0 +LFQ 122 0 0 0 0 0 0 0 0 0 0 0 0 0 38 0 0 0 0 0 0 6 0 0 0 0 0 31 0 0 0 0 105 0 0 225 0 0 0 0 0 0 0 0 6 147 911 0 +LFQ 123 0 0 0 0 0 0 0 0 0 0 0 0 0 30 0 0 0 0 0 0 13 0 0 0 0 0 31 0 0 0 0 107 0 0 218 0 0 0 0 0 0 0 2 6 153 879 0 +LFQ 124 0 0 0 0 0 0 0 0 0 0 0 0 0 28 0 0 0 0 0 0 9 0 0 0 0 0 33 0 0 0 0 91 1 0 228 0 0 0 2 0 0 0 2 8 158 851 0 +LFQ 125 0 0 0 0 0 0 0 0 0 0 0 0 0 29 0 0 0 1 0 0 8 0 0 0 0 0 29 0 0 0 0 113 0 0 193 0 0 0 0 0 0 0 3 8 155 836 0 +LFQ 126 0 0 0 0 0 0 0 0 0 0 0 0 0 36 0 0 0 0 0 0 11 0 0 0 0 0 27 0 0 0 0 84 0 0 202 0 0 0 1 0 0 1 3 8 146 820 0 +LFQ 127 1 0 0 0 1 0 0 0 0 0 0 0 0 27 0 0 0 0 0 0 12 0 0 0 0 0 35 0 0 0 0 96 1 0 186 0 0 0 0 0 0 0 2 4 138 812 0 +LFQ 128 1 0 0 0 0 0 0 0 0 0 0 0 0 28 0 0 0 1 0 0 7 0 0 0 0 0 26 0 0 0 0 80 0 0 200 0 0 0 0 0 0 0 2 13 141 785 0 +LFQ 129 0 0 0 0 0 0 0 1 0 0 1 0 0 34 0 0 0 1 0 0 6 0 0 0 0 0 27 0 0 0 0 75 3 0 192 0 0 0 0 0 0 0 1 7 138 767 0 +LFQ 130 0 0 1 0 0 0 0 0 0 0 0 0 0 27 0 0 0 0 0 0 4 0 0 0 0 0 28 0 0 0 0 79 2 0 184 0 0 0 0 0 0 0 4 11 133 752 0 +LFQ 131 0 0 0 0 0 0 0 0 0 0 0 0 0 23 0 0 0 1 0 0 6 0 0 0 0 0 21 0 0 0 0 82 1 0 182 0 0 0 1 0 0 0 3 7 113 746 0 +LFQ 132 0 0 0 0 0 0 0 0 0 0 0 0 0 19 0 0 0 0 0 0 9 0 0 0 0 0 18 0 0 0 0 71 0 0 186 0 0 0 0 0 0 0 0 5 134 699 0 +LFQ 133 0 0 0 0 0 0 0 0 0 0 0 0 0 25 0 0 0 0 0 0 3 0 0 0 0 0 25 0 0 0 0 83 0 0 160 0 0 0 1 0 0 0 3 5 112 699 0 +LFQ 134 0 0 0 0 0 0 0 0 0 0 0 0 0 26 0 0 0 1 0 0 5 0 0 0 0 0 26 0 0 0 0 70 1 0 160 0 0 0 0 0 0 0 0 9 115 684 0 +LFQ 135 0 0 0 0 0 0 0 0 0 0 0 0 0 21 0 0 0 0 0 0 6 0 0 0 0 0 26 0 0 0 0 70 0 0 152 2 0 0 0 0 0 0 0 5 115 675 0 +LFQ 136 0 0 0 0 0 0 0 0 0 0 0 0 0 26 0 0 0 1 0 0 7 0 0 0 0 0 15 0 0 0 0 71 0 0 144 0 0 0 1 0 0 0 0 3 111 662 0 +LFQ 137 0 0 0 0 0 0 0 0 0 0 0 0 0 25 0 0 0 1 0 0 6 0 0 0 0 0 21 0 0 0 0 62 0 0 138 0 0 0 0 0 0 0 4 5 103 646 0 +LFQ 138 0 0 0 0 0 0 0 0 0 0 0 0 0 17 0 0 0 0 0 0 4 0 0 0 0 0 21 0 0 0 0 65 1 0 142 1 0 0 0 0 0 0 2 5 111 613 0 +LFQ 139 0 0 0 0 0 0 0 0 0 0 0 0 0 15 0 0 0 0 0 0 10 0 0 0 0 0 15 0 0 0 0 66 0 0 134 0 0 0 1 0 0 0 3 4 109 591 0 +LFQ 140 0 0 0 0 0 0 0 0 0 0 0 0 0 25 0 0 0 0 0 0 5 0 0 0 0 0 12 0 0 0 0 59 0 0 127 0 0 0 1 0 0 0 2 5 97 583 0 +LFQ 141 0 0 0 0 0 0 0 0 0 0 0 0 0 18 0 0 0 0 0 0 5 0 0 0 0 0 16 0 0 0 0 64 0 0 119 0 0 0 0 0 0 0 2 6 84 574 0 +LFQ 142 0 0 0 0 0 0 0 0 0 0 0 0 0 20 0 0 0 1 0 0 3 0 0 0 0 0 20 0 0 0 0 47 0 0 117 0 0 0 1 0 0 0 1 2 88 547 0 +LFQ 143 0 0 5 1 0 0 0 0 0 0 0 1 0 71 0 1 0 5 4 0 12 0 0 0 0 3 34 0 0 0 0 52 18 1 36 2 1 0 5 0 0 1 13 14 124 416 0 +# GC Content of first fragments. Use `grep ^GCF | cut -f 2-` to extract this part. +GCF 7.29 0 +GCF 15.08 1 +GCF 17.09 0 +GCF 18.84 1 +GCF 19.35 2 +GCF 19.85 1 +GCF 20.35 0 +GCF 20.85 5 +GCF 21.36 4 +GCF 21.86 6 +GCF 22.36 20 +GCF 22.86 14 +GCF 23.37 23 +GCF 23.87 57 +GCF 24.37 86 +GCF 24.87 83 +GCF 25.38 107 +GCF 25.88 104 +GCF 26.38 78 +GCF 26.88 77 +GCF 27.39 125 +GCF 27.89 201 +GCF 28.39 258 +GCF 28.89 336 +GCF 29.40 353 +GCF 29.90 254 +GCF 30.40 213 +GCF 30.90 217 +GCF 31.41 195 +GCF 31.91 203 +GCF 32.41 177 +GCF 32.91 153 +GCF 33.42 160 +GCF 33.92 130 +GCF 34.42 104 +GCF 34.92 82 +GCF 35.43 96 +GCF 35.93 100 +GCF 36.43 106 +GCF 36.93 136 +GCF 37.44 138 +GCF 37.94 113 +GCF 38.44 73 +GCF 38.94 35 +GCF 39.45 16 +GCF 39.95 12 +GCF 40.45 8 +GCF 40.95 11 +GCF 41.46 13 +GCF 42.21 11 +GCF 42.96 13 +GCF 43.47 15 +GCF 43.97 12 +GCF 44.47 13 +GCF 44.97 12 +GCF 45.48 10 +GCF 45.98 12 +GCF 46.48 10 +GCF 46.98 5 +GCF 47.49 13 +GCF 47.99 14 +GCF 48.49 13 +GCF 49.25 4 +GCF 50.00 3 +GCF 50.50 2 +GCF 51.01 1 +# GC Content of last fragments. Use `grep ^GCL | cut -f 2-` to extract this part. +GCL 5.53 0 +GCL 11.31 1 +GCL 13.07 0 +GCL 14.82 1 +GCL 15.83 0 +GCL 17.09 2 +GCL 18.09 1 +GCL 18.84 0 +GCL 19.35 1 +GCL 19.85 0 +GCL 20.35 2 +GCL 20.85 6 +GCL 21.36 5 +GCL 21.86 6 +GCL 22.61 16 +GCL 23.37 24 +GCL 23.87 58 +GCL 24.37 94 +GCL 24.87 90 +GCL 25.38 110 +GCL 25.88 106 +GCL 26.38 85 +GCL 26.88 79 +GCL 27.39 120 +GCL 27.89 210 +GCL 28.39 262 +GCL 29.15 342 +GCL 29.90 254 +GCL 30.40 211 +GCL 30.90 227 +GCL 31.41 196 +GCL 31.91 203 +GCL 32.41 176 +GCL 32.91 155 +GCL 33.42 153 +GCL 33.92 122 +GCL 34.42 94 +GCL 34.92 75 +GCL 35.43 96 +GCL 35.93 100 +GCL 36.43 103 +GCL 37.19 141 +GCL 37.94 111 +GCL 38.44 65 +GCL 38.94 34 +GCL 39.45 19 +GCL 39.95 10 +GCL 40.45 5 +GCL 40.95 10 +GCL 41.46 16 +GCL 41.96 12 +GCL 42.46 10 +GCL 42.96 13 +GCL 43.47 15 +GCL 44.22 12 +GCL 45.23 10 +GCL 45.98 13 +GCL 46.48 10 +GCL 46.98 5 +GCL 47.49 12 +GCL 47.99 13 +GCL 48.49 12 +GCL 49.50 3 +GCL 50.50 2 +GCL 51.01 1 +# ACGT content per cycle. Use `grep ^GCC | cut -f 2-` to extract this part. The columns are: cycle; A,C,G,T base counts as a percentage of all A/C/G/T bases [%]; and N and O counts as a percentage of all A/C/G/T bases [%] +GCC 1 33.73 15.23 16.08 34.95 0.05 0.00 +GCC 2 33.26 15.50 17.56 33.68 0.07 0.00 +GCC 3 34.59 15.25 15.74 34.41 0.04 0.00 +GCC 4 34.25 15.59 14.81 35.35 0.07 0.00 +GCC 5 34.04 15.51 14.65 35.80 0.04 0.00 +GCC 6 34.55 15.18 14.90 35.37 0.07 0.00 +GCC 7 33.79 15.99 15.21 35.00 0.04 0.00 +GCC 8 33.09 15.24 16.00 35.66 0.11 0.00 +GCC 9 34.44 14.22 15.75 35.58 0.07 0.00 +GCC 10 34.60 13.75 16.53 35.12 0.07 0.00 +GCC 11 34.33 14.12 15.87 35.68 0.05 0.00 +GCC 12 34.09 13.63 16.18 36.10 0.12 0.00 +GCC 13 33.74 13.78 18.14 34.34 0.07 0.00 +GCC 14 33.91 13.41 17.33 35.35 0.07 0.00 +GCC 15 33.43 15.10 16.95 34.52 0.12 0.00 +GCC 16 35.00 14.64 16.58 33.78 0.14 0.00 +GCC 17 33.22 13.89 16.64 36.25 0.11 0.00 +GCC 18 33.53 14.44 17.40 34.63 0.09 0.00 +GCC 19 32.82 14.78 16.44 35.96 0.09 0.00 +GCC 20 32.91 14.01 15.13 37.95 0.09 0.00 +GCC 21 32.48 14.01 15.89 37.62 0.07 0.00 +GCC 22 33.20 13.15 17.02 36.64 0.11 0.00 +GCC 23 33.37 13.15 16.96 36.52 0.11 0.00 +GCC 24 33.42 14.32 16.55 35.71 0.09 0.00 +GCC 25 34.75 14.16 15.39 35.69 0.14 0.00 +GCC 26 34.77 14.74 15.86 34.63 0.09 0.00 +GCC 27 34.44 14.62 16.41 34.53 0.11 0.00 +GCC 28 33.81 14.80 16.60 34.79 0.14 0.00 +GCC 29 32.78 15.11 17.35 34.76 0.07 0.00 +GCC 30 33.59 14.01 16.28 36.11 0.07 0.00 +GCC 31 31.51 15.00 16.46 37.03 0.14 0.00 +GCC 32 32.85 15.33 15.53 36.29 0.11 0.00 +GCC 33 33.03 14.83 14.74 37.40 0.18 0.00 +GCC 34 31.55 14.77 15.43 38.25 0.14 0.00 +GCC 35 31.62 13.70 17.11 37.56 0.12 0.00 +GCC 36 33.35 13.99 16.95 35.71 0.11 0.00 +GCC 37 34.93 13.37 15.30 36.40 0.11 0.00 +GCC 38 32.95 15.35 16.29 35.40 0.09 0.00 +GCC 39 32.83 14.42 16.44 36.31 0.14 0.00 +GCC 40 32.53 14.47 17.47 35.53 0.11 0.00 +GCC 41 32.82 14.00 16.47 36.71 0.11 0.00 +GCC 42 32.08 15.16 14.97 37.80 0.11 0.00 +GCC 43 32.53 14.39 15.71 37.36 0.09 0.00 +GCC 44 33.32 13.78 16.18 36.71 0.16 0.00 +GCC 45 34.93 14.79 15.20 35.07 0.14 0.00 +GCC 46 33.30 14.49 15.91 36.30 0.09 0.00 +GCC 47 33.27 14.41 15.58 36.74 0.16 0.00 +GCC 48 33.61 13.94 16.09 36.36 0.14 0.00 +GCC 49 33.68 14.10 16.10 36.13 0.09 0.00 +GCC 50 34.40 14.52 16.02 35.06 0.12 0.00 +GCC 51 32.24 16.31 15.61 35.85 0.14 0.00 +GCC 52 30.68 14.79 17.46 37.07 0.09 0.00 +GCC 53 32.12 15.12 17.20 35.56 0.07 0.00 +GCC 54 32.50 15.04 16.66 35.81 0.12 0.00 +GCC 55 31.01 16.58 17.31 35.10 0.16 0.00 +GCC 56 31.83 15.29 16.65 36.23 0.12 0.00 +GCC 57 31.48 15.42 15.83 37.27 0.14 0.00 +GCC 58 33.02 14.52 15.02 37.44 0.18 0.00 +GCC 59 31.54 14.75 16.56 37.15 0.12 0.00 +GCC 60 30.03 14.95 16.88 38.14 0.12 0.00 +GCC 61 30.74 15.64 16.51 37.12 0.12 0.00 +GCC 62 31.90 16.15 15.32 36.63 0.11 0.00 +GCC 63 32.40 14.53 15.56 37.51 0.18 0.00 +GCC 64 30.36 15.07 16.43 38.15 0.13 0.00 +GCC 65 30.70 14.89 16.59 37.82 0.16 0.00 +GCC 66 30.54 16.30 15.44 37.71 0.07 0.00 +GCC 67 30.73 16.55 14.74 37.98 0.09 0.00 +GCC 68 30.99 14.80 16.96 37.26 0.05 0.00 +GCC 69 29.97 15.79 16.10 38.14 0.07 0.00 +GCC 70 29.88 15.26 16.44 38.42 0.07 0.00 +GCC 71 29.89 15.82 16.20 38.08 0.05 0.00 +GCC 72 31.27 16.51 15.56 36.66 0.07 0.00 +GCC 73 30.37 15.27 15.12 39.24 0.04 0.00 +GCC 74 30.08 14.71 15.41 39.80 0.05 0.00 +GCC 75 30.67 14.94 14.61 39.78 0.09 0.00 +GCC 76 29.79 14.84 15.43 39.93 0.09 0.00 +GCC 77 29.44 14.93 16.17 39.47 0.04 0.00 +GCC 78 29.81 15.62 16.34 38.22 0.09 0.00 +GCC 79 31.83 14.51 14.84 38.82 0.06 0.00 +GCC 80 29.94 15.88 14.58 39.60 0.09 0.00 +GCC 81 31.19 15.92 15.18 37.71 0.06 0.00 +GCC 82 31.66 16.19 15.34 36.81 0.04 0.00 +GCC 83 31.18 16.06 13.76 39.01 0.06 0.00 +GCC 84 30.96 16.06 12.89 40.10 0.04 0.00 +GCC 85 30.01 15.23 13.24 41.53 0.10 0.00 +GCC 86 30.81 15.86 14.66 38.68 0.02 0.00 +GCC 87 30.47 16.15 12.72 40.66 0.04 0.00 +GCC 88 31.11 16.50 12.35 40.04 0.02 0.00 +GCC 89 30.24 15.85 11.73 42.19 0.06 0.00 +GCC 90 31.22 14.87 12.36 41.55 0.02 0.00 +GCC 91 30.23 17.01 12.89 39.87 0.02 0.00 +GCC 92 31.36 14.43 13.43 40.78 0.06 0.00 +GCC 93 32.06 15.47 13.66 38.81 0.02 0.00 +GCC 94 32.56 14.86 14.96 37.62 0.02 0.00 +GCC 95 31.75 15.88 14.36 38.01 0.02 0.00 +GCC 96 32.25 16.62 12.71 38.43 0.02 0.00 +GCC 97 31.78 16.01 15.10 37.11 0.04 0.00 +GCC 98 31.47 15.10 15.78 37.65 0.02 0.00 +GCC 99 32.77 16.08 14.79 36.36 0.02 0.00 +GCC 100 31.18 16.81 13.53 38.48 0.00 0.00 +GCC 101 32.53 15.79 12.82 38.85 0.05 0.00 +GCC 102 34.85 14.89 13.38 36.89 0.02 0.00 +GCC 103 33.09 15.40 14.57 36.93 0.02 0.00 +GCC 104 34.25 13.60 14.78 37.37 0.00 0.00 +GCC 105 34.82 15.70 13.12 36.37 0.00 0.00 +GCC 106 36.36 14.74 13.60 35.30 0.00 0.00 +GCC 107 34.51 15.11 13.95 36.43 0.03 0.00 +GCC 108 32.09 16.88 14.37 36.65 0.05 0.00 +GCC 109 33.64 14.78 14.09 37.49 0.03 0.00 +GCC 110 35.15 14.14 15.88 34.83 0.03 0.00 +GCC 111 32.46 15.41 15.41 36.73 0.00 0.00 +GCC 112 30.38 16.47 14.49 38.66 0.03 0.00 +GCC 113 30.38 16.75 13.57 39.30 0.00 0.00 +GCC 114 31.95 16.24 15.52 36.29 0.00 0.00 +GCC 115 31.65 16.61 16.00 35.74 0.03 0.00 +GCC 116 32.63 15.92 13.73 37.72 0.03 0.00 +GCC 117 30.30 15.50 13.72 40.48 0.00 0.00 +GCC 118 31.59 15.86 13.78 38.78 0.00 0.00 +GCC 119 29.82 15.70 15.86 38.62 0.06 0.00 +GCC 120 32.51 15.42 14.86 37.21 0.00 0.00 +GCC 121 30.50 16.06 15.80 37.64 0.00 0.00 +GCC 122 31.72 16.32 15.67 36.29 0.00 0.00 +GCC 123 31.69 15.51 15.51 37.29 0.00 0.00 +GCC 124 31.50 15.71 16.74 36.04 0.00 0.00 +GCC 125 31.78 15.14 16.82 36.26 0.00 0.00 +GCC 126 33.98 16.26 14.92 34.84 0.00 0.00 +GCC 127 36.59 13.61 14.98 34.83 0.04 0.00 +GCC 128 31.58 16.32 16.04 36.07 0.04 0.00 +GCC 129 32.15 14.51 15.23 38.10 0.00 0.00 +GCC 130 32.32 14.77 14.65 38.26 0.04 0.00 +GCC 131 33.14 14.71 15.77 36.39 0.00 0.00 +GCC 132 31.94 14.32 16.61 37.13 0.00 0.00 +GCC 133 34.86 13.79 15.59 35.76 0.00 0.00 +GCC 134 32.53 15.71 18.55 33.21 0.00 0.00 +GCC 135 30.59 14.64 17.77 37.00 0.00 0.00 +GCC 136 31.60 13.15 18.79 36.46 0.00 0.00 +GCC 137 31.35 13.59 18.50 36.56 0.00 0.00 +GCC 138 33.20 12.92 20.58 33.30 0.00 0.00 +GCC 139 33.17 13.39 18.15 35.29 0.00 0.00 +GCC 140 33.84 12.65 17.74 35.76 0.00 0.00 +GCC 141 32.37 12.77 17.18 37.68 0.00 0.00 +GCC 142 33.29 12.32 17.83 36.55 0.00 0.00 +GCC 143 32.56 11.24 16.09 40.11 0.37 0.00 +# ACGT content per cycle, read oriented. Use `grep ^GCT | cut -f 2-` to extract this part. The columns are: cycle; A,C,G,T base counts as a percentage of all A/C/G/T bases [%] +GCT 1 36.58 12.84 18.48 32.10 +GCT 2 36.22 13.80 19.26 30.72 +GCT 3 37.11 13.23 17.77 31.90 +GCT 4 36.98 13.14 17.26 32.62 +GCT 5 38.07 12.38 17.78 31.77 +GCT 6 37.81 12.27 17.81 32.10 +GCT 7 36.21 12.91 18.30 32.59 +GCT 8 35.72 13.64 17.60 33.04 +GCT 9 36.34 12.08 17.90 33.68 +GCT 10 37.67 12.10 18.18 32.05 +GCT 11 36.48 12.75 17.24 33.53 +GCT 12 36.45 12.69 17.13 33.74 +GCT 13 35.19 14.35 17.58 32.88 +GCT 14 35.86 12.95 17.79 33.40 +GCT 15 36.82 14.00 18.05 31.13 +GCT 16 37.26 13.01 18.21 31.52 +GCT 17 37.51 13.08 17.46 31.96 +GCT 18 35.16 14.12 17.72 33.00 +GCT 19 35.16 14.78 16.44 33.62 +GCT 20 34.03 13.30 15.84 36.83 +GCT 21 35.12 12.75 17.15 34.98 +GCT 22 34.87 13.08 17.09 34.97 +GCT 23 34.23 13.27 16.84 35.66 +GCT 24 34.10 13.18 17.69 35.04 +GCT 25 34.81 13.03 16.52 35.64 +GCT 26 34.86 13.54 17.07 34.54 +GCT 27 34.40 13.54 17.49 34.56 +GCT 28 33.32 13.45 17.94 35.29 +GCT 29 34.23 14.47 17.99 33.31 +GCT 30 34.50 13.53 16.76 35.21 +GCT 31 35.20 13.78 17.68 33.34 +GCT 32 32.78 14.18 16.68 36.36 +GCT 33 34.98 13.09 16.48 35.45 +GCT 34 33.88 13.58 16.62 35.92 +GCT 35 33.39 14.11 16.71 35.79 +GCT 36 32.46 13.61 17.32 36.60 +GCT 37 34.27 12.07 16.60 37.06 +GCT 38 34.06 13.54 18.10 34.30 +GCT 39 33.98 13.46 17.40 35.16 +GCT 40 33.55 15.02 16.92 34.52 +GCT 41 35.20 14.57 15.90 34.33 +GCT 42 34.00 13.72 16.40 35.88 +GCT 43 34.03 14.00 16.10 35.87 +GCT 44 34.12 14.23 15.74 35.91 +GCT 45 35.14 14.83 15.17 34.86 +GCT 46 34.12 15.13 15.27 35.49 +GCT 47 34.39 14.60 15.38 35.62 +GCT 48 34.99 14.97 15.06 34.97 +GCT 49 35.63 14.56 15.64 34.17 +GCT 50 36.43 14.61 15.93 33.03 +GCT 51 34.32 15.65 16.27 33.77 +GCT 52 35.36 16.62 15.63 32.39 +GCT 53 35.01 15.21 17.11 32.68 +GCT 54 35.63 15.93 15.77 32.67 +GCT 55 33.93 16.85 17.04 32.18 +GCT 56 36.03 15.86 16.08 32.03 +GCT 57 35.72 15.46 15.79 33.03 +GCT 58 36.44 14.57 14.96 34.02 +GCT 59 34.96 16.46 14.85 33.73 +GCT 60 34.75 16.61 15.22 33.42 +GCT 61 34.73 16.81 15.33 33.13 +GCT 62 36.49 15.41 16.07 32.04 +GCT 63 35.69 14.65 15.44 34.22 +GCT 64 33.57 15.32 16.17 34.94 +GCT 65 35.42 15.12 16.36 33.09 +GCT 66 34.43 15.55 16.19 33.82 +GCT 67 36.56 16.42 14.86 32.15 +GCT 68 35.24 16.51 15.25 33.00 +GCT 69 35.89 16.19 15.70 32.22 +GCT 70 35.74 15.39 16.31 32.56 +GCT 71 35.12 15.48 16.55 32.86 +GCT 72 36.28 16.36 15.71 31.65 +GCT 73 36.49 15.27 15.12 33.12 +GCT 74 36.00 15.06 15.06 33.88 +GCT 75 35.02 14.25 15.31 35.42 +GCT 76 33.67 15.61 14.66 36.06 +GCT 77 33.99 15.78 15.32 34.91 +GCT 78 34.47 16.31 15.66 33.56 +GCT 79 35.53 15.03 14.32 35.12 +GCT 80 34.22 16.55 13.91 35.32 +GCT 81 34.06 16.94 14.16 34.84 +GCT 82 33.49 16.12 15.41 34.98 +GCT 83 33.37 16.39 13.43 36.82 +GCT 84 34.51 16.41 12.54 36.54 +GCT 85 35.36 15.54 12.92 36.18 +GCT 86 35.87 16.56 13.95 33.61 +GCT 87 36.14 16.15 12.72 34.99 +GCT 88 34.86 15.78 13.07 36.29 +GCT 89 36.49 15.48 12.09 35.94 +GCT 90 35.66 14.38 12.85 37.11 +GCT 91 34.51 16.17 13.74 35.58 +GCT 92 36.35 15.74 12.12 35.79 +GCT 93 37.04 15.83 13.30 33.82 +GCT 94 36.03 16.49 13.33 34.16 +GCT 95 35.13 15.86 14.38 34.62 +GCT 96 36.29 15.90 13.43 34.39 +GCT 97 34.38 16.04 15.07 34.51 +GCT 98 34.97 16.60 14.28 34.15 +GCT 99 35.63 17.13 13.73 33.51 +GCT 100 35.04 17.97 12.37 34.62 +GCT 101 36.07 16.41 12.21 35.31 +GCT 102 35.95 15.95 12.32 35.78 +GCT 103 35.69 16.42 13.55 34.33 +GCT 104 36.71 15.83 12.54 34.92 +GCT 105 35.89 16.97 11.85 35.29 +GCT 106 37.78 16.00 12.33 33.88 +GCT 107 34.71 16.75 12.31 36.23 +GCT 108 33.61 16.91 14.35 35.13 +GCT 109 33.72 15.23 13.64 37.41 +GCT 110 33.73 16.96 13.06 36.25 +GCT 111 33.42 18.23 12.59 35.77 +GCT 112 33.35 17.52 13.44 35.69 +GCT 113 33.19 16.78 13.54 36.49 +GCT 114 31.64 17.33 14.43 36.61 +GCT 115 31.76 18.46 14.16 35.62 +GCT 116 33.38 16.35 13.31 36.97 +GCT 117 31.41 16.15 13.07 39.37 +GCT 118 34.41 16.99 12.65 35.95 +GCT 119 32.94 19.20 12.36 35.51 +GCT 120 33.20 17.23 13.05 36.52 +GCT 121 32.36 18.62 13.24 35.78 +GCT 122 31.69 18.88 13.12 36.32 +GCT 123 30.82 18.61 12.42 38.16 +GCT 124 30.51 19.76 12.70 37.03 +GCT 125 32.33 18.89 13.07 35.71 +GCT 126 31.78 18.09 13.08 37.05 +GCT 127 35.79 16.65 11.93 35.63 +GCT 128 31.73 17.56 14.79 35.91 +GCT 129 32.59 17.07 12.67 37.66 +GCT 130 33.80 17.72 11.70 36.78 +GCT 131 34.32 17.75 12.72 35.21 +GCT 132 33.96 18.59 12.35 35.11 +GCT 133 34.23 15.86 13.52 36.39 +GCT 134 30.65 19.64 14.62 35.08 +GCT 135 32.13 18.62 13.80 35.45 +GCT 136 30.97 18.93 13.01 37.09 +GCT 137 30.41 17.96 14.14 37.50 +GCT 138 30.75 20.17 13.33 35.75 +GCT 139 32.65 18.89 12.65 35.82 +GCT 140 33.73 18.57 11.83 35.87 +GCT 141 32.09 16.33 13.62 37.97 +GCT 142 32.41 16.47 13.68 37.44 +GCT 143 32.43 16.71 10.63 40.23 +# ACGT content per cycle for first fragments. Use `grep ^FBC | cut -f 2-` to extract this part. The columns are: cycle; A,C,G,T base counts as a percentage of all A/C/G/T bases [%]; and N and O counts as a percentage of all A/C/G/T bases [%] +FBC 1 34.09 15.25 16.11 34.55 0.07 0.00 +FBC 2 33.51 15.96 16.74 33.79 0.04 0.00 +FBC 3 34.70 14.46 15.92 34.92 0.00 0.00 +FBC 4 34.08 15.92 14.79 35.21 0.04 0.00 +FBC 5 34.42 15.38 15.03 35.16 0.00 0.00 +FBC 6 34.72 14.72 15.14 35.43 0.04 0.00 +FBC 7 34.46 15.88 15.31 34.35 0.00 0.00 +FBC 8 33.88 15.04 15.75 35.33 0.07 0.00 +FBC 9 34.04 14.61 15.78 35.57 0.04 0.00 +FBC 10 34.82 13.16 16.95 35.07 0.04 0.00 +FBC 11 34.10 14.14 15.60 36.16 0.00 0.00 +FBC 12 34.68 13.35 16.19 35.78 0.14 0.00 +FBC 13 33.81 14.40 17.81 33.98 0.07 0.00 +FBC 14 34.11 13.40 17.48 35.00 0.04 0.00 +FBC 15 33.33 15.55 16.68 34.43 0.14 0.00 +FBC 16 34.22 14.52 17.32 33.94 0.14 0.00 +FBC 17 32.82 13.27 16.64 37.26 0.11 0.00 +FBC 18 33.71 15.08 17.28 33.92 0.11 0.00 +FBC 19 32.67 14.83 16.42 36.08 0.07 0.00 +FBC 20 32.74 14.44 15.04 37.78 0.07 0.00 +FBC 21 32.29 13.97 16.06 37.68 0.00 0.00 +FBC 22 32.82 13.38 17.28 36.52 0.11 0.00 +FBC 23 33.43 12.53 16.93 37.12 0.11 0.00 +FBC 24 33.63 14.51 16.28 35.58 0.07 0.00 +FBC 25 34.69 13.99 15.06 36.26 0.18 0.00 +FBC 26 35.15 15.40 15.29 34.16 0.07 0.00 +FBC 27 34.00 14.58 16.11 35.31 0.11 0.00 +FBC 28 33.45 14.60 17.37 34.59 0.18 0.00 +FBC 29 32.27 15.21 18.12 34.40 0.04 0.00 +FBC 30 34.18 14.08 15.89 35.85 0.04 0.00 +FBC 31 31.26 15.10 16.91 36.73 0.18 0.00 +FBC 32 33.51 15.26 15.16 36.07 0.11 0.00 +FBC 33 33.03 15.32 14.54 37.11 0.25 0.00 +FBC 34 30.80 14.42 15.63 39.15 0.18 0.00 +FBC 35 31.00 14.10 17.86 37.04 0.14 0.00 +FBC 36 33.46 13.80 16.75 35.98 0.07 0.00 +FBC 37 35.46 13.38 15.19 35.96 0.11 0.00 +FBC 38 33.43 14.98 16.11 35.49 0.07 0.00 +FBC 39 32.84 14.48 16.90 35.78 0.11 0.00 +FBC 40 33.19 14.16 17.96 34.68 0.11 0.00 +FBC 41 32.13 13.99 16.79 37.10 0.11 0.00 +FBC 42 32.03 15.48 14.42 38.07 0.11 0.00 +FBC 43 33.26 14.41 15.65 36.67 0.07 0.00 +FBC 44 33.84 13.58 15.85 36.72 0.21 0.00 +FBC 45 34.60 14.71 15.52 35.17 0.14 0.00 +FBC 46 32.88 14.74 15.98 36.40 0.07 0.00 +FBC 47 32.79 14.83 15.40 36.98 0.21 0.00 +FBC 48 33.99 14.54 15.71 35.76 0.18 0.00 +FBC 49 33.45 13.96 16.26 36.33 0.07 0.00 +FBC 50 33.69 14.62 16.08 35.61 0.14 0.00 +FBC 51 32.16 16.15 15.44 36.25 0.14 0.00 +FBC 52 30.94 14.69 16.75 37.62 0.07 0.00 +FBC 53 32.23 15.12 17.22 35.43 0.04 0.00 +FBC 54 32.55 15.38 16.52 35.54 0.14 0.00 +FBC 55 30.94 17.04 17.65 34.37 0.21 0.00 +FBC 56 31.92 15.18 17.10 35.80 0.14 0.00 +FBC 57 31.60 14.82 15.96 37.62 0.14 0.00 +FBC 58 33.31 14.59 14.87 37.23 0.25 0.00 +FBC 59 31.90 15.29 16.25 36.56 0.14 0.00 +FBC 60 29.60 15.34 17.19 37.87 0.14 0.00 +FBC 61 30.86 15.13 16.77 37.25 0.14 0.00 +FBC 62 31.18 16.18 15.79 36.86 0.11 0.00 +FBC 63 33.06 14.31 15.67 36.96 0.25 0.00 +FBC 64 30.43 15.11 16.65 37.81 0.14 0.00 +FBC 65 30.88 14.73 16.34 38.05 0.21 0.00 +FBC 66 30.22 16.45 15.38 37.96 0.07 0.00 +FBC 67 31.04 16.65 14.68 37.64 0.11 0.00 +FBC 68 31.15 14.73 17.21 36.90 0.04 0.00 +FBC 69 29.94 16.05 16.23 37.78 0.07 0.00 +FBC 70 29.91 15.10 16.43 38.56 0.04 0.00 +FBC 71 30.05 15.37 16.24 38.34 0.04 0.00 +FBC 72 30.68 16.45 16.19 36.67 0.04 0.00 +FBC 73 29.85 15.33 15.36 39.46 0.04 0.00 +FBC 74 30.33 15.04 15.36 39.27 0.00 0.00 +FBC 75 31.03 15.04 14.16 39.77 0.11 0.00 +FBC 76 30.10 14.81 15.21 39.88 0.11 0.00 +FBC 77 28.60 14.89 15.85 40.66 0.00 0.00 +FBC 78 29.78 15.54 16.28 38.40 0.15 0.00 +FBC 79 31.34 14.87 15.09 38.70 0.07 0.00 +FBC 80 29.67 15.62 14.91 39.80 0.11 0.00 +FBC 81 30.87 16.55 15.45 37.14 0.08 0.00 +FBC 82 31.50 16.42 15.39 36.69 0.00 0.00 +FBC 83 30.02 16.62 14.13 39.23 0.08 0.00 +FBC 84 30.85 15.31 13.18 40.66 0.04 0.00 +FBC 85 30.17 15.47 12.93 41.42 0.16 0.00 +FBC 86 31.37 15.78 14.76 38.08 0.04 0.00 +FBC 87 30.56 16.41 12.64 40.39 0.04 0.00 +FBC 88 30.67 16.76 13.03 39.53 0.00 0.00 +FBC 89 29.71 16.27 11.77 42.25 0.04 0.00 +FBC 90 31.34 14.16 12.48 42.02 0.00 0.00 +FBC 91 30.60 16.14 12.93 40.32 0.00 0.00 +FBC 92 30.78 14.52 13.14 41.56 0.04 0.00 +FBC 93 32.80 15.03 13.81 38.36 0.00 0.00 +FBC 94 32.91 14.97 14.75 37.37 0.00 0.00 +FBC 95 32.13 15.79 14.71 37.37 0.04 0.00 +FBC 96 31.62 16.77 13.01 38.60 0.00 0.00 +FBC 97 32.07 15.65 15.47 36.81 0.00 0.00 +FBC 98 32.11 14.85 15.80 37.24 0.00 0.00 +FBC 99 33.12 15.64 15.32 35.92 0.00 0.00 +FBC 100 30.84 16.23 13.95 38.98 0.00 0.00 +FBC 101 33.24 15.62 12.49 38.65 0.00 0.00 +FBC 102 34.92 14.41 13.59 37.08 0.00 0.00 +FBC 103 32.65 15.33 14.94 37.08 0.00 0.00 +FBC 104 34.58 13.08 14.46 37.88 0.00 0.00 +FBC 105 34.40 15.80 13.45 36.35 0.00 0.00 +FBC 106 35.66 15.10 13.22 36.02 0.00 0.00 +FBC 107 34.69 15.42 13.87 36.02 0.00 0.00 +FBC 108 31.83 17.43 14.35 36.39 0.00 0.00 +FBC 109 32.91 15.23 13.91 37.95 0.00 0.00 +FBC 110 35.22 13.71 15.48 35.59 0.00 0.00 +FBC 111 32.48 15.28 14.95 37.29 0.00 0.00 +FBC 112 30.55 16.69 14.75 38.01 0.00 0.00 +FBC 113 30.42 17.35 14.25 37.97 0.00 0.00 +FBC 114 32.26 16.85 15.53 35.37 0.00 0.00 +FBC 115 31.07 17.14 15.68 36.10 0.00 0.00 +FBC 116 32.41 16.39 13.31 37.89 0.00 0.00 +FBC 117 30.23 15.83 13.61 40.33 0.00 0.00 +FBC 118 30.92 15.96 13.64 39.47 0.00 0.00 +FBC 119 29.50 16.07 15.55 38.88 0.06 0.00 +FBC 120 33.18 14.88 15.08 36.87 0.00 0.00 +FBC 121 30.76 16.15 16.15 36.94 0.00 0.00 +FBC 122 31.86 16.71 15.69 35.74 0.00 0.00 +FBC 123 32.17 15.53 14.97 37.33 0.00 0.00 +FBC 124 31.68 15.84 16.48 36.01 0.00 0.00 +FBC 125 32.43 14.87 16.69 36.01 0.00 0.00 +FBC 126 35.10 15.72 14.45 34.73 0.00 0.00 +FBC 127 35.80 13.82 15.11 35.27 0.00 0.00 +FBC 128 32.06 16.34 15.79 35.81 0.00 0.00 +FBC 129 32.05 14.34 15.54 38.06 0.00 0.00 +FBC 130 31.15 14.75 14.75 39.34 0.00 0.00 +FBC 131 32.63 14.15 16.36 36.86 0.00 0.00 +FBC 132 31.81 14.54 16.48 37.18 0.00 0.00 +FBC 133 35.14 13.69 15.23 35.95 0.00 0.00 +FBC 134 31.78 16.30 18.50 33.42 0.00 0.00 +FBC 135 30.21 14.35 17.54 37.90 0.00 0.00 +FBC 136 31.59 12.75 18.94 36.71 0.00 0.00 +FBC 137 30.85 14.23 19.60 35.32 0.00 0.00 +FBC 138 33.71 13.11 20.80 32.38 0.00 0.00 +FBC 139 33.12 13.06 18.37 35.46 0.00 0.00 +FBC 140 33.74 12.75 17.47 36.04 0.00 0.00 +FBC 141 32.65 13.61 17.35 36.39 0.00 0.00 +FBC 142 31.63 12.84 19.02 36.50 0.00 0.00 +FBC 143 31.49 11.32 16.11 41.08 0.12 0.00 +# ACGT raw counters for first fragments. Use `grep ^FTC | cut -f 2-` to extract this part. The columns are: A,C,G,T,N base counters +FTC 108689 50470 52527 124018 240 +# ACGT content per cycle for last fragments. Use `grep ^LBC | cut -f 2-` to extract this part. The columns are: cycle; A,C,G,T base counts as a percentage of all A/C/G/T bases [%]; and N and O counts as a percentage of all A/C/G/T bases [%] +LBC 1 33.37 15.21 16.06 35.35 0.04 0.00 +LBC 2 33.00 15.05 18.38 33.57 0.11 0.00 +LBC 3 34.48 16.03 15.57 33.91 0.07 0.00 +LBC 4 34.42 15.26 14.83 35.49 0.11 0.00 +LBC 5 33.66 15.64 14.26 36.43 0.07 0.00 +LBC 6 34.39 15.65 14.66 35.31 0.11 0.00 +LBC 7 33.13 16.11 15.11 35.65 0.07 0.00 +LBC 8 32.30 15.44 16.26 36.00 0.14 0.00 +LBC 9 34.85 13.84 15.72 35.59 0.11 0.00 +LBC 10 34.39 14.34 16.11 35.17 0.11 0.00 +LBC 11 34.56 14.09 16.15 35.20 0.11 0.00 +LBC 12 33.50 13.91 16.18 36.41 0.11 0.00 +LBC 13 33.66 13.16 18.48 34.69 0.07 0.00 +LBC 14 33.71 13.41 17.18 35.70 0.11 0.00 +LBC 15 33.53 14.66 17.21 34.60 0.11 0.00 +LBC 16 35.78 14.77 15.83 33.62 0.14 0.00 +LBC 17 33.61 14.51 16.64 35.24 0.11 0.00 +LBC 18 33.35 13.80 17.52 35.33 0.07 0.00 +LBC 19 32.97 14.73 16.47 35.84 0.11 0.00 +LBC 20 33.07 13.59 15.22 38.11 0.11 0.00 +LBC 21 32.66 14.06 15.73 37.56 0.14 0.00 +LBC 22 33.57 12.92 16.75 36.76 0.11 0.00 +LBC 23 33.32 13.77 17.00 35.91 0.11 0.00 +LBC 24 33.22 14.12 16.82 35.84 0.11 0.00 +LBC 25 34.81 14.34 15.72 35.13 0.11 0.00 +LBC 26 34.39 14.09 16.43 35.10 0.11 0.00 +LBC 27 34.88 14.66 16.71 33.75 0.11 0.00 +LBC 28 34.17 15.01 15.83 34.99 0.11 0.00 +LBC 29 33.29 15.01 16.57 35.13 0.11 0.00 +LBC 30 33.00 13.95 16.68 36.37 0.11 0.00 +LBC 31 31.76 14.90 16.00 37.33 0.11 0.00 +LBC 32 32.19 15.40 15.90 36.52 0.11 0.00 +LBC 33 33.04 14.34 14.94 37.69 0.11 0.00 +LBC 34 32.30 15.12 15.23 37.34 0.11 0.00 +LBC 35 32.23 13.31 16.36 38.09 0.11 0.00 +LBC 36 33.24 14.17 17.15 35.44 0.14 0.00 +LBC 37 34.40 13.35 15.41 36.85 0.11 0.00 +LBC 38 32.48 15.73 16.47 35.32 0.11 0.00 +LBC 39 32.82 14.35 15.99 36.84 0.18 0.00 +LBC 40 31.88 14.77 16.97 36.39 0.11 0.00 +LBC 41 33.51 14.02 16.15 36.32 0.11 0.00 +LBC 42 32.13 14.84 15.51 37.52 0.11 0.00 +LBC 43 31.81 14.38 15.76 38.05 0.11 0.00 +LBC 44 32.80 13.99 16.51 36.71 0.11 0.00 +LBC 45 35.26 14.88 14.88 34.98 0.14 0.00 +LBC 46 33.72 14.24 15.83 36.21 0.11 0.00 +LBC 47 33.76 13.99 15.76 36.49 0.11 0.00 +LBC 48 33.23 13.35 16.47 36.95 0.11 0.00 +LBC 49 33.90 14.24 15.94 35.92 0.11 0.00 +LBC 50 35.11 14.43 15.96 34.51 0.11 0.00 +LBC 51 32.31 16.46 15.78 35.44 0.14 0.00 +LBC 52 30.43 14.90 18.17 36.51 0.11 0.00 +LBC 53 32.02 15.12 17.18 35.68 0.11 0.00 +LBC 54 32.44 14.69 16.79 36.07 0.11 0.00 +LBC 55 31.07 16.12 16.98 35.84 0.11 0.00 +LBC 56 31.74 15.41 16.19 36.65 0.11 0.00 +LBC 57 31.36 16.02 15.70 36.92 0.14 0.00 +LBC 58 32.74 14.45 15.16 37.65 0.11 0.00 +LBC 59 31.19 14.20 16.87 37.74 0.11 0.00 +LBC 60 30.46 14.57 16.57 38.40 0.11 0.00 +LBC 61 30.61 16.14 16.25 36.99 0.11 0.00 +LBC 62 32.62 16.13 14.85 36.40 0.11 0.00 +LBC 63 31.74 14.74 15.46 38.06 0.11 0.00 +LBC 64 30.29 15.02 16.20 38.48 0.11 0.00 +LBC 65 30.51 15.06 16.85 37.59 0.11 0.00 +LBC 66 30.87 16.15 15.51 37.46 0.07 0.00 +LBC 67 30.43 16.45 14.80 38.32 0.07 0.00 +LBC 68 30.82 14.87 16.70 37.61 0.07 0.00 +LBC 69 30.00 15.54 15.97 38.49 0.07 0.00 +LBC 70 29.85 15.43 16.44 38.28 0.11 0.00 +LBC 71 29.73 16.27 16.17 37.83 0.07 0.00 +LBC 72 31.86 16.56 14.93 36.65 0.11 0.00 +LBC 73 30.90 15.21 14.88 39.01 0.04 0.00 +LBC 74 29.84 14.39 15.45 40.32 0.11 0.00 +LBC 75 30.31 14.84 15.06 39.78 0.07 0.00 +LBC 76 29.49 14.87 15.64 39.99 0.07 0.00 +LBC 77 30.27 14.97 16.48 38.27 0.07 0.00 +LBC 78 29.84 15.70 16.41 38.05 0.04 0.00 +LBC 79 32.33 14.15 14.59 38.93 0.04 0.00 +LBC 80 30.21 16.14 14.26 39.40 0.08 0.00 +LBC 81 31.52 15.29 14.91 38.28 0.04 0.00 +LBC 82 31.82 15.97 15.28 36.93 0.08 0.00 +LBC 83 32.34 15.50 13.39 38.78 0.04 0.00 +LBC 84 31.07 16.81 12.60 39.53 0.04 0.00 +LBC 85 29.85 14.98 13.54 41.63 0.04 0.00 +LBC 86 30.25 15.93 14.55 39.27 0.00 0.00 +LBC 87 30.39 15.89 12.80 40.93 0.04 0.00 +LBC 88 31.56 16.24 11.67 40.54 0.04 0.00 +LBC 89 30.76 15.42 11.69 42.13 0.08 0.00 +LBC 90 31.10 15.59 12.23 41.08 0.04 0.00 +LBC 91 29.86 17.87 12.85 39.42 0.04 0.00 +LBC 92 31.93 14.35 13.72 40.00 0.08 0.00 +LBC 93 31.31 15.91 13.51 39.27 0.04 0.00 +LBC 94 32.21 14.75 15.17 37.87 0.04 0.00 +LBC 95 31.36 15.98 14.01 38.65 0.00 0.00 +LBC 96 32.88 16.46 12.40 38.25 0.04 0.00 +LBC 97 31.50 16.38 14.72 37.40 0.09 0.00 +LBC 98 30.84 15.35 15.76 38.06 0.05 0.00 +LBC 99 32.43 16.51 14.26 36.80 0.05 0.00 +LBC 100 31.52 17.39 13.11 37.98 0.00 0.00 +LBC 101 31.83 15.96 13.16 39.05 0.10 0.00 +LBC 102 34.77 15.37 13.16 36.70 0.05 0.00 +LBC 103 33.53 15.47 14.21 36.79 0.05 0.00 +LBC 104 33.92 14.11 15.09 36.87 0.00 0.00 +LBC 105 35.23 15.59 12.79 36.38 0.00 0.00 +LBC 106 37.06 14.38 13.97 34.58 0.00 0.00 +LBC 107 34.33 14.80 14.03 36.84 0.05 0.00 +LBC 108 32.36 16.34 14.40 36.91 0.10 0.00 +LBC 109 34.38 14.32 14.27 37.03 0.05 0.00 +LBC 110 35.09 14.56 16.28 34.07 0.05 0.00 +LBC 111 32.44 15.54 15.86 36.16 0.00 0.00 +LBC 112 30.20 16.24 14.24 39.32 0.06 0.00 +LBC 113 30.33 16.15 12.89 40.63 0.00 0.00 +LBC 114 31.65 15.62 15.51 37.22 0.00 0.00 +LBC 115 32.22 16.08 16.32 35.38 0.06 0.00 +LBC 116 32.85 15.46 14.14 37.55 0.06 0.00 +LBC 117 30.36 15.18 13.83 40.63 0.00 0.00 +LBC 118 32.25 15.75 13.93 38.08 0.00 0.00 +LBC 119 30.15 15.33 16.16 38.36 0.06 0.00 +LBC 120 31.85 15.96 14.64 37.56 0.00 0.00 +LBC 121 30.24 15.98 15.45 38.33 0.00 0.00 +LBC 122 31.59 15.93 15.66 36.83 0.00 0.00 +LBC 123 31.20 15.50 16.05 37.25 0.00 0.00 +LBC 124 31.33 15.59 17.01 36.07 0.00 0.00 +LBC 125 31.13 15.42 16.95 36.51 0.00 0.00 +LBC 126 32.86 16.80 15.38 34.95 0.00 0.00 +LBC 127 37.37 13.39 14.84 34.40 0.08 0.00 +LBC 128 31.10 16.29 16.29 36.32 0.08 0.00 +LBC 129 32.24 14.68 14.92 38.15 0.00 0.00 +LBC 130 33.50 14.79 14.54 37.17 0.08 0.00 +LBC 131 33.64 15.26 15.18 35.92 0.00 0.00 +LBC 132 32.08 14.11 16.74 37.07 0.00 0.00 +LBC 133 34.59 13.89 15.95 35.57 0.00 0.00 +LBC 134 33.27 15.13 18.60 33.00 0.00 0.00 +LBC 135 30.97 14.93 18.00 36.10 0.00 0.00 +LBC 136 31.60 13.54 18.64 36.22 0.00 0.00 +LBC 137 31.85 12.96 17.41 37.78 0.00 0.00 +LBC 138 32.69 12.73 20.37 34.22 0.00 0.00 +LBC 139 33.23 13.71 17.93 35.13 0.00 0.00 +LBC 140 33.95 12.55 18.01 35.48 0.00 0.00 +LBC 141 32.09 11.94 17.00 38.96 0.00 0.00 +LBC 142 34.95 11.81 16.65 36.60 0.00 0.00 +LBC 143 33.62 11.17 16.07 39.14 0.61 0.00 +# ACGT raw counters for last fragments. Use `grep ^LTC | cut -f 2-` to extract this part. The columns are: A,C,G,T,N base counters +LTC 108882 50371 52310 124355 269 +# Insert sizes. Use `grep ^IS | cut -f 2-` to extract this part. The columns are: insert size, pairs total, inward oriented pairs, outward oriented pairs, other pairs +IS 0 0 0 0 0 +IS 1 0 0 0 0 +IS 2 0 0 0 0 +IS 3 0 0 0 0 +IS 4 0 0 0 0 +IS 5 0 0 0 0 +IS 6 0 0 0 0 +IS 7 0 0 0 0 +IS 8 0 0 0 0 +IS 9 0 0 0 0 +IS 10 0 0 0 0 +IS 11 0 0 0 0 +IS 12 0 0 0 0 +IS 13 0 0 0 0 +IS 14 0 0 0 0 +IS 15 0 0 0 0 +IS 16 0 0 0 0 +IS 17 0 0 0 0 +IS 18 0 0 0 0 +IS 19 0 0 0 0 +IS 20 0 0 0 0 +IS 21 0 0 0 0 +IS 22 0 0 0 0 +IS 23 0 0 0 0 +IS 24 0 0 0 0 +IS 25 0 0 0 0 +IS 26 0 0 0 0 +IS 27 0 0 0 0 +IS 28 0 0 0 0 +IS 29 0 0 0 0 +IS 30 0 0 0 0 +IS 31 0 0 0 0 +IS 32 1 0 1 0 +IS 33 0 0 0 0 +IS 34 0 0 0 0 +IS 35 0 0 0 0 +IS 36 0 0 0 0 +IS 37 0 0 0 0 +IS 38 0 0 0 0 +IS 39 0 0 0 0 +IS 40 0 0 0 0 +IS 41 1 1 0 0 +IS 42 0 0 0 0 +IS 43 0 0 0 0 +IS 44 0 0 0 0 +IS 45 0 0 0 0 +IS 46 0 0 0 0 +IS 47 0 0 0 0 +IS 48 0 0 0 0 +IS 49 3 3 0 0 +IS 50 0 0 0 0 +IS 51 1 1 0 0 +IS 52 2 2 0 0 +IS 53 0 0 0 0 +IS 54 1 1 0 0 +IS 55 0 0 0 0 +IS 56 0 0 0 0 +IS 57 0 0 0 0 +IS 58 1 1 0 0 +IS 59 2 2 0 0 +IS 60 1 1 0 0 +IS 61 4 4 0 0 +IS 62 1 1 0 0 +IS 63 5 5 0 0 +IS 64 0 0 0 0 +IS 65 5 5 0 0 +IS 66 2 2 0 0 +IS 67 6 6 0 0 +IS 68 3 3 0 0 +IS 69 5 5 0 0 +IS 70 10 10 0 0 +IS 71 11 11 0 0 +IS 72 7 7 0 0 +IS 73 8 8 0 0 +IS 74 4 4 0 0 +IS 75 12 12 0 0 +IS 76 11 11 0 0 +IS 77 19 19 0 0 +IS 78 15 15 0 0 +IS 79 13 13 0 0 +IS 80 17 17 0 0 +IS 81 24 24 0 0 +IS 82 18 18 0 0 +IS 83 19 19 0 0 +IS 84 25 25 0 0 +IS 85 15 15 0 0 +IS 86 24 24 0 0 +IS 87 30 30 0 0 +IS 88 29 29 0 0 +IS 89 21 21 0 0 +IS 90 16 16 0 0 +IS 91 24 24 0 0 +IS 92 30 30 0 0 +IS 93 23 23 0 0 +IS 94 21 20 1 0 +IS 95 43 43 0 0 +IS 96 54 54 0 0 +IS 97 34 34 0 0 +IS 98 28 28 0 0 +IS 99 24 24 0 0 +IS 100 44 44 0 0 +IS 101 24 24 0 0 +IS 102 27 27 0 0 +IS 103 22 22 0 0 +IS 104 33 33 0 0 +IS 105 26 26 0 0 +IS 106 28 28 0 0 +IS 107 35 35 0 0 +IS 108 26 26 0 0 +IS 109 24 24 0 0 +IS 110 34 34 0 0 +IS 111 29 29 0 0 +IS 112 22 22 0 0 +IS 113 36 36 0 0 +IS 114 30 30 0 0 +IS 115 49 49 0 0 +IS 116 36 35 1 0 +IS 117 33 33 0 0 +IS 118 34 34 0 0 +IS 119 38 38 0 0 +IS 120 14 14 0 0 +IS 121 39 39 0 0 +IS 122 30 30 0 0 +IS 123 28 28 0 0 +IS 124 36 35 1 0 +IS 125 36 36 0 0 +IS 126 25 25 0 0 +IS 127 32 32 0 0 +IS 128 31 31 0 0 +IS 129 28 28 0 0 +IS 130 39 39 0 0 +IS 131 45 44 1 0 +IS 132 25 25 0 0 +IS 133 18 18 0 0 +IS 134 25 25 0 0 +IS 135 31 31 0 0 +IS 136 30 29 1 0 +IS 137 29 29 0 0 +IS 138 34 34 0 0 +IS 139 32 32 0 0 +IS 140 28 28 0 0 +IS 141 41 41 0 0 +IS 142 27 27 0 0 +IS 143 23 23 0 0 +IS 144 26 26 0 0 +IS 145 31 31 0 0 +IS 146 21 21 0 0 +IS 147 29 29 0 0 +IS 148 18 18 0 0 +IS 149 17 17 0 0 +IS 150 19 19 0 0 +IS 151 20 20 0 0 +IS 152 28 28 0 0 +IS 153 28 28 0 0 +IS 154 18 18 0 0 +IS 155 23 23 0 0 +IS 156 20 20 0 0 +IS 157 29 29 0 0 +IS 158 16 16 0 0 +IS 159 15 15 0 0 +IS 160 14 14 0 0 +IS 161 18 18 0 0 +IS 162 19 19 0 0 +IS 163 15 15 0 0 +IS 164 9 9 0 0 +IS 165 11 11 0 0 +IS 166 21 21 0 0 +IS 167 9 9 0 0 +IS 168 17 17 0 0 +IS 169 16 16 0 0 +IS 170 17 17 0 0 +IS 171 13 13 0 0 +IS 172 14 14 0 0 +IS 173 21 21 0 0 +IS 174 9 9 0 0 +IS 175 9 9 0 0 +IS 176 7 7 0 0 +IS 177 9 9 0 0 +IS 178 9 9 0 0 +IS 179 9 9 0 0 +IS 180 2 2 0 0 +IS 181 8 8 0 0 +IS 182 8 8 0 0 +IS 183 3 3 0 0 +IS 184 12 12 0 0 +IS 185 10 10 0 0 +IS 186 5 5 0 0 +IS 187 7 7 0 0 +IS 188 1 1 0 0 +IS 189 5 5 0 0 +IS 190 8 8 0 0 +IS 191 10 10 0 0 +IS 192 8 8 0 0 +IS 193 2 2 0 0 +IS 194 6 6 0 0 +IS 195 1 1 0 0 +IS 196 2 2 0 0 +IS 197 3 3 0 0 +IS 198 2 2 0 0 +IS 199 4 4 0 0 +IS 200 7 7 0 0 +IS 201 2 2 0 0 +IS 202 6 6 0 0 +IS 203 4 4 0 0 +IS 204 4 4 0 0 +IS 205 2 2 0 0 +IS 206 4 4 0 0 +IS 207 4 4 0 0 +# Read lengths. Use `grep ^RL | cut -f 2-` to extract this part. The columns are: read length, count +RL 30 1 +RL 33 1 +RL 41 1 +RL 45 1 +RL 49 6 +RL 51 2 +RL 52 4 +RL 54 2 +RL 58 2 +RL 59 4 +RL 60 2 +RL 61 8 +RL 62 2 +RL 63 10 +RL 65 10 +RL 66 4 +RL 67 12 +RL 68 7 +RL 69 10 +RL 70 20 +RL 71 22 +RL 72 14 +RL 73 16 +RL 74 8 +RL 75 24 +RL 76 22 +RL 77 38 +RL 78 30 +RL 79 26 +RL 80 34 +RL 81 48 +RL 82 36 +RL 83 38 +RL 84 50 +RL 85 30 +RL 86 48 +RL 87 60 +RL 88 58 +RL 89 42 +RL 90 32 +RL 91 45 +RL 92 60 +RL 93 46 +RL 94 40 +RL 95 85 +RL 96 108 +RL 97 68 +RL 98 56 +RL 99 48 +RL 100 88 +RL 101 48 +RL 102 54 +RL 103 44 +RL 104 66 +RL 105 52 +RL 106 56 +RL 107 71 +RL 108 52 +RL 109 48 +RL 110 68 +RL 111 58 +RL 112 44 +RL 113 72 +RL 114 60 +RL 115 97 +RL 116 72 +RL 117 66 +RL 118 68 +RL 119 75 +RL 120 29 +RL 121 78 +RL 122 60 +RL 123 56 +RL 124 72 +RL 125 72 +RL 126 50 +RL 127 62 +RL 128 62 +RL 129 56 +RL 130 79 +RL 131 90 +RL 132 50 +RL 133 37 +RL 134 51 +RL 135 62 +RL 136 60 +RL 137 58 +RL 138 68 +RL 139 64 +RL 140 56 +RL 141 82 +RL 142 54 +RL 143 1634 +# Read lengths - first fragments. Use `grep ^FRL | cut -f 2-` to extract this part. The columns are: read length, count +FRL 30 1 +FRL 41 1 +FRL 45 1 +FRL 49 3 +FRL 51 1 +FRL 52 2 +FRL 54 1 +FRL 58 1 +FRL 59 2 +FRL 60 1 +FRL 61 4 +FRL 62 1 +FRL 63 5 +FRL 65 5 +FRL 66 2 +FRL 67 6 +FRL 68 3 +FRL 69 5 +FRL 70 10 +FRL 71 11 +FRL 72 7 +FRL 73 8 +FRL 74 4 +FRL 75 12 +FRL 76 11 +FRL 77 19 +FRL 78 15 +FRL 79 13 +FRL 80 17 +FRL 81 24 +FRL 82 18 +FRL 83 19 +FRL 84 25 +FRL 85 15 +FRL 86 24 +FRL 87 30 +FRL 88 29 +FRL 89 21 +FRL 90 16 +FRL 91 23 +FRL 92 30 +FRL 93 23 +FRL 94 20 +FRL 95 42 +FRL 96 54 +FRL 97 34 +FRL 98 28 +FRL 99 24 +FRL 100 44 +FRL 101 24 +FRL 102 27 +FRL 103 22 +FRL 104 33 +FRL 105 26 +FRL 106 28 +FRL 107 36 +FRL 108 26 +FRL 109 24 +FRL 110 34 +FRL 111 29 +FRL 112 22 +FRL 113 36 +FRL 114 30 +FRL 115 49 +FRL 116 36 +FRL 117 33 +FRL 118 34 +FRL 119 38 +FRL 120 14 +FRL 121 39 +FRL 122 30 +FRL 123 28 +FRL 124 36 +FRL 125 36 +FRL 126 26 +FRL 127 31 +FRL 128 31 +FRL 129 28 +FRL 130 40 +FRL 131 45 +FRL 132 25 +FRL 133 18 +FRL 134 26 +FRL 135 31 +FRL 136 30 +FRL 137 29 +FRL 138 34 +FRL 139 32 +FRL 140 28 +FRL 141 41 +FRL 142 27 +FRL 143 814 +# Read lengths - last fragments. Use `grep ^LRL | cut -f 2-` to extract this part. The columns are: read length, count +LRL 33 1 +LRL 49 3 +LRL 51 1 +LRL 52 2 +LRL 54 1 +LRL 58 1 +LRL 59 2 +LRL 60 1 +LRL 61 4 +LRL 62 1 +LRL 63 5 +LRL 65 5 +LRL 66 2 +LRL 67 6 +LRL 68 4 +LRL 69 5 +LRL 70 10 +LRL 71 11 +LRL 72 7 +LRL 73 8 +LRL 74 4 +LRL 75 12 +LRL 76 11 +LRL 77 19 +LRL 78 15 +LRL 79 13 +LRL 80 17 +LRL 81 24 +LRL 82 18 +LRL 83 19 +LRL 84 25 +LRL 85 15 +LRL 86 24 +LRL 87 30 +LRL 88 29 +LRL 89 21 +LRL 90 16 +LRL 91 22 +LRL 92 30 +LRL 93 23 +LRL 94 20 +LRL 95 43 +LRL 96 54 +LRL 97 34 +LRL 98 28 +LRL 99 24 +LRL 100 44 +LRL 101 24 +LRL 102 27 +LRL 103 22 +LRL 104 33 +LRL 105 26 +LRL 106 28 +LRL 107 35 +LRL 108 26 +LRL 109 24 +LRL 110 34 +LRL 111 29 +LRL 112 22 +LRL 113 36 +LRL 114 30 +LRL 115 48 +LRL 116 36 +LRL 117 33 +LRL 118 34 +LRL 119 37 +LRL 120 15 +LRL 121 39 +LRL 122 30 +LRL 123 28 +LRL 124 36 +LRL 125 36 +LRL 126 24 +LRL 127 31 +LRL 128 31 +LRL 129 28 +LRL 130 39 +LRL 131 45 +LRL 132 25 +LRL 133 19 +LRL 134 25 +LRL 135 31 +LRL 136 30 +LRL 137 29 +LRL 138 34 +LRL 139 32 +LRL 140 28 +LRL 141 41 +LRL 142 27 +LRL 143 820 +# Mapping qualities for reads !(UNMAP|SECOND|SUPPL|QCFAIL|DUP). Use `grep ^MAPQ | cut -f 2-` to extract this part. The columns are: mapq, count +MAPQ 40 1 +MAPQ 42 1 +MAPQ 44 1 +MAPQ 54 1 +MAPQ 60 3980 +# Indel distribution. Use `grep ^ID | cut -f 2-` to extract this part. The columns are: length, number of insertions, number of deletions +ID 1 2 10 +# Indels per cycle. Use `grep ^IC | cut -f 2-` to extract this part. The columns are: cycle, number of insertions (fwd), .. (rev) , number of deletions (fwd), .. (rev) +IC 3 0 0 1 0 +IC 10 0 1 0 0 +IC 35 0 0 1 0 +IC 39 0 0 1 0 +IC 53 0 0 0 1 +IC 54 0 0 0 1 +IC 61 0 0 1 0 +IC 62 0 0 0 1 +IC 77 0 0 1 0 +IC 80 1 0 0 1 +IC 132 0 0 0 1 +# Coverage distribution. Use `grep ^COV | cut -f 2-` to extract this part. +COV [1-1] 1 40 +COV [2-2] 2 83 +COV [3-3] 3 32 +COV [4-4] 4 14 +COV [5-5] 5 10 +COV [6-6] 6 1 +COV [7-7] 7 12 +COV [8-8] 8 8 +COV [9-9] 9 9 +COV [10-10] 10 10 +COV [11-11] 11 1 +COV [12-12] 12 5 +COV [13-13] 13 1 +COV [14-14] 14 4 +COV [15-15] 15 1 +COV [16-16] 16 9 +COV [17-17] 17 1 +COV [18-18] 18 1 +COV [19-19] 19 2 +COV [20-20] 20 5 +COV [21-21] 21 13 +COV [22-22] 22 9 +COV [23-23] 23 2 +COV [24-24] 24 6 +COV [25-25] 25 1 +COV [26-26] 26 98 +COV [30-30] 30 2 +COV [32-32] 32 1 +COV [36-36] 36 1 +COV [37-37] 37 1 +COV [40-40] 40 2 +COV [41-41] 41 1 +COV [43-43] 43 2 +COV [45-45] 45 2 +COV [46-46] 46 1 +COV [48-48] 48 1 +COV [50-50] 50 5 +COV [52-52] 52 5 +COV [54-54] 54 3 +COV [55-55] 55 1 +COV [56-56] 56 2 +COV [57-57] 57 1 +COV [58-58] 58 2 +COV [59-59] 59 1 +COV [60-60] 60 1 +COV [63-63] 63 1 +COV [64-64] 64 1 +COV [66-66] 66 5 +COV [68-68] 68 1 +COV [70-70] 70 1 +COV [71-71] 71 1 +COV [72-72] 72 3 +COV [73-73] 73 1 +COV [74-74] 74 7 +COV [78-78] 78 6 +COV [80-80] 80 6 +COV [81-81] 81 1 +COV [82-82] 82 7 +COV [83-83] 83 1 +COV [84-84] 84 2 +COV [85-85] 85 1 +COV [86-86] 86 4 +COV [87-87] 87 1 +COV [88-88] 88 23 +COV [90-90] 90 7 +COV [92-92] 92 7 +COV [93-93] 93 1 +COV [94-94] 94 1 +COV [95-95] 95 1 +COV [98-98] 98 1 +COV [100-100] 100 1 +COV [101-101] 101 2 +COV [103-103] 103 1 +COV [104-104] 104 1 +COV [111-111] 111 1 +COV [114-114] 114 1 +COV [115-115] 115 2 +COV [116-116] 116 1 +COV [120-120] 120 1 +COV [121-121] 121 1 +COV [125-125] 125 1 +COV [126-126] 126 1 +COV [129-129] 129 2 +COV [130-130] 130 1 +COV [135-135] 135 1 +COV [136-136] 136 1 +COV [140-140] 140 1 +COV [143-143] 143 1 +COV [144-144] 144 1 +COV [145-145] 145 1 +COV [147-147] 147 1 +COV [148-148] 148 1 +COV [151-151] 151 1 +COV [154-154] 154 1 +COV [158-158] 158 2 +COV [159-159] 159 1 +COV [160-160] 160 1 +COV [164-164] 164 1 +COV [166-166] 166 1 +COV [170-170] 170 2 +COV [172-172] 172 1 +COV [173-173] 173 1 +COV [176-176] 176 1 +COV [178-178] 178 1 +COV [180-180] 180 2 +COV [184-184] 184 1 +COV [188-188] 188 1 +COV [190-190] 190 2 +COV [191-191] 191 1 +COV [192-192] 192 2 +COV [197-197] 197 1 +COV [200-200] 200 2 +COV [205-205] 205 1 +COV [208-208] 208 2 +COV [210-210] 210 1 +COV [212-212] 212 1 +COV [214-214] 214 1 +COV [215-215] 215 1 +COV [218-218] 218 1 +COV [224-224] 224 1 +COV [226-226] 226 1 +COV [229-229] 229 1 +COV [231-231] 231 2 +COV [232-232] 232 1 +COV [236-236] 236 1 +COV [240-240] 240 2 +COV [241-241] 241 1 +COV [242-242] 242 1 +COV [244-244] 244 3 +COV [245-245] 245 1 +COV [247-247] 247 1 +COV [250-250] 250 1 +COV [252-252] 252 1 +COV [254-254] 254 1 +COV [258-258] 258 2 +COV [259-259] 259 1 +COV [262-262] 262 1 +COV [263-263] 263 1 +COV [264-264] 264 1 +COV [265-265] 265 1 +COV [271-271] 271 1 +COV [274-274] 274 1 +COV [275-275] 275 1 +COV [278-278] 278 1 +COV [280-280] 280 1 +COV [281-281] 281 2 +COV [284-284] 284 1 +COV [286-286] 286 2 +COV [288-288] 288 2 +COV [289-289] 289 1 +COV [292-292] 292 1 +COV [293-293] 293 1 +COV [294-294] 294 1 +COV [296-296] 296 1 +COV [300-300] 300 1 +COV [302-302] 302 1 +COV [304-304] 304 2 +COV [306-306] 306 1 +COV [308-308] 308 1 +COV [310-310] 310 1 +COV [311-311] 311 1 +COV [314-314] 314 1 +COV [315-315] 315 1 +COV [317-317] 317 1 +COV [318-318] 318 2 +COV [320-320] 320 2 +COV [324-324] 324 1 +COV [325-325] 325 1 +COV [326-326] 326 3 +COV [329-329] 329 1 +COV [330-330] 330 1 +COV [331-331] 331 1 +COV [332-332] 332 1 +COV [333-333] 333 1 +COV [334-334] 334 2 +COV [338-338] 338 1 +COV [339-339] 339 1 +COV [340-340] 340 1 +COV [342-342] 342 1 +COV [343-343] 343 1 +COV [344-344] 344 3 +COV [345-345] 345 1 +COV [348-348] 348 3 +COV [349-349] 349 1 +COV [350-350] 350 1 +COV [352-352] 352 1 +COV [356-356] 356 3 +COV [357-357] 357 1 +COV [358-358] 358 4 +COV [360-360] 360 1 +COV [362-362] 362 9 +COV [364-364] 364 7 +COV [366-366] 366 3 +COV [367-367] 367 2 +COV [368-368] 368 28 +COV [374-374] 374 2 +COV [375-375] 375 2 +COV [387-387] 387 2 +COV [388-388] 388 1 +COV [389-389] 389 1 +COV [399-399] 399 1 +COV [401-401] 401 1 +COV [403-403] 403 1 +COV [406-406] 406 1 +COV [415-415] 415 1 +COV [419-419] 419 1 +COV [425-425] 425 1 +COV [426-426] 426 1 +COV [430-430] 430 1 +COV [432-432] 432 1 +COV [436-436] 436 1 +COV [445-445] 445 1 +COV [447-447] 447 1 +COV [454-454] 454 1 +COV [458-458] 458 2 +COV [459-459] 459 1 +COV [460-460] 460 1 +COV [463-463] 463 1 +COV [476-476] 476 1 +COV [477-477] 477 1 +COV [480-480] 480 1 +COV [481-481] 481 1 +COV [483-483] 483 2 +COV [489-489] 489 1 +COV [492-492] 492 1 +COV [500-500] 500 1 +COV [501-501] 501 1 +COV [504-504] 504 1 +COV [508-508] 508 1 +COV [511-511] 511 1 +COV [512-512] 512 1 +COV [515-515] 515 1 +COV [525-525] 525 3 +COV [529-529] 529 1 +COV [533-533] 533 1 +COV [539-539] 539 1 +COV [540-540] 540 1 +COV [541-541] 541 1 +COV [546-546] 546 1 +COV [549-549] 549 1 +COV [550-550] 550 1 +COV [553-553] 553 1 +COV [559-559] 559 1 +COV [563-563] 563 2 +COV [565-565] 565 1 +COV [569-569] 569 1 +COV [575-575] 575 1 +COV [577-577] 577 1 +COV [578-578] 578 1 +COV [579-579] 579 1 +COV [591-591] 591 2 +COV [592-592] 592 2 +COV [593-593] 593 2 +COV [601-601] 601 1 +COV [603-603] 603 1 +COV [605-605] 605 1 +COV [610-610] 610 1 +COV [611-611] 611 1 +COV [613-613] 613 2 +COV [617-617] 617 1 +COV [622-622] 622 1 +COV [625-625] 625 1 +COV [628-628] 628 1 +COV [637-637] 637 2 +COV [639-639] 639 1 +COV [640-640] 640 1 +COV [643-643] 643 1 +COV [652-652] 652 2 +COV [657-657] 657 1 +COV [661-661] 661 1 +COV [663-663] 663 2 +COV [665-665] 665 1 +COV [669-669] 669 1 +COV [671-671] 671 1 +COV [674-674] 674 1 +COV [675-675] 675 1 +COV [679-679] 679 1 +COV [685-685] 685 1 +COV [687-687] 687 1 +COV [689-689] 689 1 +COV [692-692] 692 1 +COV [694-694] 694 1 +COV [697-697] 697 2 +COV [698-698] 698 1 +COV [699-699] 699 1 +COV [705-705] 705 1 +COV [711-711] 711 1 +COV [714-714] 714 1 +COV [719-719] 719 2 +COV [724-724] 724 1 +COV [727-727] 727 1 +COV [728-728] 728 1 +COV [732-732] 732 1 +COV [733-733] 733 1 +COV [735-735] 735 1 +COV [738-738] 738 1 +COV [741-741] 741 1 +COV [746-746] 746 1 +COV [752-752] 752 1 +COV [755-755] 755 3 +COV [756-756] 756 1 +COV [757-757] 757 1 +COV [763-763] 763 1 +COV [765-765] 765 1 +COV [767-767] 767 1 +COV [769-769] 769 1 +COV [770-770] 770 1 +COV [771-771] 771 2 +COV [773-773] 773 2 +COV [774-774] 774 1 +COV [775-775] 775 1 +COV [779-779] 779 3 +COV [781-781] 781 1 +COV [782-782] 782 1 +COV [785-785] 785 2 +COV [788-788] 788 1 +COV [789-789] 789 2 +COV [792-792] 792 1 +COV [793-793] 793 5 +COV [794-794] 794 4 +COV [795-795] 795 7 +COV [796-796] 796 9 +COV [797-797] 797 8 +COV [799-799] 799 1 +COV [801-801] 801 1 +COV [806-806] 806 1 +COV [807-807] 807 1 +COV [817-817] 817 1 +COV [820-820] 820 1 +COV [824-824] 824 1 +COV [825-825] 825 1 +COV [847-847] 847 1 +COV [850-850] 850 1 +COV [851-851] 851 1 +COV [853-853] 853 1 +COV [868-868] 868 1 +COV [873-873] 873 1 +COV [874-874] 874 1 +COV [875-875] 875 1 +COV [892-892] 892 1 +COV [893-893] 893 1 +COV [902-902] 902 1 +COV [906-906] 906 1 +COV [908-908] 908 1 +COV [916-916] 916 1 +COV [925-925] 925 1 +COV [927-927] 927 1 +COV [935-935] 935 1 +COV [937-937] 937 1 +COV [944-944] 944 1 +COV [955-955] 955 1 +COV [965-965] 965 2 +COV [967-967] 967 1 +COV [986-986] 986 1 +COV [988-988] 988 1 +COV [999-999] 999 2 +COV [1000<] 1000 259 +# GC-depth. Use `grep ^GCD | cut -f 2-` to extract this part. The columns are: GC%, unique sequence percentiles, 10th, 25th, 50th, 75th and 90th depth percentile +GCD 0.0 100.000 0.000 0.000 0.000 0.000 0.000 diff --git a/tests/expected/dna/test.thresholds.bed.gz b/tests/expected/dna/test.thresholds.bed.gz new file mode 100644 index 00000000..10d328e2 Binary files /dev/null and b/tests/expected/dna/test.thresholds.bed.gz differ diff --git a/tests/expected/dna/test.thresholds.bed.gz.csi b/tests/expected/dna/test.thresholds.bed.gz.csi new file mode 100644 index 00000000..efa85327 Binary files /dev/null and b/tests/expected/dna/test.thresholds.bed.gz.csi differ diff --git a/tests/expected/dna/test.wgs_metrics.txt b/tests/expected/dna/test.wgs_metrics.txt new file mode 100644 index 00000000..68e27b72 --- /dev/null +++ b/tests/expected/dna/test.wgs_metrics.txt @@ -0,0 +1,258 @@ +## METRICS CLASS picard.analysis.WgsMetrics +GENOME_TERRITORY MEAN_COVERAGE SD_COVERAGE MEDIAN_COVERAGE MAD_COVERAGE PCT_EXC_ADAPTER PCT_EXC_MAPQ PCT_EXC_DUPE PCT_EXC_UNPAIRED PCT_EXC_BASEQ PCT_EXC_OVERLAP PCT_EXC_CAPPED PCT_EXC_TOTAL PCT_1X PCT_5X PCT_10X PCT_15X PCT_20X PCT_25X PCT_30X PCT_40X PCT_50X PCT_60X PCT_70X PCT_80X PCT_90X PCT_100X FOLD_80_BASE_PENALTY FOLD_90_BASE_PENALTY FOLD_95_BASE_PENALTY HET_SNP_SENSITIVITY HET_SNP_Q +40001 3.531312 27.339314 0 0 0 0 0.299737 0 0.007352 0.324694 0.157699 0.789481 0.029124 0.024374 0.022949 0.020174 0.019425 0.019075 0.018375 0.01705 0.016725 0.016375 0.01615 0.0158 0.01545 0.01505 ? ? ? 0.027852 0 + +## HISTOGRAM java.lang.Integer +coverage high_quality_coverage_count +0 38836 +1 105 +2 42 +3 23 +4 20 +5 9 +6 19 +7 9 +8 13 +9 7 +10 95 +11 2 +12 5 +13 4 +14 5 +15 6 +16 10 +17 6 +18 5 +19 3 +20 1 +21 4 +22 4 +23 2 +24 3 +25 5 +26 5 +27 8 +28 5 +29 5 +30 17 +31 20 +32 9 +33 0 +34 2 +35 0 +36 2 +37 1 +38 1 +39 1 +40 0 +41 4 +42 0 +43 2 +44 0 +45 1 +46 2 +47 1 +48 1 +49 2 +50 0 +51 3 +52 2 +53 0 +54 1 +55 2 +56 0 +57 0 +58 5 +59 1 +60 0 +61 1 +62 3 +63 0 +64 1 +65 3 +66 0 +67 0 +68 0 +69 1 +70 2 +71 2 +72 3 +73 0 +74 1 +75 0 +76 2 +77 2 +78 2 +79 0 +80 1 +81 3 +82 1 +83 3 +84 0 +85 1 +86 1 +87 1 +88 1 +89 2 +90 0 +91 4 +92 1 +93 2 +94 1 +95 1 +96 3 +97 0 +98 1 +99 3 +100 1 +101 0 +102 1 +103 1 +104 1 +105 4 +106 2 +107 1 +108 3 +109 2 +110 2 +111 2 +112 2 +113 3 +114 0 +115 2 +116 0 +117 3 +118 6 +119 0 +120 3 +121 1 +122 2 +123 3 +124 2 +125 4 +126 0 +127 4 +128 5 +129 1 +130 12 +131 8 +132 6 +133 25 +134 2 +135 0 +136 2 +137 0 +138 0 +139 0 +140 1 +141 2 +142 2 +143 0 +144 1 +145 0 +146 0 +147 0 +148 2 +149 0 +150 1 +151 0 +152 1 +153 1 +154 1 +155 0 +156 1 +157 1 +158 1 +159 2 +160 3 +161 1 +162 0 +163 0 +164 0 +165 2 +166 0 +167 1 +168 0 +169 0 +170 3 +171 1 +172 0 +173 1 +174 1 +175 1 +176 2 +177 0 +178 1 +179 2 +180 1 +181 0 +182 0 +183 2 +184 0 +185 2 +186 0 +187 1 +188 0 +189 1 +190 0 +191 3 +192 0 +193 0 +194 0 +195 1 +196 1 +197 2 +198 2 +199 0 +200 3 +201 0 +202 3 +203 0 +204 0 +205 1 +206 0 +207 0 +208 1 +209 0 +210 1 +211 0 +212 1 +213 1 +214 0 +215 2 +216 3 +217 2 +218 0 +219 1 +220 1 +221 0 +222 0 +223 2 +224 0 +225 1 +226 2 +227 0 +228 0 +229 3 +230 1 +231 1 +232 2 +233 2 +234 0 +235 1 +236 0 +237 1 +238 1 +239 1 +240 2 +241 0 +242 1 +243 0 +244 2 +245 1 +246 1 +247 2 +248 0 +249 0 +250 387 + diff --git a/tests/expected/protein/VERSIONS.txt b/tests/expected/protein/VERSIONS.txt new file mode 100644 index 00000000..0221a981 --- /dev/null +++ b/tests/expected/protein/VERSIONS.txt @@ -0,0 +1,2 @@ +seqkit 2.13.0 +pyteomics 5.0.1 diff --git a/tests/expected/protein/genome.rnaseq_metrics.txt b/tests/expected/protein/genome.rnaseq_metrics.txt new file mode 100644 index 00000000..234adce4 --- /dev/null +++ b/tests/expected/protein/genome.rnaseq_metrics.txt @@ -0,0 +1,108 @@ +## METRICS CLASS picard.analysis.RnaSeqMetrics +PF_BASES PF_ALIGNED_BASES RIBOSOMAL_BASES CODING_BASES UTR_BASES INTRONIC_BASES INTERGENIC_BASES IGNORED_READS CORRECT_STRAND_READS INCORRECT_STRAND_READS NUM_R1_TRANSCRIPT_STRAND_READS NUM_R2_TRANSCRIPT_STRAND_READS NUM_UNEXPLAINED_READS PCT_R1_TRANSCRIPT_STRAND_READS PCT_R2_TRANSCRIPT_STRAND_READS PCT_RIBOSOMAL_BASES PCT_CODING_BASES PCT_UTR_BASES PCT_INTRONIC_BASES PCT_INTERGENIC_BASES PCT_MRNA_BASES PCT_USABLE_BASES PCT_CORRECT_STRAND_READS MEDIAN_CV_COVERAGE MEDIAN_5PRIME_BIAS MEDIAN_3PRIME_BIAS MEDIAN_5PRIME_TO_3PRIME_BIAS SAMPLE LIBRARY READ_GROUP +672131 670989 0 323630 84236 263123 0 0 0 664 661 1 0.501132 0.498868 0 0.482318 0.12554 0.392142 0.482318 0.481498 0 4.318717 0 0.529614 0 + +## HISTOGRAM java.lang.Integer +normalized_position All_Reads.normalized_coverage +0 0 +1 0 +2 0 +3 0 +4 0 +5 0 +6 0 +7 0 +8 0 +9 0 +10 0 +11 0 +12 0 +13 0 +14 0 +15 0 +16 0 +17 0 +18 0 +19 0 +20 0 +21 0 +22 0 +23 0 +24 0 +25 0 +26 0 +27 0 +28 0 +29 0 +30 0 +31 0 +32 0 +33 0 +34 0 +35 0 +36 0 +37 0 +38 0 +39 0 +40 0 +41 0 +42 0 +43 0 +44 0 +45 0 +46 0 +47 0 +48 0 +49 0 +50 0 +51 0 +52 0 +53 0 +54 0 +55 0 +56 0 +57 0 +58 0 +59 0 +60 0 +61 0 +62 0.007542 +63 0.353257 +64 0.901997 +65 0.824165 +66 0.103172 +67 0.004348 +68 0 +69 0 +70 0 +71 0 +72 0 +73 0 +74 0 +75 0 +76 0 +77 0 +78 0 +79 0 +80 0 +81 0 +82 0 +83 0 +84 0 +85 0 +86 0 +87 0 +88 0 +89 0 +90 0 +91 0.651006 +92 6.035418 +93 15.914658 +94 23.771379 +95 25.867993 +96 18.602848 +97 6.578541 +98 0.156266 +99 0.03077 +100 0.012318 + diff --git a/tests/expected/protein/protein_mini_with_cazymes.seqkit.tsv b/tests/expected/protein/protein_mini_with_cazymes.seqkit.tsv new file mode 100644 index 00000000..1ce9c512 --- /dev/null +++ b/tests/expected/protein/protein_mini_with_cazymes.seqkit.tsv @@ -0,0 +1,2 @@ +file format type num_seqs sum_len min_len avg_len max_len Q1 Q2 Q3 sum_gap N50 N50_num Q20(%) Q30(%) AvgQual GC(%) sum_n +protein_mini_with_cazymes.faa FASTA Protein 16 6757 199 422.3 757 294 376 516 0 430 6 0 0 0.00 0.00 0 diff --git a/tests/expected/protein/small.pyteomics.tsv b/tests/expected/protein/small.pyteomics.tsv new file mode 100644 index 00000000..907360cb --- /dev/null +++ b/tests/expected/protein/small.pyteomics.tsv @@ -0,0 +1,19 @@ +metric value +spectra 48 +peaks 305213 +rt_min 0.004935 +rt_max 0.487237 +precursors 34 +precursors_without_charge 34 +precursor_mz_min 558.7494 +precursor_mz_max 882.5350 +ms1_spectra 14 +ms1_peaks 279869 +ms1_min_peaks 15238 +ms1_max_peaks 33335 +ms1_total_ion_current 588376797.3453 +ms2_spectra 34 +ms2_peaks 25344 +ms2_min_peaks 485 +ms2_max_peaks 1064 +ms2_total_ion_current 11505552.3204 diff --git a/tests/expected/protein/yeast_UPS_mini.seqkit.tsv b/tests/expected/protein/yeast_UPS_mini.seqkit.tsv new file mode 100644 index 00000000..819a3a2e --- /dev/null +++ b/tests/expected/protein/yeast_UPS_mini.seqkit.tsv @@ -0,0 +1,2 @@ +file format type num_seqs sum_len min_len avg_len max_len Q1 Q2 Q3 sum_gap N50 N50_num Q20(%) Q30(%) AvgQual GC(%) sum_n +yeast_UPS_mini.fasta FASTA Protein 10 3284 81 328.4 755 157 236 526 0 526 3 0 0 0.00 0.00 0 diff --git a/tests/protein_integration_test.rs b/tests/protein_integration_test.rs new file mode 100644 index 00000000..b6246209 --- /dev/null +++ b/tests/protein_integration_test.rs @@ -0,0 +1,320 @@ +//! Parity tests for the protein pipeline against the committed reference +//! outputs. +//! +//! The fixtures under `tests/expected/protein/` are the output of the upstream +//! tools themselves, at the versions pinned in `VERSIONS.txt`. A failure here +//! is a defect in RustQC, not a reason to regenerate the fixture. + +use std::path::{Path, PathBuf}; + +use rustqc::protein::sequence::{self, defects, output, stats::SequenceStats}; + +fn fixture(name: &str) -> PathBuf { + Path::new(env!("CARGO_MANIFEST_DIR")) + .join("tests/expected/protein") + .join(name) +} + +fn input(name: &str) -> PathBuf { + Path::new(env!("CARGO_MANIFEST_DIR")) + .join("tests/data/protein") + .join(name) +} + +fn scratch(name: &str) -> PathBuf { + let dir = std::env::temp_dir().join("rustqc-protein-parity"); + std::fs::create_dir_all(&dir).unwrap(); + dir.join(name) +} + +/// The fixtures record which seqkit produced them; a mismatch is a version +/// skew rather than a parity failure, so it is reported as such. +#[test] +fn fixture_tool_versions_are_the_pinned_ones() { + let versions = std::fs::read_to_string(fixture("VERSIONS.txt")).unwrap(); + assert!( + versions.contains("seqkit\t2.13.0"), + "unexpected seqkit fixture version: {versions}" + ); +} + +/// The whole `seqkit stats -a -T` table, both fixtures, byte for byte. +#[test] +fn sequence_stats_match_seqkit() { + for name in ["yeast_UPS_mini.fasta", "protein_mini_with_cazymes.faa"] { + let records = sequence::read_fasta(&input(name)).unwrap(); + let stats = SequenceStats::from_records(&records); + let path = scratch(&format!("{name}.tsv")); + output::write_seqkit_stats(&[(name.to_string(), stats)], &path).unwrap(); + + let stem = name.rsplit_once('.').unwrap().0; + let got = std::fs::read_to_string(&path).unwrap(); + let want = std::fs::read_to_string(fixture(&format!("{stem}.seqkit.tsv"))).unwrap(); + + let got_lines: Vec<&str> = got.lines().collect(); + let want_lines: Vec<&str> = want.lines().collect(); + assert_eq!(got_lines.len(), want_lines.len(), "{name}: line count"); + for (i, (a, b)) in got_lines.iter().zip(&want_lines).enumerate() { + assert_eq!(a, b, "{name}: line {} differs", i + 1); + } + } +} + +/// The two conventions that had to be recovered from seqkit's behaviour, since +/// neither is what a statistics library gives by default. +#[test] +fn quartiles_use_tukeys_halves_with_bankers_rounding() { + let records = sequence::read_fasta(&input("yeast_UPS_mini.fasta")).unwrap(); + let stats = SequenceStats::from_records(&records); + // Lengths are 81 140 157 189 198 273 381 526 584 755. + assert_eq!(stats.q1, 157, "linear interpolation would give 165"); + assert_eq!( + stats.q2, 236, + "the median is 235.5, rounded to the even 236" + ); + assert_eq!(stats.q3, 526); + + let records = sequence::read_fasta(&input("protein_mini_with_cazymes.faa")).unwrap(); + let stats = SequenceStats::from_records(&records); + assert_eq!(stats.q2, 376, "376.5 rounds down, to the even 376"); + assert_eq!(stats.q3, 516, "516.5 likewise"); +} + +/// Composition is what seqkit does not report, so it is checked against the +/// input directly: every residue counted once, summing to the total length. +#[test] +fn composition_accounts_for_every_residue() { + for name in ["yeast_UPS_mini.fasta", "protein_mini_with_cazymes.faa"] { + let records = sequence::read_fasta(&input(name)).unwrap(); + let stats = SequenceStats::from_records(&records); + let counted: u64 = stats.composition.values().sum(); + assert_eq!( + counted, stats.total, + "{name}: composition must account for every residue" + ); + let fractions: f64 = stats.composition.keys().map(|r| stats.fraction(*r)).sum(); + assert!( + (fractions - 1.0).abs() < 1e-12, + "{name}: fractions sum to {fractions}" + ); + } +} + +/// Both fixtures are real reference proteomes, so they should be clean. +#[test] +fn the_reference_proteomes_carry_no_defects() { + for name in ["yeast_UPS_mini.fasta", "protein_mini_with_cazymes.faa"] { + let records = sequence::read_fasta(&input(name)).unwrap(); + let found = defects::inspect(&records, false); + assert!( + found.is_clean(), + "{name}: expected a clean proteome, found {} defects: {found:?}", + found.count() + ); + } +} + +/// The report is RustQC's own format, so it is checked for structure and for +/// carrying the figures the stats table does not. +#[test] +fn the_report_carries_composition_and_defects() { + let name = "yeast_UPS_mini.fasta"; + let records = sequence::read_fasta(&input(name)).unwrap(); + let stats = SequenceStats::from_records(&records); + let found = defects::inspect(&records, false); + let path = scratch("report.txt"); + output::write_report(name, &stats, &found, &path).unwrap(); + + let text = std::fs::read_to_string(&path).unwrap(); + assert!(text.contains("## Composition")); + assert!(text.contains("## Defects")); + assert!(text.contains("internal_stop\t0")); + // Methionine starts every protein, so it cannot be absent. + let methionine = text + .lines() + .find(|l| l.starts_with("M\t")) + .expect("a row for methionine"); + let count: u64 = methionine.split('\t').nth(1).unwrap().parse().unwrap(); + assert!(count > 0, "methionine should appear in a real proteome"); +} + +// =================================================================== +// Mass spectrometry +// =================================================================== + +/// The reference figures pyteomics produced, keyed by metric name. +#[cfg(feature = "proteomics")] +fn pyteomics_reference() -> std::collections::HashMap { + std::fs::read_to_string(fixture("small.pyteomics.tsv")) + .unwrap() + .lines() + .skip(1) + .filter_map(|l| l.split_once('\t')) + .map(|(k, v)| (k.to_string(), v.to_string())) + .collect() +} + +/// Every counter mzdata and pyteomics can both report, on the same file. +/// +/// Two independent readers agreeing on 305213 peaks across 48 spectra is a +/// stronger statement than either agreeing with itself. +#[cfg(feature = "proteomics")] +#[test] +fn spectra_metrics_match_pyteomics() { + use rustqc::protein::spectra; + + let metrics = spectra::analyse(&input("small.mzML")).unwrap(); + let want = pyteomics_reference(); + let integer = |key: &str| -> u64 { want[key].parse().unwrap() }; + let float = |key: &str| -> f64 { want[key].parse().unwrap() }; + + assert_eq!(metrics.total_spectra(), integer("spectra")); + assert_eq!(metrics.total_peaks(), integer("peaks")); + assert_eq!(metrics.precursors, integer("precursors")); + assert_eq!( + metrics.precursors_without_charge, + integer("precursors_without_charge"), + "this file annotates no charge states, and that must be visible" + ); + + assert!( + (metrics.rt_min - float("rt_min")).abs() < 1e-6, + "rt_min was {}", + metrics.rt_min + ); + assert!( + (metrics.rt_max - float("rt_max")).abs() < 1e-6, + "rt_max was {}", + metrics.rt_max + ); + assert!((metrics.precursor_mz_min - float("precursor_mz_min")).abs() < 1e-4); + assert!((metrics.precursor_mz_max - float("precursor_mz_max")).abs() < 1e-4); + + for level in [1u8, 2] { + let m = metrics + .levels + .get(&level) + .unwrap_or_else(|| panic!("no MS{level} spectra")); + assert_eq!(m.spectra, integer(&format!("ms{level}_spectra"))); + assert_eq!(m.peaks, integer(&format!("ms{level}_peaks"))); + assert_eq!(m.min_peaks, integer(&format!("ms{level}_min_peaks"))); + assert_eq!(m.max_peaks, integer(&format!("ms{level}_max_peaks"))); + + // Intensities are 32-bit in the file, so the two readers accumulate + // them at different precision; a relative tolerance is the honest + // comparison rather than an exact one. + let want_tic = float(&format!("ms{level}_total_ion_current")); + let relative = (m.total_ion_current - want_tic).abs() / want_tic; + assert!( + relative < 1e-6, + "MS{level} total ion current: got {}, want {want_tic}, relative {relative}", + m.total_ion_current + ); + } +} + +/// The derived figures the report leans on. +#[cfg(feature = "proteomics")] +#[test] +fn spectra_derived_figures_are_consistent() { + use rustqc::protein::spectra; + + let metrics = spectra::analyse(&input("small.mzML")).unwrap(); + assert_eq!( + metrics.ms2_per_ms1(), + Some(34.0 / 14.0), + "34 fragmentation scans for 14 survey scans" + ); + assert!(metrics.rt_span() > 0.0, "the run spans some time"); + assert_eq!( + metrics.total_peaks(), + metrics.levels.values().map(|l| l.peaks).sum::(), + "the total must be the sum of the levels" + ); +} + +/// The report is RustQC's own format, so it is checked for structure. +#[cfg(feature = "proteomics")] +#[test] +fn the_spectra_report_carries_every_section() { + use rustqc::protein::spectra; + + let metrics = spectra::analyse(&input("small.mzML")).unwrap(); + let path = scratch("spectra_report.txt"); + spectra::output::write_report("small.mzML", &metrics, &path).unwrap(); + let text = std::fs::read_to_string(&path).unwrap(); + + assert!(text.contains("## Run")); + assert!(text.contains("## Levels")); + assert!(text.contains("## Precursors")); + assert!(text.contains("spectra\t48")); + assert!(text.contains("without_charge\t34")); + assert!( + !text.contains("## Charge states"), + "this file annotates no charges, so the section is omitted" + ); +} + +// =================================================================== +// Coding-region assignment +// =================================================================== + +/// Base assignment against Picard `CollectRnaSeqMetrics`. +/// +/// The alignment is the DNA fixture and the annotation is the matching real +/// chr22 slice, so the two describe the same 40 kb of genome. Only one +/// transcript in that slice carries a CDS and no read covers it, so +/// `CODING_BASES` is legitimately zero; what the fixture does exercise is the +/// exonic, intronic and intergenic split, and that they account for every +/// aligned base. +#[test] +fn coding_base_assignment_matches_picard() { + use rust_htslib::bam::{Read as BamRead, Reader}; + use rustqc::protein::coding::{output::CodingCounts, RegionSets}; + + let root = Path::new(env!("CARGO_MANIFEST_DIR")); + let genes = rustqc::gtf::parse_gtf( + root.join("tests/data/protein/genome.gtf").to_str().unwrap(), + &[], + ) + .unwrap(); + let regions = RegionSets::from_genes(genes.values()); + + let mut reader = Reader::from_path(root.join("tests/data/dna/test.dna.bam")).unwrap(); + let header = reader.header().to_owned(); + let mut counts = CodingCounts::default(); + let mut record = rust_htslib::bam::Record::new(); + while let Some(result) = reader.read(&mut record) { + result.unwrap(); + let chrom = if record.tid() >= 0 { + String::from_utf8_lossy(header.tid2name(record.tid() as u32)).to_string() + } else { + String::new() + }; + counts.process_read(&record, &chrom, ®ions); + } + + let reference = std::fs::read_to_string(fixture("genome.rnaseq_metrics.txt")).unwrap(); + let header_row: Vec<&str> = reference.lines().nth(1).unwrap().split('\t').collect(); + let value_row: Vec<&str> = reference.lines().nth(2).unwrap().split('\t').collect(); + let want = |name: &str| -> u64 { + let index = header_row.iter().position(|c| *c == name).unwrap(); + value_row[index].parse().unwrap_or(0) + }; + + assert_eq!(counts.total, want("PF_BASES"), "PF_BASES"); + assert_eq!(counts.aligned, want("PF_ALIGNED_BASES"), "PF_ALIGNED_BASES"); + assert_eq!(counts.coding, want("CODING_BASES"), "CODING_BASES"); + assert_eq!(counts.utr, want("UTR_BASES"), "UTR_BASES"); + assert_eq!(counts.intronic, want("INTRONIC_BASES"), "INTRONIC_BASES"); + assert_eq!( + counts.intergenic, + want("INTERGENIC_BASES"), + "INTERGENIC_BASES" + ); + assert_eq!( + counts.coding + counts.utr + counts.intronic + counts.intergenic, + counts.aligned, + "the four classes must account for every aligned base" + ); +}