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README.md

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@@ -7,6 +7,9 @@ The Personal Cancer Genome Reporter (PCGR) is a stand-alone software package for
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![PCGR overview](PCGR_workflow.png)
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### News
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* _May 22nd 2019_: **0.8.1 release**
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* Added *Cancer_NOS.toml* for unspecified tumor types
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* Minor bugfixing
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* _May 20th 2019_: **0.8.0 release**
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* Bundle update (VEP, CIViC, UniProt, CancerMine, dbNSFP, OpenTargets, DisGeNET, TCGA, ICGC-PCAWG)
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* New functionality
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##### Latest release
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a. Download and unpack the [latest software release (0.8.0)](https://github.com/sigven/pcgr/releases/tag/v0.8.0)
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a. Download and unpack the [latest software release (0.8.1)](https://github.com/sigven/pcgr/releases/tag/v0.8.1)
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b. Download and unpack the assembly-specific data bundle in the PCGR directory
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* [grch37 data bundle - 20190519](https://drive.google.com/open?id=1vIESS8NxiITUnrqZoWOdNk1YsklH8f1C) (approx 15Gb)
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A _data/_ folder within the _pcgr-X.X_ software folder should now have been produced
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c. Pull the [PCGR Docker image (0.8.0)](https://hub.docker.com/r/sigven/pcgr/) from DockerHub (approx 5.2Gb):
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* `docker pull sigven/pcgr:0.8.0` (PCGR annotation engine)
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c. Pull the [PCGR Docker image (0.8.1)](https://hub.docker.com/r/sigven/pcgr/) from DockerHub (approx 5.2Gb):
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* `docker pull sigven/pcgr:0.8.1` (PCGR annotation engine)
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#### STEP 3: Input preprocessing
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positional arguments:
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pcgr_dir PCGR base directory with accompanying data directory,
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e.g. ~/pcgr-0.8.0
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e.g. ~/pcgr-0.8.1
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output_dir Output directory
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{grch37,grch38} Genome assembly build: grch37 or grch38
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configuration_file PCGR configuration file (TOML format, in conf/ folder)
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The _examples_ folder contain input files from two tumor samples sequenced within TCGA (**GRCh37** only). It also contains PCGR configuration files customized for these cases. A report for a colorectal tumor case can be generated by running the following command in your terminal window:
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`python pcgr.py --input_vcf ~/pcgr-0.8.0/examples/tumor_sample.COAD.vcf.gz`
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`--input_cna ~/pcgr-0.8.0/examples/tumor_sample.COAD.cna.tsv --tumor_purity 0.9 --tumor_ploidy 2.0`
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` ~/pcgr-0.8.0 ~/pcgr-0.8.0/examples grch37 ~/pcgr-0.8.0/examples/examples_COAD.toml tumor_sample.COAD`
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`python pcgr.py --input_vcf ~/pcgr-0.8.1/examples/tumor_sample.COAD.vcf.gz`
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`--input_cna ~/pcgr-0.8.1/examples/tumor_sample.COAD.cna.tsv --tumor_purity 0.9 --tumor_ploidy 2.0`
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` ~/pcgr-0.8.1 ~/pcgr-0.8.1/examples grch37 ~/pcgr-0.8.1/examples/examples_COAD.toml tumor_sample.COAD`
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This command will run the Docker-based PCGR workflow and produce the following output files in the _examples_ folder:

conf/Cancer_NOS.toml

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# Basic PCGR configuration options (TOML).
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[tumor_only]
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## If input VCF contains mix of germline/somatic (variants called with no matching control, i.e. tumor-only) set vcf_tumor_only to true
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vcf_tumor_only = false
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## If vcf_tumor_only = true, several filters can be configured, all as a means to minimize the proportion of germline calls in the raw set derived from tumor-only calling
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## Exclude variants (SNVs/InDels) with minor allele frequency above the following population-specific thresholds
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## 1000 Genomes Project - WGS data
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maf_onekg_eur = 0.002
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maf_onekg_amr = 0.002
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maf_onekg_afr = 0.002
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maf_onekg_sas = 0.002
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maf_onekg_eas = 0.002
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maf_onekg_global = 0.002
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## exclude variants with minor allele frequency above the following population-specific thresholds
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## gnomAD - WES data
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maf_gnomad_nfe = 0.002
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maf_gnomad_amr = 0.002
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maf_gnomad_afr = 0.002
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maf_gnomad_asj = 0.002
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maf_gnomad_sas = 0.002
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maf_gnomad_eas = 0.002
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maf_gnomad_fin = 0.002
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maf_gnomad_oth = 0.002
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maf_gnomad_global = 0.002
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## Exclude variants occurring in PoN (panel of normals, if provided as VCF)
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exclude_pon = true
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## Exclude likely homozygous germline variants (100% allelic fraction for alternate allele in tumor, very unlikely somatic event)
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exclude_likely_hom_germline = false
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## Exclude likely heterozygous germline variants
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## Must satisfy i) 40-60 % allelic fraction for alternate allele in tumor sample, ii) present in dbSNP + gnomAD, ii) not existing as somatic event in COSMIC/TCGA
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## Note that the application of this filter may be suboptimal for very impure tumors or variants affected by CNAs etc (under these circumstances, the allelic fraction
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## will be skewed (see e.g. discussion in PMID:29249243)
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exclude_likely_het_germline = false
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## Exclude variants found in dbSNP (only those that are NOT found in ClinVar(somatic origin)/DoCM/TCGA/COSMIC)
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exclude_dbsnp_nonsomatic = false
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## exclude all non-exonic variants
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exclude_nonexonic = true
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[allelic_support]
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## Specify INFO tags in input VCF that denotes depth/allelic fraction in tumor and normal sample
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## An additional tag that denotes call confidence (call_conf_tag) can also be specified, which will
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## be used for exploration in the global variant browser. Note that 'tumor_dp_tag' must be of
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## Type=Integer, and 'tumor_af_tag' must be of Type=Float (similarly for normal sample)
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tumor_dp_tag = ""
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tumor_af_tag = ""
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control_dp_tag = ""
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control_af_tag = ""
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call_conf_tag = ""
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## set thresholds for tumor depth/allelic fraction, will be applied before report generation
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## will only apply if 'tumor_dp_tag' and 'tumor_af_tag' are specified above (similarly
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## for 'control_dp_tag' and 'control_af_tag'
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tumor_dp_min = 0
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tumor_af_min = 0.0
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control_dp_min = 0
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control_af_max = 1.0
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[mutational_burden]
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## Calculate mutational burden (similar to Chalmers et al., Genome Med, 2017)
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mutational_burden = true
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## Size of coding target region in megabases (defaults to size of protein-coding regions of GENCODE ~ 34 Mb)
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## Note: this should ideally denote the callable target size (i.e. reflecting variable sequencing depth)
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target_size_mb = 34.0
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## set upper limits to tumor mutational burden tertiles (mutations/Mb)
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tmb_low_limit = 5
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tmb_intermediate_limit = 20
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## tmb_high = tmb > tmb_intermediate_limit
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[cna]
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## log ratio thresholds for determination of copy number gains and homozygous deletions
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logR_gain = 0.8
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logR_homdel = -0.8
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## mean percent overlap between copy number segment and gene transcripts for reporting of gains/losses in tumor suppressor genes/oncogenes
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cna_overlap_pct = 50
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[msi]
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## Predict microsatellite instability
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msi = true
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[mutational_signatures]
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## Identify relative contribution of 30 known mutational signatures (COSMIC) through the deconstructSigs framework
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mutsignatures = true
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## deconstructSigs option: number of mutational signatures to limit the search to ('signatures.limit' in whichSignatures)
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mutsignatures_signature_limit = 6
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## deconstructSigs option: type of trimer count normalization for inference of known mutational signatures, see explanation at https://github.com/raerose01/deconstructSigs"
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## options = 'default', 'exome', 'genome', 'exome2genome'
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## NOTE: If your data (VCF) is from exome sequencing, 'default' or 'exome2genome' should be used. See https://github.com/raerose01/deconstructSigs/issues/2
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mutsignatures_normalization = "exome2genome"
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## Require a minimum number of mutations for signature estimation
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mutsignatures_mutation_limit = 100
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## deconstructSigs option: discard any signature contributions with a weight less than this amount
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mutsignatures_cutoff = 0.06
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[visual]
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## Choose visual theme of report, any of: "default", "cerulean", "journal", "flatly", "readable", "spacelab", "united", "cosmo", "lumen", "paper", "sandstone", "simplex", or "yeti" (https://bootswatch.com/)
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report_theme = "default"
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[custom_tags]
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## list VCF info tags that should be present in JSON and TSV output
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## tags should be comma separated, i.e. custom_tags = "MUTECT2_FILTER,STRELKA_FILTER"
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custom_tags = ""
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[other]
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## list/do not list noncoding variants
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list_noncoding = true
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## VEP/vcfanno processing options
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n_vcfanno_proc = 4
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n_vep_forks = 4
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## Customise the order of criteria used to pick the primary transcript in VEP (see https://www.ensembl.org/info/docs/tools/vep/script/vep_options.html#opt_pick_order)
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vep_pick_order = "canonical,appris,biotype,ccds,rank,tsl,length"
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## omit intergenic variants during VEP processing
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vep_skip_intergenic = false
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## generate a MAF for input VCF using https://github.com/mskcc/vcf2maf
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vcf2maf = true
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## Not for edit
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[tumor_type]
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type = ""

docs/CHANGELOG.md

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## CHANGELOG
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#### 0.8.1 - May 22nd 2019
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##### Added
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* *Cancer_NOS.toml* as configuration file for unspecified tumor types
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#### 0.8.0 - May 20th 2019
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##### Fixed

docs/CHANGELOG.rst

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CHANGELOG
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---------
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0.8.1 - May 22nd 2019
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^^^^^^^^^^^^^^^^^^^^^
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'''''
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0.8.0 - May 20th 2019
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^^^^^^^^^^^^^^^^^^^^^
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- Bug in value box for Tier 2 variants (new line carriage) `Issue
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#73 <https://github.com/sigven/pcgr/issues/73>`__
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'''''
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'''''
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'''''
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'''''
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docs/_build/doctrees/about.doctree

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docs/_build/html/.buildinfo

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# Sphinx build info version 1
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# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
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config: 1b5753ec6635113bf8ca74c36d47b914
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config: 21394eecc26621110784fc2eac7a29dc
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tags: 645f666f9bcd5a90fca523b33c5a78b7

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