Overview

This repository contains the code used in the paper ... It consists of Nextflow workflows for genome and transcriptome assembly, then subsequent genome annotation with maker followed by variant calling with GATK. The maker and variant calling pipelines were adapted from code by Card et al. (2019) and Khalfan (2020) respectively. This file provides a brief overview of each workflow steps, their relevant shell commands, the tools involved and the external data used (links included in the references).

Extended README

The extended readme contains the rationale behind each step (though without the accompanying shell commands) and additional information on file formats, terminology, tools etc.

Genome/transcriptome assembly

• Input:
  • Raw paired-end fastq files
  • Reference genome FASTA and GFF for Sporothrix schenckii, GCF_000961545.1
  • mtDNA reference
• 1. FastQC, MultiQC: Evaluate raw paired-end fastq files
  • FastQC provides information about read quality, overrepresented sequences, adapter content and other statistics on a per-fastq-file basis; MultiQC collates the information from multiple FastQC report files for convenience.

fastqc <reads1> <reads2>
multiqc <directory_containing<reads>

• 2. FastP: Trim adapters and poly g (identified as a contaminant in prior step).
  • FastP is an all-purpose quality control tool that can perform adapter removal, read trimming and base correction

fastp -i <reads1> -I <reads2> \
    --detect_adapter_for_pe \ # Detect adapters in paired-end reads
    -c \ # Enable overlap analysis for paired-end reads, which corrects mismatched bases if the overlap meets a certain length (default 30) and the mistmach exceeds a certain number of bases (default 5)
    -Q \ # Disable phred-based quality filtering - the reads for all given samples were already high quality (> 25).
    --trim_poly_g \ # Trim poly-g tails
    -o <reads1_out> -O <reads2_out> # Specify names for the cleaned output reads

• 3. BBduk: Filter mtDNA
  • A kmer-based quality control tool, BBduk was used here for the function of filtering out reads that match strongly to a set of query sequences (mtDNA in this case).
  • The kmer size setting for BBduk was set to the maximum of k=31 to make filtering as stringent as possible

bbduk.sh in1=<reads1> in2=<reads2> \
    out1=<reads1_out> out2=<reads2_out> \
    ref=<reference> \ # The reference sequences to filter out from, in FASTA format
    outm=<flagged> \ # Name for file containing flagged reads that got matched to ref
    k=31 \
    -Xmx1G -Xms16M \ # Allocate memory to prevent "out of memory" issues
    ```
- 4. Spades, Megahit: Assemble reads
  - Both ran in default settings
```bash
spades.py \
    -o <sample_name> \ # The directory to store resulting files.
    # The final assembly is denoted "scaffolds.fasta"
    --pe-1 <reads1> --pe-2 <reads2> \
megahit \
    -o <sample_name> # The directory to store resulting files
    # The final assembly is denoted "final.contigs.fa"
    -1 <reads1> -2 <reads2>

• 5. BUSCO, Quast: Assess assembly quality
  • BUSCO was set to use the sordariomycetes_odb10 lineage in genome mode, while Quast was provided with the reference genome
    • BUSCO needs to be set in offline mode or else it will downloads its lineage database automatically in the directory of each run
    • Quast can assess several assemblies at once for comparison purposes

# First manually download the sordariomycetes_odb10 lineage dataset
busco --download sordariomycetes_odb10

busco -i <assembly_fasta> \
    -l sordariomycetes_odb10 \
    -o <busco_output> \ # The busco output directory
    -m genome \
    --download_path <path> # The path to the "busco_downloads" folder obtained above

quast.py <assemblies> \ # The paths to all assemblies
    --fungus \ # Specifies to analyze as a fungal genome
    -r <reference> \ # path to reference genome fasta file
    -g # path to reference genome GFF file

• Transcriptome assembly was performed in much the same way as genome assembly, except mtDNA was not filtered, RNAspades was used as the assembler and finally BUSCO was set in transcriptome mode
• 6. Ragout: Assemble contigs into scaffolds
  • Ragout was provided with the GCF_000961545.1 reference sequence to scaffold from, and run on default settings
    • The paths to files are given to Ragout with an rcp file, (an example can be found here). A python script was used to automate writing this file from the command line

ragout recipe.rcp \
-o <output_directory> # Name of the output directory; the final scaffolds will be found here under the name of "target" specified in the rcp file

• 7. Minimap2: Align scaffolds to reference sequence

minimap2 -a <reference> <scaffolds> > aligned.sam # Align the scaffolds

• 8. Samtools: Extract scaffolds from sam file into separate fasta files for each chromosome

samtools sort aligned.sam -o aligned.bam # Sort, then index the aligned bam file
samtools index aligned.bam # Necessary for using samtools view with a range specifier
samtools view -h aligned.bam <chromosome> | samtools fasta > <chromosome>.fasta
# "chromosome" specifies which chromosome is to be extracted, which requires adding the -h (header) flag
# The pipe to "samtools fasta" extracts sequences aligned to that chromosome from the aligned.bam file in fasta format

• Output: genome assemblies for each sample, split into fasta files for each chromosome present in the reference sequence

Genome annotation

• Input:
  • Genemarks hmm files for each sample, trained in-house with Genemarks-ES
  • Augustus species model trained with the Augustus web server on the GCF_000961545.1 reference
  • A database of repeat elements, generated by combining the TREP database with models produced in-house by RepeatModeler on several Sporothrix species
  • est evidence: includes the previously assembled transcriptome in addition to known Sporothrix genes collected from the NCBI nucleotide database with query txid29907[Organism:exp] AND biomol_mrna[PROP]
  • Repeat proteins provided with the Maker download
  • GFF evidence from the GCF_000961545.1 reference
  • Protein evidence collected from UniProt Release 2023_02 with query Sporothrix

# Train Genemarks
gmes_petap.pl -ES -sequence <scaffolds> -fungus

# Predict repeat elements from reference fasta file
BuildDatabase -name <species>_db <reference>
RepeatModeler -database <species>_db -LTRStruct
# The -LTRStruct flag runs the LTR structural discovery pipeline in addition to the standard discovery setting
# Concatenate the RepeatModeler output (the file called consensi.fa.classified) from each species into a single file to be used by maker

• 1. Maker: First round of Maker annotation, using est, GFF, protein, repeat evidence and Genemarks model
  • Example of options here, a comment with "##" marks where an option has been changed for this round

maker -CTL # Generate the maker control files in the current directory
# Manually edit maker_opts.ctl following the example above, or use the script "maker_cli.py" found in the bin directory
maker -fix_nucleotides # Run maker in the directory containing the control files. Need the "-fix_nucleotides" flag to be compatible with some of the evidence
GFF3_merge -d <maker_output_directory>/<round1>_master_datastore.log # Combine all the maker annotations from each scaffold into a single GFF file

• 2. SNAP: Train SNAP on the output from the first round of annotation.

maker2zff <round1_gff> # This maker script converts the gff output into a zff file (<round1>.ann) and extracts the sequences in fasta format (<round1>.dna)
fathom genome.ann genome.dna -gene-stats > ${name}_SNAP-gene-stats.log 2>&1 # Obtain gene log file for statistics
fathom genome.ann genome.dna -validate > ${name}_SNAP-validate.log 2>&1 # Obtain log file for gene validations.
fathom -categorize 100 <round1>.ann <round1>.dna # Categorize genes for training: genes with errors overlapping genes etc.
#   The number after specifies how much intergenic sequence to place on either side of the gene
fathom -export 100 -plus uni.* # Only the unique genes will get exported for training
forge export.ann export.dna
hmm-assembler.pl <round1> . > <round1>.snap_hmm # Build the snap model from the files in the directory

• 3. Maker: Second round of annotation, disabling all evidence-based annotation and using Snap and Augustus for gene prediction only. This was set up so Maker would populate its annotations with the GFF file from the first round
  • Example of options here
• 4. Snap: Second round of SNAP training, using the Maker's second round output
  • The commands are the same as used in training SNAP in the first round
• 5. Maker: Final round of annotation with the most recent SNAP model
  • The maker options are the same as in the second round, just with the updated SNAP model
• 6. Maker, Seqkit: Extract Maker's final GFF and fasta transcripts using the tools packaged with Maker, combine the each scaffolds' annotations into a single file and remove duplicates with Seqkit

fasta_merge -d <maker_output_directory>/<round3>_master_datastore.log
seqkit rmdup round3.fasta > round3_deduplicated.fasta

• 7. BUSCO: Assess BUSCO completeness of final transcripts with BUSCO in transcriptome mode
• Output: GFF files containing annotations for each sample, with predicted transcripts and proteins in fasta format.

Variant calling and identification

• Input:
  • Cleaned reads (after trimming and filtering as in genome assembly)
  • Reference sequence GCF_000961545.1
• The variant calling steps are identical to that of Khalfan (2020)'s implementation (an explanation can be found in the link), with covariate analysis disabled (due to an R bug) and updated from the DSL 1 (which is deprecated in the most recent version of Nextflow) to DSL 2
  • The short reads were aligned to the reference
• Output:
  • VCF files for each sample
  • Variant stats files for each sample, summarizing the number of variants (in each category) a given gene was found to have
• Genes of interest were first identified by manually cross-referencing BUSCO output with the GCF_000961545.1 gff file
  • A script that automates the process using bcftools, when given a plain-text file of gene locations is included in the bin directory.
    • It also counts the number of variants present within each range.

bcftools view <contig_name>:<start>-<stop> <indexed_vcf>

• The bin directory contains python scripts for extracting single-copy BUSCO gene sequences from BUSCO output and combining them across samples.
• Note: If you intend to predict variant effects with SnpEff and have used the GCF_000961545 reference for variant calling, downloading SnpEff's database of the reference GCF_000961545 won't work. Clarification and further instruction in the extended readme

BUSCO gene extraction

• Input
  • GCF_000961545 reference gff and fasta file
  • BUSCO output tables for each assembly
  • Cleaned reads
• Output
  • Multiple sequence alignments for all common single-copy BUSCO genes
  • KALLISTO tables describing the abundance of each single-copy gene
• 1. A script (busco_to_gff.sh) was written to map every single-copy BUSCO gene to a gene described in the gff file, obtaining a more precise range in the process.
  • The gene table was filtered to leave only the genes that were shared by every sample (3186 genes). A list of these shared genes was created by processing the output tables with a python script

• 2. Liftoff: Lift over genome annotations from GCF_000961545 reference gff onto scaffolds, generating a lifted gff file

liftoff <scaffolds> <reference_fasta> -g <reference_gff> -o <scaffolds>_lifted.gff

• 3. awk: Use the BUSCO-gff mapping from step 1. to filter the single-copy BUSCO genes from the lifted gffs, generating a tsv file with their new locations on the lifted gff*
  • *The original BUSCO output table describes the BUSCO gene locations specific for the GCF_000961545 reference. Their locations may be different with each assembly
• 4. gffread: Extract the sequences of the BUSCO genes in fasta format using the coordinates specified from the previous file. Every gene is stored in a separate fasta file
• 5. MAFFT: Combine the fasta files of the same genes for each sample into a single file and perform a multiple sequence alignment using MAFFT

mafft --preservecase --auto <combined_gene_fastas> > aligned.fasta

• 6. kallisto: Combine the per-sample fasta files, generating a set of per-sample gene sequences. Pass the cleaned reads for the sample as input to kallisto for transcript quantification

kallisto index -i index  <sample_combined_fasta> # Create a kallisto index (the "-i" flag specifies the index filename)
kallisto quant -i index -o <sample_quantified> <reads1> <reads2> # The "-o" flag specifies output directory name

• 7. R: Using the per-gene multiple sequence alignments from step 5, construct a phylogenetic tree with the neighbor joining method in R, then calculate the Generalized Robinson Foulds distance between the gene's tree and a reference tree

GO Annotation and enrichment analysis

• Input
  • SnpEff variant stats table
  • SnpEff-annotated VCF files
  • Mapping file of NCBI gene ids to UniProt ids and GO annotation
    • A Python script is provided in the bin directory for doing this. It takes a plain text file of NCBI gene ids (each id separated by newlines) and returns a CSV file with each gene's UniProt id and GO annotations (if they exist)
  • The GO terminology file, which can be downloaded from here. "go-basic.obo" will suffice
• Output
  • VCF files with GO annotations
  • GO enrichment analysis results file

• 1. SnpSift: Filter VCF file to keep "high" effect variants and to remove variants with warnings
• 2. R: Add GO terms into the annotation field of the VCF file
• 3. R: Format list of genes and their GO annotations for enrichment analysis
• 4. Ontologizer: Perform enrichment analysis on the gene list
  • The gene population was defined as all genes that had variants and GO annotations
  • The study set was defined was genes in the upper quartile of "high" variants

Pipeline usage

Parameters

Each of the above procedures is controlled by the controller.config file, and is divided into separate workflows rather a single continuous one (set up this way due to hardware limitations). Running each procedure in order (given in controller.config) is thus important.

• Instructions on how to set parameters and what each one does is in the file

paths.config and expectations for input data

The paths.config file specifies the paths to input data and the pipeline's output

• By default, all results files will be generated in the results directory where the nextflow process was run. The folders will be numbered by their order in the pipeline.

• The Specification: line in the following denotes which parameter in paths.config a given piece of input data is denoted with in the pipeline

  • This information will also be in the file itself

• Required files

• These are the files that you will need to supply yourself in order to run the pipeline

  • DNA short reads
    • Should paired-end in gzipped fastq format, using the standard Illumina naming convention but with the lane number removed
    • E.g. S1_R1_001.fastq.gz instead of S1_L001_R1_001.fastq.gz
    • Specification: params.raw
  • RNA short reads
    • The RNA short reads can follow any naming convention, as long as they are paired-end, in fastq format (gzipped should work too) and suffixed with _1 or _2
    • Specification: params.rna
  • Reference genome fasta, gff and gbk files for study organism
    • Download from the NCBI
  • Reference genome fasta files from closely-related species
    • These are used for repeat library construction
    • Specification: params.genomes
  • Augustus gene model trained on study organism reference genome
    • Instructions are on the web server, note that you will need to edit the default NCBI fasta headers to remove all non-alpha numeric characters. The safest bet is to just keep the chromosome id and remove everything else
      • Default: >NW_015971139.1 Sporothrix schenckii 1099-18 chromosome Unknown Cont38, whole genome shotgun sequence
      • Edited: >NW015971139
    • Specification: place this in the Augustus data directory, which by default is <PATH_TO_AUGUSTUS_DOWNLOAD_DIRECTORY>/config/species
  • GO terminology file
    • Download from here. "go-basic.obo" will suffice
    • Specification: params.go_ontology

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