vartracker 2.2.0

Track the persistence (or loss) of mutations during long-term passaging

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vartracker

A bioinformatics pipeline to summarise variants called against a reference in a longitudinal study design. Written to investigate longitudinal sequencing data from long-term passaging of SARS-CoV-2. However, with appropriate reference data it can be expanded to other pathogens too.

Author: Dr Charles Foster

Table of Contents

• Features
• Installation
• Quick Start
• Output
• What does vartracker do?
• Citation
• License
• Contributing
• Support

Features

• Track mutation persistence across longitudinal samples
• Comprehensive variant analysis including amino acid consequences
• Built-in SARS-CoV-2 reference data and annotations
• Integration with functional mutation databases (literature)
• Automated plotting and statistical analysis
• Support for both SNPs and indels
• Quality control metrics for variants

Installation

The simplest, and preferred, installation route is via a conda-compatible package manager (pixi, conda, or mamba).

Conda/Mamba

vartracker and its external bioinformatics dependencies can be installed from the package channels directly:

mamba create -n vartracker -c conda-forge -c bioconda vartracker
mamba activate vartracker

If you prefer conda:

conda create -n vartracker -c conda-forge -c bioconda vartracker
conda activate vartracker

Pixi

You can also install vartracker via pixi either globally or inside an existing workspace:

# global installation
pixi global install vartracker

# add into an existing workspace
pixi workspace channel add conda-forge
pixi workspace channel add bioconda
pixi add vartracker

Biocontainers

Every Bioconda package is available as a container image for usage with your preferred container runtime. An example command to pull vartracker with docker:

# latest build
docker pull quay.io/biocontainers/vartracker:latest
# specific tag
docker pull quay.io/biocontainers/vartracker:<tag>

Alternative: PyPI (Python-only)

If you want a Python-only install (requires Python 3.11 or newer), you can still install from PyPI. In that case you must provide the required external bioinformatics tools yourself (see below):

pip install vartracker

External Dependencies

vartracker shells out to a handful of bioinformatics tools. Make sure they are discoverable on PATH before running the CLI. Minimum tested versions are tracked in docs/DEPENDENCIES.md.

• bcftools and tabix – required for all modes
• samtools, lofreq, fastp, bwa, and snakemake – required for the bam and end-to-end Snakemake workflows

If you only plan to run vartracker vcf against pre-generated VCFs, the first pair is sufficient. The additional tools are needed whenever you ask vartracker to align reads or call variants for you. Consensus genome generation in the bam and end-to-end workflows uses bcftools and samtools; it does not require bedtools.

Note: the pinned micromamba environment installs tabix/bgzip via htslib.

Installing bcftools and tabix

On macOS:

# Using Homebrew
brew install bcftools htslib samtools fastp bwa
# lofreq is available via bioconda (requires conda/mamba)
conda install -c bioconda lofreq

# Using MacPorts
sudo port install bcftools htslib samtools fastp bwa

On Linux (Ubuntu/Debian):

sudo apt-get update
sudo apt-get install bcftools tabix samtools fastp bwa
# lofreq is easiest to install via bioconda on Debian-based systems:
conda install -c bioconda lofreq

On Linux (CentOS/RHEL/Fedora):

# CentOS/RHEL with EPEL
sudo yum install epel-release
sudo yum install bcftools htslib samtools fastp bwa

# Fedora
sudo dnf install bcftools htslib samtools fastp bwa
# Install lofreq via bioconda on RPM-based systems:
conda install -c bioconda lofreq

Using conda:

conda install -c bioconda bcftools samtools tabix fastp bwa lofreq

Development Installation

For development or to get the latest version (requires Python 3.11+):

git clone https://github.com/charlesfoster/vartracker.git
cd vartracker
pip install -e .[dev]
pre-commit install

Docker: self-build

Build a container image that bundles Python, vartracker, and all external bioinformatics tools:

  # released version on Bioconda
  docker build -t vartracker:release .
  # development version
  docker build -t vartracker:dev -f Dockerfile.dev .

Docker is a self-contained reproducible option. If you publish the image, record the digest and set it when running to include it in the run manifest:

export VARTRACKER_CONTAINER_IMAGE=ghcr.io/your-org/vartracker:2.0.0
export VARTRACKER_CONTAINER_DIGEST=sha256:...

Run workflows by mounting your data directory into the container. The command below analyses an input CSV located in the current directory and writes results beside it:

docker run --rm -v "$(pwd)":/workspace vartracker \
  vcf /workspace/inputs/vcf_inputs.csv \
  --outdir /workspace/results

Quick Start

After installation, vartracker will be available as a command-line tool:

vartracker --help

Typical commands

# Analyse pre-called VCFs plus coverage files
vartracker vcf path/to/vcf_inputs.csv --outdir results/vcf_run

# Run BAMs through the Snakemake workflow, then summarise variants
vartracker bam path/to/bam_inputs.csv \
  --snakemake-outdir work/bam_pipeline \
  --outdir results/bam_summary

# Start from raw reads (FASTQ) and run the full pipeline
vartracker end-to-end path/to/read_inputs.csv \
  --cores 12 \
  --outdir results/e2e_summary

# Re-plot a heatmap from an existing vartracker results file
vartracker plot heatmap results/results.csv \
  --aa-exclude "*frameshift*" \
  --x-labels sample-number \
  --literature-csv results/sample.literature_database_hits.full.csv \
  --title "Variant allele frequencies"

# Plot whole-dataset turnover from an existing results file
vartracker plot turnover results/results.csv

# Plot collapsed variant frequencies along the genome
vartracker plot genome results/results.csv

# Zoom to a gene region, optionally using amino-acid coordinates
vartracker plot genome results/results.csv --gene F --aa-scale

# Plot selected variant trajectories from an existing results file
vartracker plot trajectory results/results.csv \
  --variants "S:D614G,S:E484K,S:N501Y"

# Plot takeover-style trajectories using AF thresholds
vartracker plot trajectory results/results.csv \
  --thresholds 0.5,0.9 \
  --crossing-only

# Generate a template spreadsheet for a directory of files
vartracker prepare spreadsheet --mode e2e --dir data/passaging --out inputs.csv

# Build a reference FASTA+GFF3 bundle from GenBank accessions
vartracker prepare reference --accessions CY114381,CY114382 --outdir refs/flu --prefix flu_ref

# Exercise the bundled smoke-test dataset
vartracker vcf --test
vartracker bam --test
vartracker end-to-end --test

All modes understand --test, which copies the example dataset from vartracker/test_data into a temporary directory, resolves relative paths, and runs the appropriate workflow.

Temporary LoFreq note:

• In bam and end-to-end mode, vartracker currently caps lofreq call-parallel at 8 threads even if --cores is higher.
• This is a temporary workaround for an older Bioconda LoFreq build that can fail during call-parallel final filtering when many shards produce an excessively long merged VCF header.
• The cap will be revisited once an updated LoFreq build is available through Bioconda.

LoFreq primer-overlap rescue:

• Amplicon schemes can create a specific LoFreq false-negative mode: after primer clipping, reads from one strand may be soft clipped at primer-overlap sites, so a genuine near-fixed variant can fail LoFreq's default strand-bias filter.
• bam and end-to-end therefore run LoFreq with --no-default-filter, then apply the normal lofreq filter step so standard LoFreq PASS calls are unchanged.
• With the default --lofreq-primer-rescue auto, the rescue step runs only when --primer-bed is supplied. In other words, auto means "use primer rescue when an amplicon primer scheme has been explicitly provided."
• In end-to-end mode, the same --primer-bed is used for samtools ampliconclip and for rescue. In bam mode, vartracker does not clip the input BAMs; the primer BED is used only to identify primer-overlap sites for rescue.
• Rescue candidates must be single-ALT SNPs that overlap a primer interval, fail the default LoFreq filter, and pass conservative near-fixed thresholds (AF>=0.95, DP>=100, DP4 alt count>=95, QUAL>=100, DP4 ref count<=20) with one-sided alternate-strand support. Indels, multi-ALT records, lower-frequency variants, and non-primer-overlap variants are not rescued by this rule.
• Rescued variants are marked with FILTER=RESCUED_PRIMER_OVERLAP, INFO/PRIMER_OVERLAP, and INFO/RESCUED_BY=overlap_primer_interval; per-sample details are written to <sample>_variants.rescued.tsv and listed in the updated spreadsheet as lofreq_rescued_tsv.
• Use --lofreq-primer-rescue off to disable rescue even when a primer BED is supplied, or --lofreq-primer-rescue on to require rescue and fail if --primer-bed is missing. The rescue thresholds can be adjusted with the --lofreq-rescue-* options.

Example amplicon run with primer rescue:

vartracker end-to-end inputs.csv \
  --primer-bed primers.bed \
  --ampliconclip-tolerance 1 \
  --outdir results/e2e_amplicon

Input Spreadsheets

Every CLI mode reads the same canonical columns:

• sample_name (required) – display name for the sample
• sample_number (required) – passage/order index used in longitudinal plots
• reads1, reads2 – FASTQ paths (required for end-to-end, optional elsewhere). The pipeline runs in single-end mode (leave the reads2 column empty) but the results are less well tested.
• bam – BAM file aligned against the SARS-CoV-2 reference
• vcf – bgzipped VCF containing variant calls with depth (DP) and allele-frequency tags
• coverage – per-base coverage TSV with columns reference<TAB>position<TAB>depth

Mode-specific expectations:

• VCF mode requires vcf and coverage, while leaving reads*/bam empty.
• BAM mode requires bam and will fill vcf + coverage during the workflow.
• End-to-end mode requires reads1 (and optionally reads2); remaining fields are generated.

The bam and end-to-end workflows also write two consensus FASTA columns to the updated Snakemake spreadsheet, plus the LoFreq rescue audit column: consensus for a simple consensus, iupac_consensus for an IUPAC-aware consensus, and lofreq_rescued_tsv for the per-sample primer-overlap rescue table. SNPs below --consensus-snp-min-af are ignored, SNPs from --consensus-snp-min-af up to --consensus-snp-thresh stay as reference bases in the simple consensus and become REF+ALT ambiguity codes in the IUPAC consensus, and SNPs at or above --consensus-snp-thresh become ALT bases. Indels are controlled separately by --consensus-indel-thresh in both consensus modes. Low-depth bases are masked as N, except for called deletion intervals so true deletions are not converted to low-depth masks.

Relative paths are resolved with respect to the CSV location, so you can store the sheet alongside your sequencing artefacts. The prepare spreadsheet subcommand can scaffold a CSV and highlight missing files.

Coverage files can be produced with samtools depth -aa sample.bam > sample_depth.txt or bedtools genomecov -ibam sample.bam -d. The file name suffix does not matter; vartracker checks for both .depth.txt and _depth.txt patterns when preparing its internal test dataset.

Mode-specific options

• vartracker vcf – accepts core analysis options such as --min-snv-freq, --min-indel-freq, --allele-frequency-tag, --multiallelic-overflow, --name, --outdir, --sample-cap, --manifest-level, and literature controls (--search-pokay, --literature-csv). Use --test to run the bundled smoke test.
• vartracker bam – everything from vcf, plus Snakemake options: --snakemake-outdir, --cores, --snakemake-dryrun, --verbose, --redo, --rulegraph, --primer-bed, --lofreq-primer-rescue, --consensus-snp-min-af, --consensus-snp-thresh, and --consensus-indel-thresh.
• vartracker end-to-end – similar to bam, with optional amplicon clipping controls: --primer-bed and --ampliconclip-tolerance (default: 1). Supplying --primer-bed also enables LoFreq primer-overlap rescue by default.
• vartracker plot heatmap (hm) – regenerate the heatmap from an existing vartracker results CSV, including all heatmap customization filters.
• vartracker plot genome – plot SNP positions along the genome or a selected gene region using all observed allele-frequency values for each variant.
• vartracker plot trajectory – plot allele-frequency trajectories for a selected or auto-ranked subset of variants, optionally in takeover mode using threshold lines and threshold-based filtering.
• vartracker plot turnover – plot new-versus-lost longitudinal turnover from the filtered result set.
• vartracker plot lifespan – plot first-to-last detection spans for a selected or auto-ranked subset of variants.

Consequence-calling note:

• Vartracker keeps distinct ALT alleles at the same position separate during preprocessing, then rejoins them immediately before bcftools csq so codon-level consequences can still be inferred correctly.
• If more than two ALT alleles remain present in a single sample at one genomic position after frequency filtering, vartracker defaults to stopping with an informative error before bcftools csq. This is the safest behavior and the default --multiallelic-overflow error mode.
• --multiallelic-overflow drop-lowest-af continues by removing the lowest-frequency retained ALT allele(s) for the affected sample before bcftools csq, and prints a warning describing the site and the dropped allele(s).
• --multiallelic-overflow skip-site continues by skipping consequence calling for the affected site entirely, leaving those variants in the results as unannotated rows and printing a warning describing the site.

Heatmap filtering:

• vcf, bam, and end-to-end always write the default heatmap. To customize heatmap content after a run, use vartracker plot heatmap results.csv [options].
• By default, all consequence classes are included except joint variants. Use --include-joint to show joint variants.
• --aa-exclude: comma-separated type_of_change patterns to exclude. Wildcards are supported.
• --aa-include: comma-separated type_of_change patterns to include.
• --only-persistent: only include new_persistent variants.
• --only-new: only include variants with variant_status == new.
• --gene-include and --gene-exclude: comma-separated gene patterns.
• --variant-type: comma-separated variant-type patterns such as snp or indel.
• --qc: comma-separated all_samples_pass_qc patterns to include. Accepted values include true, false, pass, and fail.
• --min-prop-passing-qc: minimum fraction of samples that must pass per-sample QC.
• --min-persistence: minimum number of included samples in which the variant must be present.
• --min-max-af: minimum maximum allele frequency across included samples.
• --min-sample-af: minimum allele frequency that must be reached in at least one included sample.
• --sample-subset: comma-separated sample-name patterns to plot.
• --hide-singletons: hide variants present in only one included sample.
• --min-depth: minimum site depth a variant must reach in at least one included sample.
• --x-labels sample-number: label heatmap x-axis columns by sample_number instead of sample name.
• --title: set the heatmap plot title. The default is Variant allele frequencies.
• --literature-csv: include literature links in the interactive HTML heatmap using a literature hits CSV.
• Example: --aa-exclude "synonymous,*frameshift*,stop_gained"

Standalone plot filtering:

• --gene, --effect, --min-af, --max-af: restrict the plotted result set before ranking/selection.
• --variants or --variant-file: explicitly choose variants and preserve that order.
• --sample-min, --sample-max: restrict the passage/sample-number window.
• --persistent-only and --new-only: keep only persistent new variants or only variants with variant_status == new.
• trajectory and lifespan auto-select a limited subset by default (--top-n) to stay readable.
• turnover uses all filtered variants by default and is also written automatically during the main vcf/bam/end-to-end workflows as variant_turnover_plot.pdf.
• genome uses SNPs only by default, keeps all observed allele-frequency values for each plotted variant, and writes variant_genome_plot.pdf during the main workflows.

Standalone plot output:

• --out: write to an exact file path.
• --outdir: write beside the results CSV or into the chosen directory using deterministic names such as variant_trajectory_plot.pdf or variant_genome_plot.pdf.
• --format: choose pdf, png, or svg.
• --dpi: set raster output resolution.

Genome plot options:

• --gene: zoom to a single gene region.
• --aa-scale: with --gene, use amino-acid coordinates on the x-axis.
• --cds-scale: with --gene, use CDS-relative nucleotide coordinates on the x-axis.
• --focus-coords: highlight nucleotide or amino-acid coordinate ranges, depending on the current x-axis mode. Separate color groups with ;, ranges within a group with ,, and optionally prefix a group with Name:.
• --focus-region-file: read named focus region groups from a .json, .csv, or .tsv file for an inset legend.
• --show-intersections: add a compact Region | Variant table below the genome plot for highlighted-region hits.
• In the genome plot, undetected samples are rendered at the detection threshold rather than zero; by default this floor is 0.03, or --min-af if supplied, and the dashed guide line follows that same threshold.
• --include-indels: opt in to plotting indels too. This may be ambiguous or hard to interpret.
• The standalone genome plot auto-discovers reference_features.json beside results.csv; workflow runs generate this sidecar automatically.

Trajectory threshold mode:

• --thresholds: draw horizontal AF threshold lines, e.g. 0.5,0.9.
• --crossing-only: keep only variants crossing at least one supplied threshold.
• --label-threshold-crossers: label only threshold-crossing variants to reduce clutter.
• --crossing-rule: choose whether threshold equality counts (at_or_above) or requires a strict exceedance (strictly_above).

Standalone plot examples:

• vartracker plot genome results.csv
• vartracker plot genome results.csv --gene F
• vartracker plot genome results.csv --gene F --aa-scale
• vartracker plot genome results.csv --gene F --cds-scale --focus-coords "184-210,586-630"
• vartracker plot genome results.csv --focus-coords "150-300,900-1800"
• vartracker plot genome results.csv --focus-coords "62-69,196-210;31-42,323-332,379-399;254-277"
• vartracker plot genome results.csv --gene F --aa-scale --focus-coords "Ø:62-69,196-210;I:31-42,323-332,379-399;II:254-277"
• vartracker plot genome results.csv --gene F --aa-scale --focus-coords "Ø:62-69,196-210;I:31-42,323-332,379-399" --show-intersections
• vartracker plot genome results.csv --focus-region-file fusion_regions.json
• vartracker plot genome results.csv --gene F --aa-scale --focus-coords "50-120,180-220"
• vartracker plot turnover results.csv
• vartracker plot trajectory results.csv --variants "S:D614G,S:E484K"
• vartracker plot trajectory results.csv --thresholds 0.5,0.9
• vartracker plot trajectory results.csv --thresholds 0.5,0.9 --crossing-only
• vartracker plot trajectory results.csv --thresholds 0.5,0.9 --crossing-only --label-threshold-crossers
• vartracker plot lifespan results.csv --top-n 20 --persistent-only

Note:

• The standalone plot commands require results.csv files written by current vartracker versions, which now include a slash-separated sample_number column for stable passage ordering.

• vartracker prepare spreadsheet – specify --mode (vcf, bam, or e2e), --dir to scan, --out for the CSV, and --dry-run to preview without writing a file.

• vartracker prepare reference – build a merged FASTA/GFF3 bundle from GenBank nucleotide accessions. Use --accessions or --accession-file, plus --outdir. Optional flags: --prefix, --force, --keep-intermediates, --skip-csq-validation.

Using Literature Database

To search mutations against functional databases:

1. Set up a literature database (optional):

parse_pokay pokay_database.csv

This command automatically downloads the required literature files from the pokay repository into pokay_literature/NC_045512 (override with --download-dir) and writes the processed CSV for downstream analysis.

2. Run vartracker with literature search:

vartracker [mode] input_data.csv --literature-csv pokay_database.csv -o results/

Alternatively, pass --search-pokay to automatically download and search against the Pokay SARS-CoV-2 literature database.

Command Line Reference

usage: main.py [-h] [-V] {vcf,bam,end-to-end,e2e,prepare,schema} ...

positional arguments:
  {vcf,bam,end-to-end,e2e,prepare,schema}
    vcf                 Analyse VCF inputs
    bam                 Run the BAM preprocessing workflow
    end-to-end (e2e)    Run the end-to-end workflow (Snakemake + vartracker)
    prepare             Prepare inputs and references for vartracker
    schema              Print schemas for results tables or literature CSV input

options:
  -h, --help            show this help message and exit
  -V, --version         show program's version number and exit

Use vartracker <subcommand> --help to inspect the full list of mode-specific arguments.

Prepare reference from accessions

Use this workflow to build a bcftools csq-ready reference bundle from nucleotide accessions:

# Comma-separated accessions
# Example with influenza A segments
vartracker prepare reference \
  --accessions CY114381,CY114382,CY114383,CY114384,CY114385,CY114386,CY114387,CY114388 \
  --outdir refs/influenza_a \
  --prefix influenza_a_ref

# One accession per line in a file
vartracker prepare reference \
  --accession-file accessions.txt \
  --outdir refs/

Required external tools:

• bcftools for csq smoke validation

Outputs:

• <outdir>/<prefix>.fa
• <outdir>/<prefix>.gff3
• <outdir>/<prefix>.fa.fai
• <outdir>/prepare_metadata.json

Validation notes:

• Unless --skip-csq-validation is supplied, vartracker writes a dummy coding-region VCF variant and runs bcftools csq against the generated FASTA/GFF3.
• Validation fails fast if bcftools csq exits non-zero or if the output VCF does not contain BCSQ.

Troubleshooting:

• Accession fetch failures: verify accession spelling and network access to NCBI efetch.
• SeqID mismatch errors: confirm FASTA headers and GFF3 seqids match exactly.
• csq validation failure: inspect the stderr snippet in the error output and confirm bcftools version and annotation structure.

Installation Test

After installation you can verify the workflows using the bundled demonstration dataset:

vartracker vcf --test --outdir vartracker_vcf_test_results
vartracker bam --test --outdir vartracker_bam_test_results
vartracker end-to-end --test --outdir vartracker_e2e_test_results

Each command copies the example dataset, resolves relative paths, checks for the required external tools, and writes a self-contained set of results.

Output

vartracker produces several output files:

• results.csv: Comprehensive variant analysis with all metrics
• results_metadata.json: Output schema version and results metadata
• <sample>_variants.rescued.tsv (bam/end-to-end): LoFreq primer-overlap rescue audit table, empty when rescue is disabled or no variants are rescued
• new_mutations.csv: Mutations not present in the first sample
• persistent_new_mutations.csv: New mutations that persist to the final sample
• cumulative_mutations.pdf: Plot showing mutation accumulation over time
• mutations_per_gene.pdf: Gene-wise mutation statistics
• variant_allele_frequency_heatmap.html: Interactive heatmap with optional literature annotations
• variant_allele_frequency_heatmap.pdf: Heatmap of variant allele frequencies across passages
• literature_database_hits.*.csv: Functional annotation results (if literature search used)
• run_metadata.json: Provenance manifest capturing inputs, tool versions, and run status

By default the manifest is lightweight. Use --manifest-level deep to checksum all referenced input files (FASTQ/BAM/VCF/coverage) and include file sizes.

Output schema

The results table schema is documented in docs/OUTPUT_SCHEMA.md. You can also print it from the CLI:

vartracker schema results

To write the schema to a file instead, use:

vartracker schema results --out docs/output_schema.csv
vartracker schema results --out docs/output_schema.json --format json

To print the expected literature CSV structure for --literature-csv, use:

vartracker schema literature

What does vartracker do?

The pipeline performs the following analysis:

1. VCF Standardization: Normalizes and standardizes input VCF files, preserving distinct ALT alleles at the same genomic position

2. Variant Merging: Combines all longitudinal samples

3. Annotation: Adds amino acid consequences using bcftools csq on the merged VCF so sample-specific joint consequences are inferred from each sample's surviving ALT combination

4. Comprehensive Analysis: For each variant, determines:

   • Gene location and amino acid consequences
   • Variant type (SNP/indel) and change type (synonymous/missense/etc.)
   • Persistence across samples (new/original, persistent/transient)
   • Quality control metrics
   • Amino acid property changes
   • Allele frequency dynamics

5. Visualization: Generates plots for mutation accumulation and gene-wise statistics

6. Functional Annotation: (optional) Searches against literature databases for known functional impacts

Citation

When using vartracker, please cite the software release you used. Citation metadata is provided in CITATION.cff, and GitHub releases are archived on Zenodo.

• Foster, C. (2026). vartracker (Version 2.2.0). Zenodo. https://doi.org/10.5281/zenodo.18452274

Note: the DOI above is the Zenodo concept DOI for all versions; a version-specific DOI is minted by Zenodo after each GitHub release.

Also cite relevant methods or data sources, for example:

• Foster CSP, et al. Long-term serial passaging of SARS-CoV-2 reveals signatures of convergent evolution. Journal of Virology. 2025;99: e00363-25. doi:10.1128/jvi.00363-25
• Danecek P, Bonfield JK, Liddle J, Marshall J, Ohan V, Pollard MO, et al. Twelve years of SAMtools and BCFtools. GigaScience. 2021;10. doi:10.1093/gigascience/giab008
• Danecek P, McCarthy SA. BCFtools/csq: haplotype-aware variant consequences. Bioinformatics. 2017;33: 2037–2039. doi:10.1093/bioinformatics/btx100
• Wilm A, Aw PPK, Bertrand D, Yeo GHT, Ong SH, Wong CH, et al. LoFreq: a sequence-quality aware, ultra-sensitive variant caller for uncovering cell-population heterogeneity from high-throughput sequencing datasets. Nucleic Acids Res. 2012;40: 11189–11201. doi:10.1093/nar/gks918
• Chen S, Zhou Y, Chen Y, Gu J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics. 2018;34: i884–i890. doi:10.1093/bioinformatics/bty560
• Li H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. arXiv. 2013 [cited 13 Apr 2021]. Available: https://arxiv.org/abs/1303.3997v2
• Mölder F, Jablonski KP, Letcher B, Hall MB, Tomkins-Tinch CH, Sochat V, et al. Sustainable data analysis with Snakemake. F1000Res. 2021;10: 33. doi:10.12688/f1000research.29032.2

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Support

If you encounter any issues or have questions:

1. Check the documentation
2. Search existing issues
3. Create a new issue with detailed information about your problem