exomeflow 1.0.5

Production-quality Whole Exome Sequencing analysis pipeline

ExomeFlow: A Production-Quality Python WES Analysis Toolkit

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What is it?

ExomeFlow is a Python package that provides a complete, automated Whole Exome Sequencing (WES) analysis workflow — from raw FASTQ files to functionally annotated variants — in a single reproducible CLI command.

It aims to be the standard high-level pipeline for WES analysis in Python, combining GATK best-practice variant calling, hard filtering, and ANNOVAR annotation into one modular, maintainable package. It handles cohort-level processing (multiple samples), checkpointing for resumable runs, structured logging, and parallel execution out of the box.

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Table of Contents

• What is it?
• Main Features
• Pipeline Workflow
• Where to get it
• System Requirements
• Python Dependencies
• Quick Start
• Reference Files
• Input Convention
• Output Files
• Documentation
• Getting Help
• License
• Citation

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Main Features

Here are the things ExomeFlow does well:

• Automatic sample detection — scans an input directory and detects all paired-end samples from FASTQ filenames; no manifest file required
• Complete GATK best-practice workflow — fastp QC → BWA MEM alignment → coordinate sorting → duplicate marking → BQSR → HaplotypeCaller → hard filtering → ANNOVAR annotation
• Cohort processing — processes any number of samples sequentially or in parallel with --max-workers
• Checkpointing and resume — every completed step is recorded; an interrupted run resumes exactly where it left off without repeating work
• Automatic requirements check — verifies all system tools and Python packages before the pipeline starts, reporting every missing dependency at once
• Structured logging — per-sample log files plus a pipeline-wide log with INFO / WARNING / ERROR / SUCCESS levels
• GATK hard filters — applies GATK best-practice SNP and INDEL hard-filter thresholds and extracts PASS-only variants automatically
• ANNOVAR functional annotation — annotates variants against 8 databases: refGene, ClinVar, gnomAD, dbNSFP, COSMIC, ExAC, avSNP150, and dbscSNV
• Modular architecture — each pipeline step is an independent Python module; easy to extend or modify individual steps without touching the rest
• PyPI installable — pip install exomeflow; no Docker or Nextflow required

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Pipeline Workflow

Raw FASTQ
    │
    ▼
┌─────────────────────────────────────────────────────────┐
│  Step 1   fastp         Quality control & adapter trim   │
│           length ≥ 50 bp · base quality ≥ Q30            │
└──────────────────────────┬──────────────────────────────┘
                           │
                           ▼
┌─────────────────────────────────────────────────────────┐
│  Step 2   BWA MEM        Read alignment to hg38          │
│           -Y -K 100000000 · read-group tags set          │
└──────────────────────────┬──────────────────────────────┘
                           │
                           ▼
┌─────────────────────────────────────────────────────────┐
│  Step 3   GATK SortSam   Coordinate-sort BAM             │
│  Step 4   samtools       Flagstat alignment QC           │
│  Step 5   GATK MarkDuplicates   PCR duplicate removal    │
│  Step 6   GATK BuildBamIndex    BAI index                │
└──────────────────────────┬──────────────────────────────┘
                           │
                           ▼
┌─────────────────────────────────────────────────────────┐
│  Step 7   GATK BQSR      BaseRecalibrator + ApplyBQSR    │
│           Known sites: dbSNP · Mills · known indels      │
│           → recalibrated.bam  (IGV-ready)                │
└──────────────────────────┬──────────────────────────────┘
                           │
                           ▼
┌─────────────────────────────────────────────────────────┐
│  Step 8   GATK HaplotypeCaller   Variant calling         │
│           Exome intervals + padding · dbSNP annotation   │
└──────────────────────────┬──────────────────────────────┘
                           │
                    ┌──────┴──────┐
                    ▼             ▼
               SNP filters   INDEL filters
               (Step 9)       (Step 10)
                    └──────┬──────┘
                           │  MergeVcfs
                           ▼
┌─────────────────────────────────────────────────────────┐
│  Step 11  SelectVariants  Extract PASS-only variants     │
└──────────────────────────┬──────────────────────────────┘
                           │
                           ▼
┌─────────────────────────────────────────────────────────┐
│  Step 12  ANNOVAR         Functional annotation          │
│           refGene · ClinVar · gnomAD · dbNSFP · COSMIC   │
│           → multianno.vcf  +  multianno.txt              │
└─────────────────────────────────────────────────────────┘

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Where to get it

The source code is hosted on GitHub at: https://github.com/imrobintomar/exomeflow

Binary installers for the latest released version are available at the Python Package Index (PyPI).

# PyPI
pip install exomeflow

# Install latest development version from GitHub
pip install git+https://github.com/imrobintomar/exomeflow.git

The list of changes between each release can be found in the Release History.

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System Requirements

ExomeFlow calls the following external tools via the command line. They must be installed separately and available on your PATH.

Tool	Minimum Version	Install
BWA	≥ 0.7.17	conda install -c bioconda bwa
SAMtools	≥ 1.13	conda install -c bioconda samtools
GATK	≥ 4.6.0	Download jar + add to PATH
fastp	≥ 0.20.1	conda install -c bioconda fastp
Perl	≥ 5.26	conda install perl
ANNOVAR	latest	Register + download

Run python check_requirements.py to verify all tools are installed and meet minimum version requirements before starting the pipeline. This check also runs automatically as Step 0 of every pipeline run.

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Python Dependencies

• typer — Builds the CLI interface
• rich — Provides coloured terminal output and structured logging
• pandas — Data handling for variant count summaries
• matplotlib — Variant summary figure generation

See requirements.txt for exact minimum versions.

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Quick Start

1. Install

pip install exomeflow

2. Check requirements

python check_requirements.py

3. Prepare FASTQ files

fastq/
├── sample1_1.fastq.gz
├── sample1_2.fastq.gz
├── sample2_1.fastq.gz
└── sample2_2.fastq.gz

4. Run the pipeline

exomeflow run \
  --input-dir    fastq/ \
  --output       results/ \
  --reference    refs/hg38.fa \
  --dbsnp        refs/dbsnp.vcf.gz \
  --mills        refs/Mills_and_1000G_gold_standard.indels.hg38.vcf.gz \
  --known-indels refs/Homo_sapiens_assembly38.known_indels.vcf.gz \
  --intervals    refs/Illumina_Exome_TargetedRegions_v1.2.hg38.bed \
  --annovar-bin  /path/to/annovar \
  --annovar-db   /path/to/annovar/humandb \
  --threads      32 \
  --max-workers  2

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Reference Files

File	Description
hg38.fa	BWA-indexed reference genome (UCSC / GATK resource bundle)
dbsnp.vcf.gz	dbSNP VCF — bgzipped + tabix-indexed
Mills_and_1000G_gold_standard.indels.hg38.vcf.gz	Mills gold standard indels
Homo_sapiens_assembly38.known_indels.vcf.gz	Known indels for BQSR
Exome capture BED	From your capture kit vendor (Illumina / Twist / Agilent)
ANNOVAR humandb	hg38 annotation databases

Download the GATK resource bundle:

gsutil -m cp -r gs://gatk-best-practices/somatic-hg38/ .

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Input Convention

ExomeFlow automatically detects samples from paired-end FASTQ filenames. Files must follow the pattern:

<sample_id>_1.fastq.gz   ← Read 1
<sample_id>_2.fastq.gz   ← Read 2

The sample_id can be any string — SRR accession, patient ID, etc.

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Output Files

File	Description
Mapsam/<sample>_recalibrated.bam	Analysis-ready BAM — open in IGV
VCF/<sample>.vcf	Raw HaplotypeCaller output
VCF/<sample>_PASS.vcf	PASS-only hard-filtered variants
VCF/<sample>.annovar.hg38_multianno.vcf	Annotated VCF
VCF/<sample>.annovar.hg38_multianno.txt	Annotated tab-delimited table
filtered_fastp/<sample>_fastp.html	fastp QC report
Mapsam/<sample>_flagstat.txt	Alignment statistics
logs/analysis_<timestamp>.log	Full pipeline log
logs/<sample>_<timestamp>.log	Per-sample log

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Documentation

Full usage documentation is available in USAGE.md, including:

• Complete CLI option reference
• How to resume interrupted runs
• How to tune parallel processing
• Common errors and fixes
• Quick reference card

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Getting Help

For usage questions, please open a GitHub Issue.

Bug reports, feature requests, and general questions are all welcome.

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License

MIT

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Citation

If you use ExomeFlow in your research, please cite:

Robin Tomar. ExomeFlow: A Production-Quality Python Package for Automated Whole Exome Sequencing Analysis. AIIMS New Delhi, 2025. https://pypi.org/project/exomeflow/

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Built for the bioinformatics community · Robin Tomar, AIIMS New Delhi