Nextflow Somatic Short-Read Variant Calling

This repository implements a comprehensive Nextflow pipeline for somatic short-read variant calling, supporting multiple small variant callers (Mutect2, Strelka, DeepSomatic) and structural variant calling (Manta) with integrated quality control and annotation.

Pipeline Architecture

Primary Use Case

Primary support: 30X whole-genome sequencing (WGS) Illumina short reads with GRCh38 (hg38) alignment

The pipeline is optimized for the following workflow:

• Input: Illumina short-read FASTQ files (~30X coverage) or pre-aligned BAM/CRAM files
• Quality Filtering: FASTP for read-level trimming and quality filtering
• Alignment: BWA-MEM2 alignment to GRCh38 (hg38) reference genome (or skip if BAM/CRAM input)
• Preprocessing: Optional alignment preprocessing (disabled by default)
• Variant Calling:
  • Small Variants: Mutect2, Strelka, or DeepSomatic (somatic SNP/INDEL detection)
  • Structural Variants: Manta (SV detection)
• Quality Metrics:
  • Variant statistics (bcftools stats)
  • Genomic coverage analysis (bedtools genomecov)
• Variant Annotation:
  • SnpEff (annotation database)
  • VEP (Ensembl Variant Effect Predictor)
• Output: Annotated VCF files with comprehensive quality metrics

Configuration for Primary Use Case

# Default configuration uses:
# - Mutect2 as small variant caller (or strelka/deepsomatic)
# - Manta as structural variant caller (optional)
# - HG38/GRCh38 reference genome
# - FASTP quality filtering
# - Preprocessing optional (skip_preprocessor: true by default)
# - SnpEff + VEP annotation enabled

pixi run nextflow run main.nf -profile docker -resume

Quick Start

1. Prepare a Samplesheet

Create a CSV samplesheet with your input. The pipeline requires tumor-normal paired samples and supports three input modes.

Required Columns (all modes):

• patient: Patient identifier (same for normal/tumor pairs)
• sex: Sex of the patient (M/F)
• status: Sample type (0 = normal, 1 = tumor)
• sample: Sample identifier
• lane: Sequencing lane (optional, defaults to L001)

Mode A: FASTQ Input (Full Pipeline)

patient,sex,status,sample,lane,fastq_1,fastq_2
P001,M,0,P001_N,L001,/path/to/P001_N_R1.fastq.gz,/path/to/P001_N_R2.fastq.gz
P001,M,1,P001_T,L001,/path/to/P001_T_R1.fastq.gz,/path/to/P001_T_R2.fastq.gz
P002,F,0,P002_N,L001,/path/to/P002_N_R1.fastq.gz,/path/to/P002_N_R2.fastq.gz
P002,F,1,P002_T,L001,/path/to/P002_T_R1.fastq.gz,/path/to/P002_T_R2.fastq.gz

Mode B: BAM Input (Skip Alignment)

patient,sex,status,sample,lane,bam,bai
P001,M,0,P001_N,L001,/path/to/P001_N.bam,/path/to/P001_N.bam.bai
P001,M,1,P001_T,L001,/path/to/P001_T.bam,/path/to/P001_T.bam.bai
P002,F,0,P002_N,L001,/path/to/P002_N.bam,/path/to/P002_N.bam.bai
P002,F,1,P002_T,L001,/path/to/P002_T.bam,/path/to/P002_T.bam.bai

Mode C: CRAM Input (Skip Alignment + Auto-Convert)

patient,sex,status,sample,lane,cram,crai
P001,M,0,P001_N,L001,/path/to/P001_N.cram,/path/to/P001_N.cram.crai
P001,M,1,P001_T,L001,/path/to/P001_T.cram,/path/to/P001_T.cram.crai
P002,F,0,P002_N,L001,/path/to/P002_N.cram,/path/to/P002_N.cram.crai
P002,F,1,P002_T,L001,/path/to/P002_T.cram,/path/to/P002_T.cram.crai

CRAM Benefits:

• Compressed input: CRAM files are ~4x smaller than BAM (78% compression)
• Faster pipeline: Skip alignment step when re-running variant calling
• Automatic conversion: CRAM→BAM conversion integrated into pipeline
• Supported for all callers: Mutect2, Strelka, DeepSomatic, and Manta

2. Run the Pipeline

Standard Run (FASTQ Input)

nextflow run main.nf \
  --input samplesheet.csv \
  --profile docker \
  -resume

CRAM Input Example

# Run with CRAM files (auto-converts to BAM before variant calling)
nextflow run main.nf \
  --input samplesheet_cram.csv \
  --profile docker \
  -resume

Advanced Options

# With Manta for structural variants
nextflow run main.nf \
  --input samplesheet.csv \
  --structural_variant_caller manta \
  --profile docker \
  -resume

# Use Strelka instead of Mutect2
nextflow run main.nf \
  --input samplesheet.csv \
  --small_variant_caller strelka \
  --profile docker \
  -resume

# Use DeepSomatic with WGS model
nextflow run main.nf \
  --input samplesheet.csv \
  --small_variant_caller deepsomatic \
  --deepsomatic_model_type WGS \
  --profile docker \
  -resume

# Skip annotation for faster processing
nextflow run main.nf \
  --input samplesheet.csv \
  --profile docker \
  -resume

# Enable alignment preprocessing (e.g., BQSR)
nextflow run main.nf \
  --input samplesheet.csv \
  --preprocessor gatk \
  --profile docker \
  -resume

For test mode with sample data:

nextflow run main.nf -profile docker,test -resume

3. View Results

Output files will be generated in the results/ directory. File structure depends on input mode:

FASTQ Input Results:

• results/alignment/*.bam - Aligned BAM files
• results/variant_calling/*.vcf.gz - Raw variant calls
• results/variant_annotation/*.vcf - Annotated variants
• results/qc/ - Quality metrics (variant stats, coverage bedgraph)

CRAM Input Results:

• results/variant_calling/*.vcf.gz - Raw variant calls (from converted BAM)
• results/variant_annotation/*.vcf - Annotated variants
• results/qc/ - Quality metrics (variant stats, coverage bedgraph)
• No intermediate BAM files (discarded after variant calling unless configured otherwise)

Common output files:

• results/pipeline_info/ - Execution timeline and trace logs

For more advanced usage and configuration options, see the Pipeline Architecture documentation.

Key Features

• Multiple Input Formats: FASTQ (full pipeline), BAM, and CRAM (skip alignment, auto-convert)
• Somatic Small Variant Callers: Mutect2, Strelka, DeepSomatic
• Somatic Structural Variant Callers: Manta
• Tumor-Normal Pairing: Built-in support for matched tumor-normal samples
• Quality Metrics: Variant statistics (bcftools), genomic coverage (bedtools)
• Variant Annotation: SnpEff, VEP
• CRAM Support: Built-in CRAM→BAM conversion for efficient re-calling
• Flexible Configuration: Container support (Docker/Singularity), multiple profiles
• Alignment Preprocessing: Optional BQSR and other preprocessing steps

License

MIT (LICENSE)