Neoantigen Discovery Pipeline

A comprehensive bioinformatics pipeline for detecting neoantigens from paired tumor-normal samples using whole exome sequencing (WES) and RNA-seq data.

Overview

This pipeline processes raw NGS data to identify potential neoantigens in cancer samples through the following steps:

1. Quality control and preprocessing of raw fastq files
2. Alignment to reference genome
3. HLA typing
4. Somatic variant calling
5. Variant annotation
6. Neoantigen prediction using multiple prediction algorithms

Documentation

• Installation Guide - How to set up the pipeline and its dependencies
• Usage Guide - How to run the pipeline with your data
• Troubleshooting Guide - Solutions for common issues

Quick Start

# Clone the repository
git clone https://github.com/iichelhadi/Neoantigen_detection_pipeline.git
cd Neoantigen_detection_pipeline

# Set up conda environments
conda env create -f envs/alignment.yaml
conda env create -f envs/variant_calling.yaml
conda env create -f envs/vep.yaml
conda env create -f envs/pvactools.yaml

# Configure the pipeline
cp config/config.sh.template config/config.sh
# Edit config.sh with your specific paths and settings

# Run the pipeline
bash scripts/run_pipeline.sh -c config/config.sh -s data/sample_info.tsv

Prerequisites

The pipeline requires the following software (installed via conda environments):

• TrimGalore and FastQC (quality control)
• BWA and HISAT2 (alignment)
• GATK and Samtools (variant calling)
• OptiType (HLA typing)
• VEP (Variant Effect Predictor)
• pVACtools (neoantigen prediction)

See the Installation Guide for detailed requirements and setup instructions.

Directory Structure

neoantigen-pipeline/
├── config/
│   └── config.sh.template
├── data/
│   └── sample_info.tsv.template
├── scripts/
│   ├── 01_preprocess.sh
│   ├── 02_dna_alignment.sh
│   ├── 03_rna_alignment.sh
│   ├── 04_hla_typing.sh
│   ├── 05_variant_calling.sh
│   ├── 06_variant_annotation.sh
│   ├── 07_neoantigen_prediction.sh
│   ├── run_pipeline.sh
│   ├── submit_pipeline.sh
│   └── utils/
│       └── check_dependencies.sh
├── envs/
│   ├── alignment.yaml
│   ├── variant_calling.yaml
│   ├── vep.yaml
│   └── pvactools.yaml
├── docs/
│   ├── installation.md
│   ├── usage.md
│   └── troubleshooting.md
└── README.md

Output

The pipeline generates the following output directories:

• trim_fq/: Quality-trimmed fastq files
• BAM_files/: Aligned BAM files
• optitype/: HLA typing results
• vcf_files/: Variant calls in VCF format
• vcf_files_annotated/: Annotated variants
• pvacseq/: Neoantigen predictions

For detailed information about running the pipeline and interpreting results, see the Usage Guide.

References

If you use this pipeline in your research, please cite the following tools:

• TrimGalore
• BWA-MEM: Li H. (2013) Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM
• GATK: McKenna A, et al. (2010) The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data
• OptiType: Szolek A, et al. (2014) OptiType: precision HLA typing from next-generation sequencing data
• VEP: McLaren W, et al. (2016) The Ensembl Variant Effect Predictor
• pVACtools: Hundal J, et al. (2020) pVACtools: a computational toolkit to identify and visualize cancer neoantigens

Citation

If you use this pipeline in your research, please cite:

Elhadi Iich. (2022). Neoantigen Detection Pipeline [Computer software]. https://github.com/iichelhadi/Neoantigen_detection_pipeline

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.