QC pipelines

deeptools tools for exploring deep sequencing data

• Analysis of correlation of bamfiles

Mapping pipelines

Automated FASTQ to CNV workflow

An end-to-end Nextflow workflow now lives in main.nf. It automates:

1. Raw-read FastQC
2. fastp trimming
3. Trimmed-read FastQC
4. BWA-MEM mapping with read groups
5. samtools duplicate marking and filtered BAM generation with samtools flagstat
6. MultiQC summary
7. CNV calling with CNVkit, GATK CNV, or both

Files

• config/samples.tsv - one row per sample, with FASTQ paths and CNV reference group.
• nextflow.config - reference genome, targets, output directory, executors, resources, and CNV settings.
• envs/qc_mapping_cnv.yaml - Conda environment for Nextflow and the required tools.
• main.nf - automated pipeline definition.

Quick start

Edit config/samples.tsv and nextflow.config, then run:

conda env create -f envs/qc_mapping_cnv.yaml
conda activate qc_mapping_cnv
nextflow run main.nf -profile conda -resume

For an HPC run using the built-in SLURM profile:

nextflow run main.nf -profile slurm -resume

CNV modes

Set params.cnv_method in nextflow.config:

• cnvkit - build CNVkit references from samples marked normal, control, or reference, then call .called.cns files.
• gatk - preprocess target intervals, collect read counts, build a panel of normals, denoise, segment, and call .called.seg files.
• both - run both CNVkit and GATK CNV outputs from the same marked BAMs.

Samples with cnv_role set to normal, control, or reference are used for CNV references. Other roles, such as case or treated, are CNV-called against their cnv_reference_group.

Generic ChIP-seq Nextflow workflow

For ChIP-seq, a separate generic Nextflow workflow is available in chipseq_main.nf with configuration in chipseq_nextflow.config.

Why this is generic

• Input FASTQ files are fully controlled by config/chipseq_samples.tsv (no fixed filename pattern is required).
• Sample names can be any value in the sample column.
• The control_sample column pairs each ChIP sample with the exact Input sample ID for downstream bamCompare output.
• Paths and run parameters are set in chipseq_nextflow.config, including reference genome, mapping filters, bigWig options, and bamCompare settings (ratio mode with duplicate ignoring, no scale-factor normalization).

Files

• chipseq_main.nf - ChIP-seq QC, trimming, mapping/filtering, optional bigWig generation, optional ChIP/Input bamCompare, and MultiQC.
• chipseq_nextflow.config - ChIP-seq pipeline parameters and runtime profiles.
• config/chipseq_samples.tsv - sample sheet template; edit file paths, sample IDs, and control_sample pairing.

Quick start

nextflow run chipseq_main.nf -c chipseq_nextflow.config -profile conda -resume

For SLURM:

nextflow run chipseq_main.nf -c chipseq_nextflow.config -profile slurm -resume

Copy Number Variation (CNV) Analysis Pipelines

GATK & CNVkit Workflows for Targeted and Whole-Exome Sequencing

Overview

This repository provides reproducible, HPC-ready workflows for copy number variation (CNV) analysis using two independent pipelines:

1. GATK CNV Workflow — Best-practice CNV calling using the Broad Institute's Genome Analysis Toolkit (GATK).
2. CNVkit Workflow — Coverage-based CNV detection using CNVkit for targeted and hybrid capture sequencing.

Each workflow includes:

• Ready-to-run SLURM batch scripts for HPC clusters
• Step-by-step setup and execution guides
• Notes on parameters, expected outputs, and biological interpretation

Reproducibility

All scripts are fully modular and can be customized per project. Each step includes:

• Input and output definitions
• Environment setup instructions
• Optional parameters for advanced tuning To rerun or adapt:
• Update paths in the scripts (BAM, REF, TARGETS, etc.)
• Submit each job to the HPC queue using sbatch
• Review logs and resulting CNV tables/plots

Duplicate and fusion genes

• Manual inspection of fusioned genes
• Using the "supplementary", "mates on different chromosomes", and mates on same chromosomes but in distant than expected" reads.
• Compare it with Normal.

Repository Structure

Folder Organization

• CNVkit: Contains scripts and tools for copy number variation analysis using CNVkit.
• GATK_CNV: Includes files related to the Genome Analysis Toolkit for copy number variations.
• Mapping: Houses the mapping files and scripts used for aligning sequencing data.
• QC: Contains quality control metrics and reports for the datasets.
• ChIP-Seq_Chromatin_analysis: Includes analysis scripts and data related to ChIP-Seq experiments.
• Duplication_fusion_genes: Contains files related to the analysis of gene duplications and fusions.
• deeptools: Houses scripts and tools used for deep data analysis.
• bcftools: Contains tools for variant calling and manipulating VCF files.
• fastq: Houses FASTQ files of raw sequencing data.

Technology Stack

• Shell: 84.9%
• Jupyter Notebook: 15.1%

Citation

relevant tools:

• GATK CNV – Benjamin et al., Nature Genetics (2013)
• CNVkit – Talevich et al., PLOS Computational Biology (2016)