BDB-Genomics ChIP-seq Pipeline

A scalable, reproducible, and modular Snakemake pipeline for end-to-end processing of paired-end ChIP-seq data. Starting from raw FASTQ files, the pipeline performs quality control, alignment, duplicate removal, peak calling, coverage generation, control-aware signal normalization, and downstream analysis.

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Requirements

Software	Version	Purpose
Snakemake	≥ 8.0	Workflow manager
Singularity / Apptainer	≥ 3.8	Container runtime
Conda / Mamba	any	Environment manager
Python	≥ 3.10	Config validation script

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Installation

# Create and activate a Snakemake environment
conda create -n snakemake -c bioconda -c conda-forge snakemake singularity graphviz -y
conda activate snakemake

# Clone the pipeline
git clone https://github.com/<your-org>/chipseq_pipeline.git
cd chipseq_pipeline

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Quick Start

1. Prepare reference files

Place the following in data/reference/:

File	Description
genome.fa	Reference genome FASTA
genome.chrom.sizes	Chromosome sizes (from samtools faidx + cut -f1,2)
ENCODE_blacklist.bed	ENCODE blacklist (e.g., hg38)
annotation.gtf	Gene annotation GTF (Ensembl or GENCODE)

Build the Bowtie2 index:

bowtie2-build data/reference/genome.fa data/02_alignment/bowtie2/index/genome

2. Define your samples

Create data/fastp/samples.tsv (tab-separated):

sample	condition	replicate	fastq_r1	fastq_r2	control
Sample1	Control	1	/path/to/Sample1_R1.fastq.gz	/path/to/Sample1_R2.fastq.gz	NONE
Sample2	Control	2	/path/to/Sample2_R1.fastq.gz	/path/to/Sample2_R2.fastq.gz	NONE
Sample3	Treatment	1	/path/to/Sample3_R1.fastq.gz	/path/to/Sample3_R2.fastq.gz	Sample1
Sample4	Treatment	2	/path/to/Sample4_R1.fastq.gz	/path/to/Sample4_R2.fastq.gz	Sample2

Note: Use absolute paths for FASTQ files.

Use NONE in the control column for control-only rows.

3. Configure parameters

Edit config.yaml — at minimum, verify:

• global.bowtie_index — path to your Bowtie2 index prefix
• global.genome_fa — path to reference FASTA
• global.genome_chrom_sizes — chrom sizes file
• mito_ChIP_calculate.params.mito_chr — "MT" (Ensembl) or "chrM" (UCSC)
• macs_peakcall.params.genome_size — "hs" (human), "mm" (mouse), etc.
• qc_gate.params.* — FRiP, NSC, RSC, mapping-rate, and duplicate-rate thresholds

4. Run the pipeline

# Dry run (check DAG without executing)
snakemake --dry-run --cores 8

# Run locally with Singularity
snakemake --use-singularity --cores 8

# Run locally with Conda
snakemake --use-conda --cores 8

# Run on SLURM cluster
snakemake --use-singularity --profile slurm --jobs 50

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Output Structure

results/
├── fastp/                      # Trimmed FASTQs + fastp reports
├── fastqc/                     # FastQC HTML + ZIP reports
├── bowtie2/                    # Raw aligned BAMs
├── samtools_sort/              # Coordinate-sorted BAMs
├── mito_ChIP/                  # Mitochondrial read statistics
├── remove_mito_reads/          # MT-filtered BAMs
├── samtools_fixmate/           # Fixmate BAMs
├── samtools_markdup/           # Deduplicated BAMs
├── samtools_index/             # BAM indices
├── samtools_view/              # MAPQ/flag filtered BAMs
├── samtools_stats/             # Alignment statistics
├── fragment_size_analysis/     # Fragment size distributions & plots
├── picard/
│   ├── CollectAlignmentSummaryMetrics/
│   └── CollectInsertSizeMetrics/
├── bedtools_genomecov/         # Raw BedGraphs
├── sorted_bedgraph_file/       # Sorted BedGraphs
├── bigwig/                     # Raw BigWig tracks
├── normalized_coverage/        # CPM-normalized BigWigs
├── bamCompare/                 # ChIP vs control log2 signal tracks
├── correlation_analysis/       # Sample correlation matrix & heatmap
├── macs2_peakcall/             # narrowPeak files
├── filtered_peaks/             # Blacklist-filtered peaks
├── heatmap/                    # TSS heatmaps (matrix + PDF)
├── frip_calculation/           # FRiP scores per sample
├── qc_gate/                    # QC pass/fail markers
├── peak_annotation/            # ChIPseeker annotation tables
├── motif_analysis/             # HOMER motif discovery output
├── phantompeakqualtools/       # NSC/RSC metrics
├── preseq/                     # Library complexity curves
├── qualimap/                   # BAM QC reports
└── multiqc/                    # Aggregated MultiQC report ← start here

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Key Parameters

Parameter	Default	Description
fastp.params.trim_front1/2	5	Bases trimmed from 5' end (R1/R2)
fastp.params.length_required	30	Minimum read length after trimming
bowtie2.sensitive	--very-sensitive	Alignment sensitivity preset
mito_ChIP_calculate.params.mito_chr	MT	Mitochondrial chromosome name
samtools_view.params.MAPQ	30	Minimum mapping quality
samtools_view.params.flags	3844	SAM flags to exclude
macs_peakcall.params.genome_size	hs	Effective genome size
macs_peakcall.params.qvalue	0.01	MACS2 peak calling q-value threshold
macs_peakcall.params.format	BAMPE	Paired-end BAM input format
qc_gate.params.min_frip	0.05	Minimum FRiP threshold
qc_gate.params.min_nsc	1.05	Minimum NSC threshold
qc_gate.params.min_rsc	0.8	Minimum RSC threshold
qc_gate.params.min_mapping_rate	90.0	Minimum mapping-rate threshold
qc_gate.params.max_duplicate_rate	20.0	Maximum duplicate-rate threshold

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Software Versions (Singularity Containers)

Tool	Version	Container
fastp	1.1.0	fastp:1.1.0--heae3180_0
FastQC	—	fastqc
Bowtie2	2.5.4	bowtie2:2.5.4--he96a11b_5
samtools	—	samtools
Picard	—	picard
bedtools	2.31.1	bedtools:2.31.1--h13024bc_3
MACS2	2.2.9.1	macs2:2.2.9.1--py39hbcbf7aa_2
deepTools	—	deeptools
HOMER	4.11	homer:4.11--pl5262h4ac6f70_9
ChIPseeker	1.46.1	bioconductor-chipseeker:1.46.1--r45hdfd78af_0
PhantomPeakQualTools	1.2.2	phantompeakqualtools:1.2.2--0
MultiQC	—	multiqc

All containers are pulled from the Galaxy Project Singularity depot.

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Authors

Himanshu Bhandary Email: 2032ushimanshu@gmail.com

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License

MIT License. See LICENSE for full terms.

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Citation

Bhandary H. et al. (2026). Modular ChIP-seq Pipeline [Software]. GitHub. https://github.com//chipseq_pipeline

(Update with journal citation once published.)