nf-core/chipseq

What nf-core/chipseq is

nf-core/chipseq is a bioinformatics pipeline designed for Chromatin ImmunoPrecipitation sequencing (ChIP-seq) data. It was originally developed by Chuan Wang and Phil Ewels at the National Genomics Infrastructure at SciLifeLab in Stockholm, Sweden, re-implemented by Harshil Patel at Seqera Labs, and converted to Nextflow DSL2 by Jose Espinosa-Carrasco at the Centre for Genomic Regulation.

The workflow resolves the challenge of standardising ChIP-seq analysis from raw sequencing reads through to biological interpretation. By automating quality assessment, read mapping, stringent filtering, peak detection, and comparative exploration, it provides researchers with reproducible results across different compute environments and experimental targets.

What the pipeline does

The workflow begins with raw read quality control using FastQC and adapter trimming via Trim Galore!. Sequencing reads are mapped to a reference genome using a choice of aligners: BWA, Chromap, Bowtie2, or STAR. Alignments have duplicates marked with Picard and can be merged across multiple sequencing libraries for the same sample. Reads undergo filtering to remove duplicates, unmapped alignments, multimapping reads, non-primary alignments, reads within blacklisted regions, and alignments containing more than four mismatches. For paired-end data, filtering additionally removes pairs with insert sizes over 2 kilobases, incorrect forward-reverse orientations, or reads mapping to different chromosomes.

Downstream analysis assesses library complexity with Preseq and Picard, calculates normalised coverage bigWig files scaled to 1 million mapped reads, and generates gene-body meta-profiles and genome-wide fingerprint enrichment metrics using deepTools. Normalised strand cross-correlation measures (NSC and RSC) are computed using phantompeakqualtools. Broad or narrow peaks are called with MACS3 and annotated against gene features using HOMER. BEDTools compiles a consensus peakset across samples, featureCounts quantifies read counts within consensus intervals, and DESeq2 conducts sample clustering and principal component analysis. An IGV session is assembled for visualisation, and MultiQC aggregates all quality metrics.

Key facts

What you can use it for

Transcription factor binding detection

Identifies narrow enrichment regions for transcription factors such as FoxA1 across the genome relative to matched input control samples.

Histone modification profiling

Maps broad histone mark domains such as EZH2 and evaluates signal distribution using gene-body meta-profiles.

Differential binding and clustering

Counts read occupancies across consensus peaks and performs sample clustering and principal component analysis across conditions.

Multi-library sample consolidation

Merges multiple sequencing runs and libraries per sample with unified duplicate re-marking and quality filtering.

Run it here

The research agent runs nf-core/chipseq with Nextflow on its own machine — up to 32 cores, 256 GiB of memory and 24 hours per run, which covers the test profile and real single-sample runs. It builds the samplesheet, checks it against the pipeline's schema and reads the results back. When a run is too big for one machine, it gives you the exact command for your own cluster instead.

Strengths and limits

Questions

How to cite it

Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso & Sven Nahnsen. The nf-core framework for community-curated bioinformatics pipelines. Nat Biotechnol. 2020 Feb 13. doi: 10.1038/s41587-020-0439-x.