nf-core/atacseq

What nf-core/atacseq is

nf-core/atacseq is a bioinformatics workflow designed to process assay for transposase-accessible chromatin using sequencing (ATAC-seq) data. It takes raw sequencing reads and performs end-to-end processing, including quality assessments, alignment, post-alignment filtering, peak calling, and statistical analysis of open chromatin regions.

Originally authored by Harshil Patel and ported to Nextflow DSL2 by Björn Langer and Jose Espinosa-Carrasco under the BovReg project, the pipeline standardises chromatin accessibility analyses. It addresses the complexity of ATAC-seq data processing by handling technical artefacts such as mitochondrial contamination, PCR duplicates, and low-quality paired alignments.

The workflow is useful for researchers who need reproducible chromatin accessibility profiles across experimental conditions. It outputs signal coverage tracks, peak annotations relative to genomic features, consensus peak sets, and differential accessibility analyses with detailed quality control reports.

What the pipeline does

Raw sequencing data in FASTQ format first undergo quality inspection via FastQC and adapter removal using Trim Galore!. Read alignment can be performed using BWA, Bowtie2, STAR, or Chromap. Resulting alignments are marked for duplicate fragments using Picard, and multiple libraries for the same sample are merged.

Alignments undergo extensive filtering using SAMtools, BEDTools, BAMTools, and Pysam. This step eliminates reads mapped to mitochondrial DNA, blacklisted genomic regions, duplicates, non-primary alignments, unmapped reads, multi-mapping reads, soft-clipped reads, and reads with more than four mismatches. For paired-end data, it additionally filters pairs with insert sizes exceeding 2 kb, pairs mapping across different chromosomes, reads missing correct forward-reverse orientation, or mate-pair failures. Filtered alignments across replicates are subsequently merged.

Processed alignments are transformed into normalised bigWig tracks scaled to one million mapped reads via BEDTools and bedGraphToBigWig. deepTools computes gene-body meta-profiles and genome-wide enrichment fingerprints, while Preseq evaluates library complexity. Broad or narrow open chromatin regions are identified using MACS2 and annotated with HOMER. BEDTools generates a consensus peak set across samples, featureCounts computes read counts across these regions, and DESeq2 conducts differential accessibility, principal component analysis, and sample clustering. Final reporting and visualisation are handled by ataqv, MultiQC, and IGV session generation.

Key facts

What you can use it for

Genome-wide chromatin accessibility profiling

Map open chromatin regions across biological samples by generating normalised bigWig signal coverage tracks and calling broad or narrow peaks with MACS2.

Differential accessibility testing

Compare chromatin accessibility between conditions or treatments using DESeq2 to identify significantly altered regulatory elements from a consensus peak set.

Genomic feature annotation of open chromatin

Annotate identified peak sets relative to nearby gene structures and transcript features using HOMER.

ATAC-seq library quality and complexity auditing

Evaluate sequencing libraries, read enrichment, and technical metrics using ataqv, deepTools fingerprints, Preseq, and MultiQC.

Run it here

The research agent runs nf-core/atacseq 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

Patel H, Langer B, Espinosa-Carrasco J, et al. nf-core/atacseq. Zenodo (2023). doi: 10.5281/zenodo.2634132; Ewels P, Peltzer A, Fillinger S, Patel H, Alneberg J, Wilm A, Garcia MU, Di Tommaso P, Nahnsen S. The nf-core framework for community-curated bioinformatics pipelines. Nat Biotechnol. 2020 Feb 13. doi: 10.1038/s41587-020-0439-x.