MACS3: A Peak-calling Platform for Bulk and Single-cell Regulatory Genomics
Abstract Since the original publication of Model-based Analysis for ChIP-Seq (MACS), the software has been widely used to identify enriched genomic regions in ChIP-seq, ATAC-seq, CUT&RUN, DNase-seq, and related regulatory genomics assays. Over the years, MACS has evolved substantially, with MACS version 3 (MACS3) now serving as the actively maintained implementation. MACS3 preserves the core MACS framework for fragment pileup, dynamic local background noise, statistical enrichment testing, and peak refinement, while adding functionality needed for contemporary bulk and single-cell workflows. It supports conventional bulk peak calling, paired-end and fragment-based file formats, modular signal processing, direct analysis of single-cell ATAC-seq fragment files, barcode-restricted pseudobulk and cluster-level peak calling, specialized ATAC-seq and variant-calling modules, as well as command-line and programmatic interfaces. MACS3 is distributed through standard software channels and supported by continuous testing across operating systems, Python versions, and CPU architectures. Here we describe the architecture, current capabilities, and recommended use of MACS3, providing an updated reference for applying the MACS framework in contemporary bulk and single-cell regulatory genomics workflows. MACS3 is open-source software available at https://github.com/macs3-project/MACS.
Authors
- Qiang Hu (ORCID: https://orcid.org/0000-0002-4090-5539)
- Philippa Doherty
- Sai C. Penikalapati (ORCID: https://orcid.org/0000-0002-3389-9302)
- Song Liu (ORCID: https://orcid.org/0000-0001-6351-2941)
- Hong Zhang (ORCID: https://orcid.org/0000-0002-0430-2319)
- Tao Liu (ORCID: https://orcid.org/0000-0002-8818-8313)
- Zihan Zhuang (ORCID: https://orcid.org/0009-0002-5846-8055)
Institutions
- Roswell Park Comprehensive Cancer Center (US)
- Cornell University (US)
- New York State College of Agriculture & Life Sciences (US)
Publication Details
- Journal
- Genomics Proteomics & Bioinformatics
- Published
- 2026-09-16
- DOI
- https://doi.org/10.1093/gpbjnl/qzag097
- Primary Topic
- Single-cell and spatial transcriptomics
- Type
- article
- Field-Weighted Citation Impact
- 0.00