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MACS specifications


Unique identifier OMICS_00446
Alternative names Model-based Analysis for ChIP-Seq, MACS2, macs2
Software type Package/Module
Interface Command line interface
Restrictions to use None
Input data A tag file, a treatment file.
Output data A file with information about called peaks, peak locations, peak summits locations, negative peaks, a script to produce a PDF image, a file that can be viewed through the UCSC genome browser, a diagnosis report and an optional file for the subpeaks option.
Output format XLS+BED+R+bedGraph+XLS+TXT
Operating system Unix/Linux
Programming languages C, Python
License BSD 3-clause “New” or “Revised” License
Computer skills Advanced
Version 2.1.0
Stability Stable
Numpy, GCC, Cython
Maintained Yes


  • MACS2-bdgdiff




No version available



  • person_outline Wei Li
  • person_outline Tao Liu

Additional information

Publication for Model-based Analysis for ChIP-Seq

MACS citations


Distinct epigenetic landscapes underlie the pathobiology of pancreatic cancer subtypes

Nat Commun
PMCID: 5958058
PMID: 29773832
DOI: 10.1038/s41467-018-04383-6

[…] alyzed using the HiChIP pipeline. Briefly, paired-end reads were mapped by BWA and pairs with one or both ends uniquely mapped were retained. H3K4me3, H3K4me1, and H3K27ac peaks were called using the MACS2 software package at false discovery rate (FDR) ≤ 1%. SICER was used to identify enriched domains for H3K27me3 and H3K9me3. For data visualization, BEDTools in combination with in-house scripts w […]


Methionine metabolism influences genomic architecture and gene expression through H3K4me3 peak width

Nat Commun
PMCID: 5955993
PMID: 29769529
DOI: 10.1038/s41467-018-04426-y

[…] th were computed on this combinational peak set using the extended reads coverage files (that is, number of fragments extended from ChIP-seq reads mapped to each base pair on the genome) generated by MACS2. Height was computed by searching for position with highest read coverage. Area was computed by integrating the reads coverage file over the peak region. Width was quantified by first normalizin […]


Comprehensive epigenetic landscape of rheumatoid arthritis fibroblast like synoviocytes

Nat Commun
PMCID: 5953939
PMID: 29765031
DOI: 10.1038/s41467-018-04310-9

[…] OA samples, each with six histone modifications (H3K4me1, H3K4me3, H3K9me3, H3K27ac, H3K27me3, and H3K36me3) and input data, were mapped to hg19 genome using bwa (0.7.7-r441). Peaks were called using MACS2 ( using a p-value cutoff of 0.1 for broad marks (9me3, 27me3, 36me3) and 0.01 for narrow marks (4me1, 4me3, 27ac). To assess the library complexity and the enrichment of the dat […]


PCGF5 is required for neural differentiation of embryonic stem cells

Nat Commun
PMCID: 5954019
PMID: 29765032
DOI: 10.1038/s41467-018-03781-0

[…] hen, the aligned reads were converted to bam format using samtools and duplicates were removed by Picard ( Finally, the histone-enriched regions were called by MACS2 with default parameters. For PCGF5 ChIP-seq, enriched peaks were called by MACS2 with p-value <10−4 as cutoff, then peaks with q-value less than 0.01 were chosen for further analysis. Peaks were […]


TGF β induces miR 100 and miR 125b but blocks let 7a through LIN28B controlling PDAC progression

Nat Commun
PMCID: 5945639
PMID: 29748571
DOI: 10.1038/s41467-018-03962-x

[…] ocus (see Fig. ) with the aim of disrupting part or the entire miRNA locus. For deletion of MIR100HG promoter region (MIR100HG∆P), pairs of sgRNAs were chosen to remove the SMAD2/3 peaks predicted by MACS2 from our ChIP-seq experiments. Finally, to generate LIN28B KO clones a single sgRNA targeting the genomic region downstream of the AUG translation start site codon was used. Oligonucleotides con […]


NOTCH mediated non cell autonomous regulation of chromatin structure during senescence

Nat Commun
PMCID: 5943456
PMID: 29743479
DOI: 10.1038/s41467-018-04283-9

[…] d reads mapping to the ‘blacklisted’ regions identified by ENCODE were further removed. Average fragment size was determined using the ChIPQC Bioconductor package, and peak calling was performed with MACS2 (version 2.1.0), using fragment size as an extension size (--extsize) parameter. High-confidence peak sets for each condition were identified separately using only those peak regions that were p […]


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MACS institution(s)
Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, Boston, MA, USA; Division of Molecular and Cellular Oncology, Department of Medical Oncology, Dana-Farber Cancer Institute and Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Gene Security Network, Inc., Redwood City, CA, USA; Molecular Pathology Unit and Center for Cancer Research, Massachusetts General Hospital and Department of Pathology, Harvard Medical School, Charlestown, MA, USA; Broad Institute of Harvard and MIT, Cambridge, MA, USA; Department of Genetics, Stanford University Medical Center, Stanford, CA, USA; Division of Biostatistics, Dan L Duncan Cancer Center, Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA
MACS funding source(s)
Supported in part by NIH grants HG004069, HG004270 and DK074967.

MACS reviews

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Miklós Laczik

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Arguably a gold standard among peak callers, MACS is excellent in detecting significant enrichments in point-source enrichment type NGS data, like ChIP-seq data with transcription factors. It is easy to use, well documented, well maintained. It can be run both with or without a control sample, and the options give you a decent flexibility (setting significance treshold, switching the model building on or off, determining the fragment size to match the experimentally determined size, working with various file formats from BED through BAM to proprietary Illumina formats etc.) It has a decent speed too.
I think it's important to understand that it is designed for point-source data, and for that it works perfectly (that's why I gave maximum rating, despite the constraints described in the following section, which I don't see as a fault - it's really not intended for the tasks below.) Numerous tests show (even a paper published by the MACS authors) that when it comes to diffuse enrichments, most notably, ChIP-seq with histone marks, then MACS fails to identify the peaks correctly. Usually it manages to detect some parts of it, some local summits within the dispersed signal, if it detects something at all. Although in the literature it is used for various datasets, including histone marks, ATAC-seq, MeDIP-seq etc., I wouldn't recommend it for detecting other enrichments than point-source peaks. There are much superior tools out there for diffuse signals (like SICER). I have to mention though that maybe H3K4me3 is an exception, it is a very "TF-like" histone mark, with sharp, clear, high enrichments, and for that mark only MACS might be acceptable. But I wouldn't trust it for any other HMs (and I've been working with ChIP-seq data of all histone marks you can imagine for ~10 years). I would also take the results of MeDIP-seq. ATAC-seq and similar data with a grain of salt if it was processed with MACS, because while it can certainly detect some enrichment, not all ofthem can be considered point-source, many methylated regions, open chromatin etc. are way broader than that.

With that said, I think it's a great tool for what it was designed.
Be aware that the MACS website is still saying that the newest version is version 1.4.2, which is not true, it has long been superceded by the major version 2, which you can find on GitHub (at the time of writing this review the latest stable version is 2.1.0).

Fabien Pichon

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A very good software to find peaks in ChIP-seq experiments (except for broad peaks like H3K27me3 and H3K36me3 where you should prefer SICER). Also works perfectly with ATAC-seq where you can use it without input (-c option).
Easy to install and use.