|Alternative names||Model-based Analysis for ChIP-Seq, MACS2, macs2|
|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.|
|Programming languages||C, Python|
|License||BSD 3-clause “New” or “Revised” License|
Numpy, GCC, Cython
Add your version
- Issues: https://github.com/taoliu/MACS/
- person_outline Wei Li <>
- person_outline Tao Liu <>
Publication for Model-based Analysis for ChIP-Seq
MACS in pipelines(139)
[…] was used for mapping the chip-seq data to the human genome hg19. data from repeated experiments were merged for analysis. unmapped reads were filtered out. high-confidence peaks were called by macs2, with the following parameters: q-value = 0.05, bandwidth = 300, arbitrary extension = 100 bp., the activating/repressive function prediction and target gene identification […]
[…] (bwa-mem, version 0.7.0). high-quality mapped reads (mapq ≥10) reads were used for downstream analyses. reads deemed as pcr duplication were removed using samtools. binding peaks were detected using macs2 with q-value ≤ 0.05. sequencing coverage was computed using medips with a 50 bp window size and read length extension to 200 bp. dna-binding motif was detected using meme with the following […]
[…] using the single-end method (50 bp reads) the chip-seq reads were mapped onto the saccer3 reference genome using the bowtie2 align program, and data normalization analysis was performed using the macs2 peak calling program. log2 values between mutant and wild-type cells were calculated using bamcompare in the deeptools, a data analysis program for high-throughput sequencing. the read counts […]
[…] external chip-seq data for h4r3me2s (geo accession gse37604)  were aligned to the mm9 genome assembly using bowtie2 v2.1.0  and uniquely aligned reads were extracted for peak detection using macs2. to identify repeats enriched for h4r3me2s, the number of chip-seq peaks overlapping each repeat class were compared with a random control where peaks were shuffled (using bedtools) […]
[…] aligner. only uniquely aligned and properly paired read tags with mapping score >15 were retained for subsequent analysis. (supplementary tables and ). methylated regions were identified with macs2. regions obtained from different samples were merged by the bedtools suite and statistical analyses were performed on the matrix of read counts over all regions. data were normalized using […]
MACS in publications(547)
[…] reference by bowtie 1.2.1 with the following parameters: ‘-m 3 –v 2’. duplicated reads were removed using the default parameters of picard 2.17.11 (http://broadinstitute.github.io/picard/) (table ). macs2 2.1.0 was used to identify the accessible regions and peaks with the following parameters: ‘-nomodel -nolambda -keep-dup all -call-summits -q 0.01’. output bedgraph format files were used […]
[…] 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 […]
[…] l-fold global changes in the bulk levels of h3k4me3. aligning the reads to a reference genome followed by peak calling identified a set of h3k4me3 peaks, i.e., genomic regions significantly enriched (macs2 enrichment q-value <1e-5) with chip-seq reads in comparison to the control (fig. ). comparing total peak number (coefficient of variation (cv) = 0.01, fig. ), genomic location (jaccard index […]
[…] genome (mm9) using bowtie (v2.2.6), using the default options. mapped sam files were sorted and converted to bam files with samtools (v0.1.19-96b5f2294a). accessible regions were determined using macs2 (v22.214.171.12451222) with the following options: “callpeak -g mm –q 0.01”. the union of all sample peaks was determined in r (v3.2.3) using the package “genomicranges” (v1.22.4). the number […]
[…] 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 (126.96.36.19950420.1) 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 […]
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).
Easy to install and use.