- Unique identifier:
- Software type:
- Restrictions to use:
- Input format:
- TXT, ELAND, BED, ELANDMULTI, ELANDEXPORT, ELANDMULTIPET, SAM, BAM, BOWTIE
- Output format:
- XLS, BED, R, WIG.GZ
- Programming languages:
- Computer skills:
- Model-based Analysis for ChIP-Seq
- Command line interface
- Input data:
- A tag file, a treatment file
- Output data:
- Peaks, peak locations, negative peaks, model image, diagnosis report
- Operating system:
- Artistic License version 2.0
- Documentation: https://github.com/taoliu/MACS/blob/macs_v1/README.rst
- Wei Li <>
new:~/sigma $ macs14 -t Alice1_S7.sam --format=SAM
INFO @ Wed, 11 Feb 2015 14:34:25:
# ARGUMENTS LIST:
# name = NA
# format = SAM
# ChIP-seq file = Alice1_S7.sam
# control file = None
# effective genome size = 2.70e+09
# band width = 300
# model fold = 10,30
# pvalue cutoff = 1.00e-05
# Large dataset will be scaled towards smaller dataset.
# Range for calculating regional lambda is: 10000 bps
INFO @ Wed, 11 Feb 2015 14:34:25: #1 read tag files...
INFO @ Wed, 11 Feb 2015 14:34:25: #1 read treatment tags...
Traceback (most recent call last):
File "/usr/bin/macs14", line 366, in <module>
File "/usr/bin/macs14", line 60, in main
(treat, control) = load_tag_files_options (options)
File "/usr/bin/macs14", line 335, in load_tag_files_options
ttsize = tp.tsize()
File "/usr/lib/python2.6/site-packages/MACS14/IO/Parser.py", line 655, in tsize
ZeroDivisionError: integer division or modulo by zero
(Zhang et al., 2008)
Model-based analysis of ChIP-Seq (MACS).
PMID: 18798982 DOI: 10.1186/gb-2008-9-9-r137
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
NIH grants HG004069, HG004270 and DK074967
2 user reviews
2 user reviews
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).