Identifying ChIP-seq enrichment using MACS

By Jianxing Feng, Tao Liu, Bo Qin, Yong Zhang, Xiaole Shirley Liu. Nature Protocols, 2012

1,676 citations recorded by the source corpus.

Model-based analysis of ChIP-seq (MACS) is a computational algorithm that identifies genome-wide locations of transcription/chromatin factor binding or histone modification from ChIP-seq data. MACS consists of four steps: removing redundant reads, adjusting read position, calculating peak enrichment and estimating the empirical false discovery rate (FDR). In this protocol, we provide a detailed demonstration of how to install MACS and how to use it to analyze three common types of ChIP-seq data sets with different characteristics: the sequence-specific transcription factor FoxA1, the histone modification mark H3K4me3 with sharp enrichment and the H3K36me3 mark with broad enrichment. We also explain how to interpret and visualize the results of MACS analyses. The algorithm requires ∼3 GB of RAM and 1.5 h of computing time to analyze a ChIP-seq data set containing 30 million reads, an estimate that increases with sequence coverage. MACS is open source and is available from http://liulab.dfci.harvard.edu/MACS/.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/059a317ddcd268410b724a019bd47f99ef76a628

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Citations in the corpus

High-resolution TADs reveal DNA sequences underlying genome organization in flies (2017, methodology): “MEME-chip (Ma et al. 2014) was used to identify motifs based on the MACS2 peaks.”
H3K4me3 breadth is linked to cell identity and transcriptional consistency (2014, neutral): “08 (Feng et al., 2012) with the ‘‘—broad’’ option for histone marks.”
The histone H3.3K27M mutation in pediatric glioma reprograms H3K27 methylation and gene expression. (2013, methodology): “Peak detection was done using MACS (Feng et al. 2012)”
Single-cell RNA-seq and computational analysis using temporal mixture modelling resolves Th1/Tfh fate bifurcation in malaria (2017, background): “0 with broad option (61) for the SP140 M(IFN-) and M(IFN-+LPS 4h) ChIP samples versus input control DNA.”
Target analysis by integration of transcriptome and ChIP-seq data with BETA (2013, methodology): “For ChIP-seq data, we use Bowtie 21 to map sequencing reads to the reference genome and MACS 22 for peak calling.”
Zelda is differentially required for chromatin accessibility, transcription factor binding, and gene expression in the early Drosophila embryo (2015, methodology): “FAIRE peaks were called with MACS2 (Feng et al. 2012) using a shift size of 125 bp.”
deepTools2: a next generation web server for deep-sequencing data analysis (2016, background): “The average fragment size of a sample is also an important parameter for many downstream analyses, such as peak calling (8,9).”
The PR/SET Domain Zinc Finger Protein Prdm4 Regulates Gene Expression in Embryonic Stem Cells but Plays a Nonessential Role in the Developing Mouse Embryo (2013, methodology): “Peak calling was performed using MACS2 (45, 46), using default parameters to call areas of enrichment in the anti-GFP ChIP over the nonspecific whole-mouse IgG control ChIP.”
Distinct structural bases for sequence-specific DNA binding by mammalian BEN domain proteins (2022, neutral): “BEND3 binding peaks were called using the MACS2 algorithm (version 2.1.1.20160309) with default parameters (Feng et al. 2012).”
Denoising genome-wide histone ChIP-seq with convolutional neural networks (2016, methodology): “For the classiﬁcation tasks (predicting presence or absence of a peak), we compared our model’s output to peaks called by the MACS2 peak caller (Feng et al. , 2012) on the high-quality signal for the target mark.”