Chip-Seq - Peak caller for broad enrichment of histone modifications?

Question

[ej]

I'm analyzing Chip-Seq data and am interested in histone modifications that have very broad peaks such as H3K27me3 and H3K36me3. I have tried peak calling using Homer but it does not seem to work well. Is there a better peak caller for these modifications that have broad enrichment across the genome? Thanks

Answer 1

[Manvendra Singh]

Why aren't you using macs for calling peaks. Its very good for Histone peaks , including broad ones

Answer 2

[Ming Tommy Tang]

I have some collections here https://github.com/crazyhottommy/ChIP-seq-analysis#peak-calling

Answer 3

[Gary]

SICER is a specialized peak caller for broad enriched histone modification (Zang, et al 2009). A comment in OMICtools also said that SICER is better than MACS for broad enrichment antibodies. However, I use MACS 1.4, the most stable version, to call H3K27me3 peaks with parameters suggested by Dr. Liu as below (Feng, et al 2012). For our ChIP-Seq data, MACS 1.4 is good enough. I used MACS, but not SICER, because we would like to use the same peak calling tool to call all ChIP-Seq data, including broad and sharp enriched peaks. In addition, MACS 2 has added broad enriched parameters (--broad) that could be helpful for your purpose.

Zang, et al. 2009. A clustering approach for identification of enriched domains from histone modification ChIP-Seq data. Bioinformatics

A comment for SICER in OMICtools

Ideal for broad peaks like H3K27me3 and H3K36me3. Where MACS will truncate peaks in smaller fragments, generating dozens of small peaks, the strenght of SICER is that you can take gaps into account. You just have to perform various tests to choose a good combination between gap size and window size. Typically, I used a window of 500nt and a gap of 3500nt for a good compromise between sensitivity and specificity.

Posted by Fabien Pichon on Oct 17, 2014

Feng JX, Liu T, Qin B, Zhang Y, Liu XS. 2012. Identifying ChIP-seq enrichment using MACS. Nature Protocols 7:1728-1740.

macs14 -t BROAD_GM12878_H3K4me3.bam -c BROAD_GM12878_H3K4me3_Control.bam -g hs -n BROAD_GM12878_H3K4me3 --nomodel --shiftsize 73 -B -S --call-subpeaks

[A. Domingues]

I have tested both, and even though I like MACS a lot, easy to use and had good results in the past, I found that for broad regions SICER gave me some better results after setting the Window and gap size appropriately. By good results I mean more enriched regions matching expectations and what could be observed visually from the normalized coverage.

[endrebak]

New version of SICER: https://github.com/endrebak/epic

Answer 4

[endrebak]

I am biased, but https://github.com/endrebak/epic is good.

Also, if you prefer R and know some linear modelling csaw is an option: https://bioconductor.org/packages/release/bioc/html/csaw.html

Answer 5

[Ido Tamir]

You could try ZINBA http://genomebiology.com/2011/12/7/r67 "ZINBA integrates local covariates with DNA-seq data to identify broad and narrow regions of enrichment, even within amplified genomic regions" It also uses local mappability information. I never used it because it has no CLI (only R).