Question: What is the best method ChIPseq differential binding analysis?
gravatar for pat.longjump
14 months ago by
pat.longjump0 wrote:

Hi all,

I want to quantitatively compare ChIP-seq data!

Experimental condition: - same background cell line but samples differ (WT, KO1) - same antibody was used to pull down transcription factor (not histone) - identical library preparation and sequencing parameters

My goal is to perform a differential binding analysis between the two samples (I have multiple biological replicates per condition). Considering I am looking at a transcription factor and have multiple biological replicates, which differential binding analysis is the most reliable for whole genome differential binding analysis?

I have checked out two review papers: 1. Shiqi Tu et al. An introduction to computational tools for differential binding analysis with ChIP-seq data 2. Sebastian Steinhauser et al. A comprehensive comparison of tools for differential ChIP-seq analysis

Based on their flow charts I have following options: -diffReps-nb -MultiGPS -PePr -ChIPDiff -ODIN -THOR

I am not sure if any of these are what I am looking for, so any help would be appreciated!

Thank you!

chip-seq • 1.6k views
ADD COMMENTlink modified 14 months ago by Rory Stark500 • written 14 months ago by pat.longjump0

I can't tell you about the other tools, but ODIN doesn't take replicates into account, so you can cross this one from your list. THOR (from the same group) uses replicates and is a better option, especially if you have input controls.

ADD REPLYlink written 14 months ago by Carlo Yague4.4k

Beside @Rory's suggestion, I would suggest you to pay attention to this paper (The Overlooked Fact: Fundamental Need for Spike-In Control for Virtually All Genome-Wide Analyses) about the wet procedure. Sebastian's work argued that there were few overlaps between different pipelines.

ADD REPLYlink written 14 months ago by zhenyisong130
gravatar for Rory Stark
14 months ago by
Rory Stark500
University of Cambridge, Cancer Research UK - Cambridge Institute
Rory Stark500 wrote:

Disclosure: I am the author of DiffBind, a differential binding analysis tool.

Interestingly, the tools you are considering are not the ones most often used to derive results in the published literature. MACS + DiffBind is by far the most widely used combo for experiments with multiple biological replicates, being the tool of choice in more than 200 peer-reviewed publications reporting biological results. This tool is recommended explicitly by the Steinhauser paper. The Shiqi Tu paper does not evaluate DiffBind directly, but recommends the methods it encapsulates (called the edgeR and DESeq methods in the article). DiffBind is well-supported, feature-rich, and includes a clear, helpful tutorial (vignette).

Another tool that is worth a look is csaw, which lets you skip the peak-calling step. Both DiffBind and csaw are part of R/Bioconductor and integrate with other Bioconductor tools.



ADD COMMENTlink modified 14 months ago • written 14 months ago by Rory Stark500

Thank you very much! I appreciate your input!

ADD REPLYlink written 14 months ago by pat.longjump0
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