Tools for ChIP-seq differential binding analysis

Detection of differential binding events in ChIP-seq data is still a tricky business. For a new collaboration, the whole project is going to depend on it, so I went out there and tried to collect existing tools, work with them and see their pros and cons.

I was looking specifically for tools that work well without replicates or input controls since we already have some data lying around from a pilot in the begging of the project, but they might be useful as well as the data comes along.

In no particular order:

diffBind

• Robust tutorial.
• Requires a strict table with sample annotation (not necessarily bad though).
• Requires peak files.
• Uses multiple replicates in analysis.
• Always requires input files to perform analysis.
• Close R integration provides many useful methods to explore output by plotting.

MANorm

• Requires peak files.
• Does not require input files.
• Terrible code packaging and usage practices.

Commands to install dependencies are outdated. If anyone is also strugling with it, here’s what worked for me:

source("https://bioconductor.org/biocLite.R")
biocLite("aroma.light")
install.packages(c("R.oo","R.utils","MASS"))

Diffreps

• Installation is not straightforward (dependency hell).
• Requires peak files.
• Does not require input files (but can be used for fold enrichment filtering).
• Some nice tools downstream of differential calling (mostly region annotation).

Odin

• Can use inputs for analysis.
• Limited description of output.
• Output in “a proprietary BED format” (do I need to say anything?)

MACS2

• Does not require peak files.
• Poor documentation on the diff bind functions
• Very immature code (“prepare a pen to write down the number of non-redundant reads” - seriously?)

MultiGPS

PePr

• Requires more than one replicate per condition for analysis.
• Does not require peak files.
• Does not require input files.
• Supports several input file formats.

ChIPDiff

• No documentation besides a readme in a zip file.
• Not really packaged.

DIME

• Poor documentation (only function description in the R package).

DBChIP

• Useful tutorial.
• Only recommended for point-source factors.
• Requires peak files.