Tool:Chance: Comprehensive Software For Quality Control And Validation Of Chip-Seq Data

Question

[Aaron Diaz]

Hi,

We at UCSF have recently developed a software tool for ChIP-seq quality control and protocol optimization called CHANCE, which appears in the current special issue of Genome Biology:

http://genomebiology.com/2012/13/10/R98/abstract

CHANCE has a graphical interface and doesn't require any knowledge of programming or statistics. The CHANCE program as well as the source code can be downloaded from: https://github.com/songlab/chance/downloads

Thanks,
Aaron Diaz
Inst. for Human Genetics, UCSF

Answer 1

[paul.e.gradie]

Wow - no updates to this?

Who is going to use this if it can't accommodate mm10?

I'll put an alternative out there: DeepTools.

http://deeptools.readthedocs.io/en/latest/index.html

These guys did a fantastic job of coding up a suite of tools for assessing sequencing results. Most importantly for ChIP, it provides the Plot_Fingerprint tool for using SES (signal extraction scaling) to asses enrichment strength. The original paper that both CHANCE and DeepTools based this on can be found here: http://www.ncbi.nlm.nih.gov/pubmed/22499706

Its clean, beautiful, and the source code is written in Python (so if you're like me and looking for a large Python project to digest, this is your ticket!)

Cheers,
Paul

Answer 2

[André Rendeiro]

I heard about CHANCE recently and just for curiosity would like to run it on my data which is from unsupported organisms.

I can't find any manual reference on how to add a custom build to use in CHANCE.

Adding annotation for a custom genome shouldn't be too difficult, unless the currently supported builds (hg18, hg19, mm9) are for some reason hard-coded in the CHANCE code.

Before I start looking more deeply into this, has anyone tried to do this?

[sinha]

Hi André, did you manage to run it on a custom build?