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Software To Analyse Chromatin-Interaction Sequencing Datasets?

I am interested in knowing what software is out there to analyse chromatin-interaction sequencing datasets like those coming from genome-wide next-gen sequencing ChIA-PET protocols:

http://noble.gs.washington.edu/proj/yeast-architecture/

So far I've only seen generic clustering tools being re-purposed for chromatin interaction analysis but nothing more.

interaction software

What kind of analysis?

Indeed, this has been a problem for the advancement of this technique :-)

@Casey: I completely agree with you.

4 answers

Quick web search threw up ChIA-PET tool. Described as "a software package for automatic processing of ChIA-PET sequence data, including linker filtering, mapping tags to reference genomes, identifying protein binding sites and chromatin interactions, and displaying the results on a graphical genome browser."

This is developed by the group that originally developed ChIA-PET, so it's a good start.

I wonder if you tried this software package. The document is very short and lack of necessary information. I have not found a way to use it. Could you give some suggestions?

As I say, quick web search - have not tried it myself.

I was wondering if anyone has tried this tool since this was posted. We are trying to install this tool and get only problems with the pipeline. The manuals are VERY unhelpful and the authors of the papers are not so supportive. So I would like to know if anyone has any success in running the pipeline

MizBee: A Multiscale Synteny Browser alt text

Hi-C/5C interaction data can be analyzed using methods from the Dostie Lab:

@Gjain - I thought about posting my5C, but it doesn't appear to allow you to analyze your own data.

@Casey : yes,its ambiguous right now just to data analysis. We have developed a new pipeline and updated the web tool. We are going to replace the current one with refined data analysis and supporting tools soon.

You might want to look at the approach applied by Jonathan Pritchard and colleagues in associating DNA methylation patterns with variation in genetic markers and in gene expression. Their paper (Bell, et al) and data can be found here.

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