Chip Seq Data Analysis

Question

[Dataminer]

Any good web resource for ChIP-Seq Data Analysis (starting from basics to intermediate level or higher)?

[Chris Evelo]

Since you know Michiel in my lab personally I would ask him directly he has done a lot of work on ChIP.

[Dataminer]

Thank you very much.

Answer 1

[Brad Chapman]

The Cistrome project:

http://cistrome.org

has a Galaxy server pre-configured with a number of ChIP-seq analysis tools for peak calling, experiment comparisons, expression/motif analysis, and visualization. They also have a data collection with experimental data for download.

Answer 2

[Ian]

The GALAXY 'NGS Toolbox Beta' tools are a simple starting point. There is mapping and peak calling for the major sequencing platforms. Plus you can visualise your results via GALAXY in UCSC, Ensembl or GeneTrack.

EDIT: I think this is more or less what Brad suggested, but directly from GALAXY.

Answer 3

[Casey Bergman]

Another option to look into the suite of BioConductor packages in R that are relevant to chip-seq experiments including:

• ChIPseqR
• BayesPeak
• CSAR
• ChIPsim
• ChIPpeakAnno

These packages and their corresponding papers should give you a good overview of relevant methods that you can piece together into your own analysis pipeline.

Answer 4

[Ashwin Kumar]

There is an online seminar on ChIP-Seq data analysis using Avadis NGS on June 9th. Register here if you are interested. The seminar will be recorded and eventually posted here alongside the tutorials for RNA-Seq and DNA-Seq data analysis.

[Dataminer]

It appears to be a commercial software. so it won''t be of great help.

[Ashwin Kumar]

Would you explain why a commercial software won't be of help? If it does the analysis required of the research, but with better (more intuitive) user experience and technical support than freely-available tools, then it should be.

[Ashwin Kumar]

Would you explain why a commercial software won't be of help? If it does the analysis required of the research, but with better user experience and technical support than freely-available tools, then it should be.

[Dataminer]

Of course I can explain Ashwin, first of all the software you are talking about (avadis), does it have any publication history in good impacvt journal? secondly how it is better than Galaxy/cistrome and the tools available in R? Moreover GREAT, also does a fairly good job. Last but not the least, personally I believe you cannot judge a software from a test run (in which a lot of or say crucial components are blocked)...which in turn means one needs to buy that particular software (if he gets convinced that a particular software can solve the biological problem) ....This indeed is a big gamble

Answer 5

[bede.portz]

I have been really impressed with HOMER. Here is he link.

HOMER

The program is powerful, you can take reads/peaks and carry out common analysis quickly within homer, or generate datasets you can operate on further for such things as clustering, and visualizing the data in the form of heatmaps using other tools.

The program is run from the command line, which means there is more of an initial learning curve than some of the GALAXY based solutions mentioned above, but I have found that a familiarity with UNIX has really helped me and was worth the initial investment of time should you not already be familiar with running tools from the command line.

The other caveat to HOMER, though not unique to HOMER, is that the various steps of ChIP-seq analysis assume things about the data, and operate on the data accordingly, by default. For example, HOMER estimates the fragment size of DNA, extends reads, and normalizes the number of reads in a dataset. All of these functions can be helpful, but they can be problematic if not understood and applied incorrectly. Fortunately, the tools can be run with many options controlling these default functions, and the documentation is very good. If you spend some time reading the documentation and working with the tools, I think you can pick up HOMER pretty quickly.

Answer 6

[dnaseiseq]

Bailey T, Krajewski P, Ladunga I, Lefebvre C, Li Q, et al. (2013) Practical Guidelines for the Comprehensive Analysis of ChIP-seq Data. PLoS Comput Biol 9(11): e1003326. doi:10.1371/journal.pcbi.1003326

http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003326