BioStar is a site for asking, answering and discussing bioinformatics questions. We are members of thecommunity and find it very useful. Often questions and answers arise at BioStar that are germane to our readers (end users of genomics resources). Every Thursday we will be highlighting one of those questions and answers here in this thread. You can ask questions in this thread, or you can always join in at BioStar.
If you’ve been noticing the ENCODE news this week and been thinking about diving into the data, one of the types of data you’ll see is ChIP-seq. A question at BioStar from a user of this technology asked others for ideas about how to work with this data. The discussion about how to proceed might offer you some insights on ways to think about the types of things you can do with similar output.
This week’s question:
Okay this is an open question for getting some ideas.
Imagine if you have a ChIP sequencing data on some disease say Type 2 diabetes (strictly for the sake of example) and you have this data from 3 different labs, which means three different sets of data on 3 different cell types.
(This ChIP sequencing data is on binding of a transcription factor X in a diseased state).
As an bioinformatician what you will first aim to get out of this data, I mean in terms of your goal(s).
Offcourse you will look for the regions which are conserved across the cell lines and the regions which are not unique.
But what will be your generic plan to get the most out from a dataset like this.
All suggestions are welcome.
The responders offer some refinement of the things to consider and to do to move forward. Have a look, and if you have other ideas let us know.