What Bioinformatics Methods Have Been Performed To Study Dna Methylation Data?

Question

[Egon Willighagen]

Instead of wondering what we think we know about the role of chemical modifications of DNA (like methylation), I am (at this moment) more interested in learning what bioinformatics methods have been used to study this phenomena. I can imagine having to link DNA sequence databases to other resources, perhaps using protein-DNA interaction software to model the effect of methylation on the complexation, etc.

Therefore: what bioinformatics methods have been performed to study DNA methylation data (or of other chemical modifications)?

Answer 1

[Alastair Kerr]

What is done after, depends on the question posed: A recent review paper highlights many of these.

See also the papers I cited in this thread for some computational techniques performed on MAP-seq (somewhat akin to chip-seq) data.

If you are working on bisulphite sequencing data then there are new sets of problems. A good start is the Bismark program

Answer 2

[Casey Bergman]

The standard genomics technique for studying DNA methlyation is Methylated DNA immunoprecipitation (MeDIP) coupled with arrays or high throughput sequencing. Of course this is not a 100% bioinformatics methodology if that is what you are looking for.

[Casey Bergman]

Try Down et al's Bayesian deconvolution strategy for immunoprecipitation-based DNA methylome analysis, called Batman

http://www.ncbi.nlm.nih.gov/pubmed/18612301

http://td-blade.gurdon.cam.ac.uk/software/batman/

[Egon Willighagen]

Well, indeed, I am more interested in what is done with the data after such experiments...

Answer 3

[Istvan Albert]

Based on my own early exposure to these methods medDIP feels a bit like coming back a full circle to microarrays; instead of gene expression variation we deal with methylation level changes. I would imagine that a lot of the microarray approaches would work after adapting them.