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)?
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Egon Willighagen
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
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Alastair Kerr
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.
Well, indeed, I am more interested in what is done with the data after such experiments...
Try Down et al's Bayesian deconvolution strategy for immunoprecipitation-based DNA methylome analysis, called Batman
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Casey Bergman
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.
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