TAMALg and TAMALPAIS: NimbleGen data analysis

Ok, I wanted to write about the relationship between TAMALPAIS and TAMALg.

keywords: Mark Bieda, TAMALPAIS, TAMALg, NimbleGen, ChIP-chip

Background
A major part of my research is developing algorithms and statistical models for analysis of ChIP-chip experiments – specifically those done with NimbleGen arrays.
TAMALPAIS (available here) predicts binding sites from NimbleGen array data and also does some basic secondary analyses like localization of binding sites in reference to transcription start sites and which genes have a binding site in the proximal promoter. The website version gives a lot of output.
TAMALg (TAMALpais generalized) recently was ranked #1 in an unbiased competition between algorithms. It uses the same exact prediction approach as TAMALPAIS (technically, it uses the L2L3combo set of predictions – to get these predictions, go to the TAMALPAIS website here). Then, in a second step, it uses the maxfour approach that I developed for promoter arrays (Krig et al., 2007 in JBC) to predict the actual amount of enrichment per binding site.

So the relationship between the TAMALPAIS and TAMALg is this:
TAMALPAIS produces the same high-quality peak predictions as TAMALg (and I say high quality because the competition showed this; see this paper abstract). But TAMALPAIS does not do the enrichment prediction. Remember to look at the L2L3combo set from TAMALPAIS to get the same predictions as TAMALg.

Future Stuff
I am planning on producing a downloadable version of TAMALg (probably Jython-based so that it will easily run on many platforms).

Remember! TAMALPAIS and TAMALg are not good for most promoter arrays!

If you have questions, you should contact me (see About tab on this site for contact info),

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