I’ve received a lot of questions recently about TAMALg availability. Unfortunately, there is only a difficult-to-install package available right now; I sent it to someone recently and they had a terrible time getting it going.
I do describe the algorithm in the supplementary materials to the ENCODE spike-in competition paper (Johnson et al, Genome Research 2008).
I would love to have a simple package to distribute, but this is little supported in today’s granting environment; in fact, I don’t think that making algorithms widely available has ever been well-supported by any US funding agency. And I doubt the situation is different here in Canada.
I may be getting another undergrad soon and would task that person with working on the package. As a new faculty member, I am simply overwhelmed with basics like getting my lab going right now.
I do hope that this situation changes and thanks to all for patience.
As I have noted previously, the L2L3combo predictions produced by the TAMALPAIS server (see previous posts on this or just search for “TAMALPAIS Bieda” – no quotes, though) are the same predictions as made by TAMALg. TAMALg also adds the step of estimating enrichment via using maxfour type methodology.
So you can get good TAMALg predictions of sites just by using the webserver. I suggest going this route.
And to repeat – TAMALg is almost certainly NOT what you want for promoter arrays. Except if you have a factor in only a tiny fraction of promoters or one of the newer designs with very long promoter regions (e.g. for 10 kb promoters, might be ok).
Ok, I wanted to write about the relationship between TAMALPAIS and TAMALg.
keywords: Mark Bieda, TAMALPAIS, TAMALg, NimbleGen, ChIP-chip
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.
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),