NestedMICA motif discovery

Matias Piipari

http://www.sanger.ac.uk/software/nmica

scalable. 1000s sequences. 100s motifs highly parallel. extendable

Actually, looking at metamotifs and motif classification in this talk.
Different TF families have different modes of binding.

Can build a metamotif that encodes the distribution across a number of motifs in a family. Like a PWM, but with confidence intervals. Developed a nested sampler to learn these from a set of motifs (eg for bHLH TFs) describes repeating patterns in a set of motifs. Can use these as a prior for motif finding which improves detection of real motif in a test set. (I think…)

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