NestedMICA motif discovery

Matias Piipari

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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