MIT6_047f08_rec05 - MIT OpenCourseWare http:/

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MIT OpenCourseWare 6.047 / 6.878 Computational Biology: Genomes, Networks, Evolution Fall 2008 For information about citing these materials or our Terms of Use, visit: .
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6.04716.878 Recitation 5 - Notes October 2, 2008 Pouya Kheradpour 1 Probabilistic Pattern Finding Many interesting motifs in biology are degenerate. We want to be able to identify and appropriately represent these motifs. 1.1 Representing Probabilistic Patterns One of the most common and simplest ways to represent these motifs is using a position frequency matrix (PFM). In a PFM each position is modeled as a distribution over the possible characters. To visualize and compare it more easily, we can also display a PFM as a logo: 2 Expectation Maximization for Motif Finding For EM motif finding we assume we are only given the size of the motif and we need to find it in a set of sequences. We also need a background probability so that we can evaluate when a motif matches a sequence. First, we will begin with some definitions: Zij: the probability that the motif in sequence istarts at position j. Sii: sequence itsjth character. Mij:
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This note was uploaded on 09/24/2010 for the course EECS 6.047 / 6. taught by Professor Manoliskellis during the Fall '08 term at MIT.

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MIT6_047f08_rec05 - MIT OpenCourseWare http:/

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