Lecture_4_motifs

2 9413 proles revisited scoring strings with a prole

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Unformatted text preview: each posi0on in the mo0f. G A T C Randomized Algorithms A T T A T T A !0 ! !0.4 ! 0.6 !0 !1.0 !0 !0 !0 !0 !1.0 !0 ! !0 !0 !0 !1.0 !0 !0.7 !0 ! !0.3 !0 !1.0 !0 !0 !0 !0 ! !1.0 !0 !0 !0 !0 !1.0 !0 !0.4 !0 ! !0.4 !0.2 !0.8! !0! !0.2! !0! Gibbs Sampling •  Popular algorithm for mo0f discovery •  Mo0f model: Posi0on Weight Matrix. •  Randomly select possible loca0ons and find a way to greedily change those loca0ons un0l we have converged to the hidden mo0f. 2 9/4/13 Profiles Revisited Scoring Strings with a Profile •  Let s = ( s 1 , . . . , s n ) be the set of star0ng posi0ons for L- mers in our n sequences •  The substrings corresponding to these star0ng posi0ons will form: - n x L alignment matrix and; - 4 x L profile matrix P - We denote pij as the profile matrix entry contained at posi0on i, j, where i = {A, C, G, T} and j = 1,…,L •  Prob(a | P) is defined...
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This note was uploaded on 02/10/2014 for the course CS 425 taught by Professor Asaben-hur during the Fall '13 term at Colorado State.

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