Lecture_3_motifs

# Think of consensus as an ancestor mof from which

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Unformatted text preview: ccaGgtacTtaggtcctctgtgcgaatctatgcgtttccaaccat cctgatagacgctatctggctatccaGgtacTtaggtcctctgtgcgaatctatgcgtttccaaccat agtactggtgtacatttgatCcAtacgtacaccggcaacctgaaacaaacgctcagaaccagaagtgc agtactggtgtacatttgatCcAtacgtacaccggcaacctgaaacaaacgctcagaaccagaagtgc aaacgtTAgtgcaccctctttcttcgtggctctggccaacgagggctgatgtataagacgaaaatttt aaacgtTAgtgcaccctctttcttcgtggctctggccaacgagggctgatgtataagacgaaaatttt agcctccgatgtaagtcatagctgtaactattacctgccacccctattacatcttacgtCcAtataca agcctccgatgtaagtcatagctgtaactattacctgccacccctattacatcttacgtCcAtataca ctgttatacaacgcgtcatggcggggtatgcgttttggtcgtcgtacgctcgatcgttaCcgtacgGc ctgttatacaacgcgtcatggcggggtatgcgttttggtcgtcgtacgctcgatcgttaCcgtacgGc •  Can we s,ll ﬁnd the mo,f, now that we have 2 muta,ons? Deﬁning Mo,fs •  To deﬁne a mo,f, lets say we know where the mo,f starts in the sequence •  The mo,f start posi,ons in their sequences can be represented as s = (s1,s2,s3,…,st) Mo,f Recogni,on: Problem Formula,ons •  Maximizing the Posi,on Weight Matrix (PWM) –  Gibbs sampling –  Expecta,on Maximiza,on (MEME) –  YMF •  Combinatorial Deﬁni,on –  MCL- WMR –  Vo,ng –  WINNOWER Mo,f Recogni,on: Problem Formula,ons •  Maximizing the Posi,on Weight Matrix (PWM) –  Gibbs sampling –  Expecta,on Maximiza,on (MEME) –  YMF •  Combinatorial Deﬁni,on –  MCL- WMR –  Vo,ng –  WINNOWER Mo,f Recogni,on A mo,f instance is a nucleo,de or amino- acid pa]ern that is over- represented and has biological signiﬁcance. The Mo,f Recogni,on Problem: Input: n strings, and parameters L and d. Output: a L- length string s, where each input string contains s at some posi,on with at most d subs,tu,ons. •  NP-complete,...
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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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