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Model-based Motif-based (MEME/MAST) Hidden Markov model-based (HMMER) Non-model-based Family Pairwise Search (FPS)
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Robert F. Murphy
Find previously unrecognized members of a family
Develop a model of a family
Motifs can be summarized and searched for using Position-Specific Scoring Matrices
Calculated from a multiple alignment of a conserved region for members of a family
Unsupervised learning methods can be used to find motifs in unaligned sequences
Best characterized algorithm is MEME T.L. Bailey & C. Elkan (1995) Unsupervised Learning of Multiple Motifs in Biopolymers Using Expectation Maximization. Machine Learning J. 21 :51-
Pick “most representative” protein sequence from a family
Convert it to a profile by replacing each amino acid by the corresponding column from a similarity matrix
For each recognized “motif” in the family, replace the corresponding section of the profile with the profile of the motif
scores from profiles of conserved motifs
similarity scores for sequence from “most representative” family member
sequence of “most representative” family member
For all known members of family, calculate (pairwise) homology to each sequence in database (using BLAST) and sum those scores
Compare BLAST using a randomly chosen family member, BLAST FPS, MEME, HMMER
W.N. Gundy (1998) Homology Detection via Family Pairwise Search. J. Comput. Biol. 5 :479-
For each method For each known protein family Train with family members Search database for matches Rank by score from search Determine how many known family members are ranked highly