LecturesPart09

# LecturesPart09 - Computational Biology, Part 9 Baum-Welch...

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Unformatted text preview: Computational Biology, Part 9 Baum-Welch Algorithm and HMMER Robert F. Murphy Copyright © 2005-2006. Copyright All rights reserved. Parameter estimation for HMMs s Simple when state sequence is known for Simple training examples training s Can be very complex for unknown paths Can Estimation when state sequence known s Count number of times each transition Count occurs, Akl s Count number of times each emission Count occurs from each state, Ek(b) s Convert to probabilities E k (b) Akl ek (b) = akl = å E k (b' ) å Akl ' l' b' Baum-Welch s Make initial parameter estimates s Use forward algorithm and backward Use algorithm to calculate probability of each sequence according to the model sequence s Calculate new model parameters s Repeat until termination criteria met Repeat (change in log likelihood < threshold) (change Estimating transition frequencies Probability that akl is used as position i in Probability sequence x f k (i) akl el ( x i +1)bl (i + 1) P (p i = k, p i +1 = l | x,q ) = P( x) s s Sum over all positions (i) and all sequences Sum (j) to get expected number of times akl is used 1 j j j Akl = å f k (i) akl el ( x i +1 )bl (i + 1) jå j P( x ) i Estimating emission frequencies s Sum over all positions for which the emitted Sum character is b and all sequences E k (b) = å j 1 j j åj f k (i)bk (i) j P ( x ) i| x = b i Updating model parameters s Convert expected numbers to probabilities Convert as if expected numbers were actual counts as Akl E k (b) akl = ek (b) = å Akl ' å E k (b' ) l' b' Test for termination s Calculate the log likelihood of the model for all of Calculate the sequences using the new parameters the n å j log P ( x | q ) j =1 s If the change in log likelihood exceeds some If threshold, go back and make new estimates of a and e Putting multiple sequence alignment and HMMs together s Given set of (unaligned) sequences s Can use CLUSTALW to create multiple Can sequence alignment sequence x http://www.ebi.ac.uk/clustalw/ s Then use HMMER to create HMM to Then represent conserved features represent Building ClustalW input file s Get collection of sequence files in FASTA Get format format s Convert from Mac to Unix format if Convert necessary necessary s Ensure each has a unique name (may Ensure already be true, depends on source of file) already s Concatenate to create input file Convert.pl #!/usr/bin/perl # for all .fa files in the current directory do two things: # convert from Mac to Unix style using "tr" # convert " NAME = " to " " using "sed" # then concate them into a single file called "input.ux" opendir(T, "."); while(\$name = readdir(T)) { if (-d \$name) { next; } if \$newname = \$name; \$newname if (\$newname =~ s/\.fa/\.1fa/) { if print "\$name\n"; print system "/usr/bin/tr '\015' '\n' < \$name > \$newname"; system \$newname2 = \$name; \$newname2 (\$newname2 =~ s/\.fa/\.2fa/); (\$newname2 system "/usr/bin/sed 's/ NAME = / /' \$newname > \$newname2"; system } } system "cat *.2fa > input.ux"; system "rm *.1fa"; system "rm *.2fa"; ClustalW s Use either ClustalW from command line or Use ClustalX via graphical interface to align set of sequences and output as .aln file of s The file can be used as input to build an The HMM HMM HMMER s Free HMM builder and searcher from Sean Free Eddy at Washington University Eddy s http://hmmer.wustl.edu ...
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## This note was uploaded on 01/13/2012 for the course BIO 101 taught by Professor Staff during the Fall '10 term at DePaul.

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