Positional statistical significance in sequence alignment.
Beginning with the concept of near-optimal sequence alignments, we can assign a probability that each element in one sequence is paired in an alignment with each element in another sequence. This involves a sum over the set of all possible pairwise alignments. The method employs a designed hidden Markov model (HMM) and the rigorous forward and forward-backward algorithms of Rabiner. The approach can use any standard sequence-element-to-element probabilistic similarity measures and affine gap penalty functions. This allows the positional alignment statistical significance to be obtained as a function of such variables. A measure of the probabilistic relationship between any single sequence and a set of sequences can be directly obtained. In addition, the employed HMM with the Viterbi algorithm provides a simple link to the standard dynamic programming optimal alignment algorithms.
BioMolecular Engineering Research Center, College of Engineering, Boston University, Massachusetts 02215, USA.
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D000263:Adenylate Kinase / Q000235:genetics; D000465:Algorithms; D000818:Animals; D002417:Cattle; D051528:Guanylate Kinases; D006454:Hemoglobins / Q000235:genetics; D006801:Humans; D007874:Leghemoglobin / Q000235:genetics; D008390:Markov Chains; D009703:Nucleoside-Phosphate Kinase / Q000235:genetics; D010446:Peptide Fragments / Q000235:genetics; D010944:Plants / Q000235:genetics; D016415:Sequence Alignment / Q000379:methods*; D012984:Software; D015003:Yeasts / Q000235:genetics
D006454:Hemoglobins; D007874:Leghemoglobin; D010446:Peptide Fragments; C070077:alpha(A) globin; D000263:Adenylate Kinase; D009703:Nucleoside-Phosphate Kinase; D051528:Guanylate Kinases