Positional statistical significance in sequence alignment.

L Yu; T F Smith

doi:10.1089/cmb.1999.6.253

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PubMed · 10421526

Positional statistical significance in sequence alignment.

L Yu·T F Smith

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Abstract

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.

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Adenylate Kinase Algorithms Animals Cattle Guanylate Kinases Hemoglobins Humans Leghemoglobin Markov Chains Nucleoside-Phosphate Kinase Peptide Fragments Plants Sequence Alignment Software Yeasts Journal Article Research Support, U.S. Gov't, P.H.S.

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L Yu, T F Smith. 1999. Positional statistical significance in sequence alignment.. https://doi.org/10.1089/cmb.1999.6.253

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