An expectation maximization algorithm for training hidden substitution models.

I Holmes; G M Rubin

doi:10.1006/jmbi.2002.5405

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An expectation maximization algorithm for training hidden substitution models.

I Holmes·G M Rubin

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Abstract

We derive an expectation maximization algorithm for maximum-likelihood training of substitution rate matrices from multiple sequence alignments. The algorithm can be used to train hidden substitution models, where the structural context of a residue is treated as a hidden variable that can evolve over time. We used the algorithm to train hidden substitution matrices on protein alignments in the Pfam database. Measuring the accuracy of multiple alignment algorithms with reference to BAliBASE (a database of structural reference alignments) our substitution matrices consistently outperform the PAM series, with the improvement steadily increasing as up to four hidden site classes are added. We discuss several applications of this algorithm in bioinformatics.

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Algorithms Amino Acid Substitution Base Sequence Bayes Theorem Computational Biology Databases, Genetic Internet Likelihood Functions Markov Chains Molecular Conformation Proteins Sequence Alignment Structure-Activity Relationship Journal Article

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I Holmes, G M Rubin. 2002-04-12. An expectation maximization algorithm for training hidden substitution models.. https://doi.org/10.1006/jmbi.2002.5405

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