An expectation maximization algorithm for training hidden substitution models

被引：62

作者：

Holmes, I ^{[1
]}

Rubin, GM ^{[1
]}

机构：

[1] Univ Calif Berkeley, Howard Hughes Med Inst, Berkeley, CA 94720 USA

来源：

JOURNAL OF MOLECULAR BIOLOGY | 2002年 / 317卷 / 05期

关键词：

molecular evolution; bioinformatics; amino acid substitution rates; Markov models;

D O I：

10.1006/jmbi.2002.5405

中图分类号：

Q5 [生物化学]; Q7 [分子生物学];

学科分类号：

071010 ; 081704 ;

摘要：

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 BAli-BASE (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. (C) 2002 Elsevier Science Ltd.

引用

页码：753 / 764

页数：12

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