期刊
JOURNAL OF MOLECULAR BIOLOGY
卷 317, 期 5, 页码 753-764出版社
ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1006/jmbi.2002.5405
关键词
molecular evolution; bioinformatics; amino acid substitution rates; Markov models
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据