Quasi-consensus-based comparison of profile hidden Markov models for protein sequences | Zendy

Robel Kahsay | Zendy; Guiquan Wang | Zendy; Guang R. Gao | Zendy; Limin Liao | Zendy; Roland L. Dunbrack | Zendy

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Quasi-consensus-based comparison of profile hidden Markov models for protein sequences

Author(s) -

Robel Kahsay,

Guiquan Wang,

Guang R. Gao,

Limin Liao,

Roland L. Dunbrack

Publication year - 2005

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/bti374

Subject(s) - computer science , benchmark (surveying) , hidden markov model , markov chain , server , sequence (biology) , data mining , dynamic programming , markov model , artificial intelligence , theoretical computer science , machine learning , algorithm , biology , world wide web , geography , cartography , genetics

A simple approach for the sensitive detection of distant relationships among protein families and for sequence-structure alignment via comparison of hidden Markov models based on their quasi-consensus sequences is presented. Using a previously published benchmark dataset, the approach is demonstrated to give better homology detection and yield alignments with improved accuracy in comparison to an existing state-of-the-art dynamic programming profile-profile comparison method. This method also runs significantly faster and is therefore suitable for a server covering the rapidly increasing structure database. A server based on this method is available at http://liao.cis.udel.edu/website/servers/modmod

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