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EVOLUTION
Comparative homology agreement search: An effective combination of homology-search methods

Intikhab Alam , Andreas Dress , Marc Rehmsmeier , and Georg Fuellen ^, ¶, ||

International NRW Graduate School in Bioinformatics and Genome Research, Center of Biotechnology, Bielefeld University, 33615 Bielefeld, Germany; Max Planck Institute for Mathematics in the Sciences, Inselstrasse 22-26, 04103 Leipzig, Germany; Department of Medicine, AG Bioinformatics, Domagkstrasse 3, 48149 Muenster, Germany; and ^¶Division of Bioinformatics, Department of Biology, University of Muenster, Schlossplatz 4, 48149 Muenster, Germany

Communicated by Walter M. Fitch, University of California, Irvine, CA, August 2, 2004 (received for review February 12, 2004)

Many methods have been developed to search for homologous members of a protein family in databases, and the reliability of results and conclusions may be compromised if only one method is used, neglecting the others. Here we introduce a general scheme for combining such methods. Based on this scheme, we implemented a tool called comparative homology agreement search (CHASE) that integrates different search strategies to obtain a combined "E value." Our results show that a consensus method integrating distinct strategies easily outperforms any of its component algorithms. More specifically, an evaluation based on the Structural Classification of Proteins database reveals that, on average, a coverage of 47% can be obtained in searches for distantly related homologues (i.e., members of the same superfamily but not the same family, which is a very difficult task), accepting only 10 false positives, whereas the individual methods obtain a coverage of 28–38%.

^|| To whom correspondence should be addressed. E-mail: fuellen{at}uni-muenster.de.

© 2004 by The National Academy of Sciences of the USA

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