Comparative homology agreement search: an effective combination of homology-search methods

Proceedings of the National Academy of Sciences of the United States of America
Intikhab AlamGeorg Fuellen

Abstract

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%.

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Citations

Feb 22, 2008·Die Naturwissenschaften·Georg Fuellen
Mar 17, 2010·Biosecurity and Bioterrorism : Biodefense Strategy, Practice, and Science·Willy A Valdivia-Granda
Jul 28, 2011·DNA Research : an International Journal for Rapid Publication of Reports on Genes and Genomes·Lena ScheubertGeorg Fuellen
Jul 20, 2007·Biochemical and Biophysical Research Communications·Xin-Yu ZhaoYu-Quan Wei
Jan 1, 2013·F1000Research·Adwait Govind JoshiRamanathan Sowdhamini
May 20, 2020·Briefings in Bioinformatics·Xiaopeng JinBin Liu
Jun 9, 2009·Journal of Chemical Information and Modeling·Bo LiLihe Zhang

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