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Nucleic Acids Research Advance Access published online on June 27, 2007
Nucleic Acids Research, doi:10.1093/nar/gkm414
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© 2007 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Computational Biology

The identification of complete domains within protein sequences using accurate E-values for semi-global alignment

Maricel G. Kann, Sergey L. Sheetlin, Yonil Park, Stephen H. Bryant and John L. Spouge*

National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD 20894, USA

*To whom correspondence should be addressed. Tel: 301 402 9310; Fax: 301 480 2484; Email: spouge{at}ncbi.nlm.nih.gov

Received December 14, 2006. Revised May 3, 2007. Accepted May 4, 2007.

The sequencing of complete genomes has created a pressing need for automated annotation of gene function. Because domains are the basic units of protein function and evolution, a gene can be annotated from a domain database by aligning domains to the corresponding protein sequence. Ideally, complete domains are aligned to protein subsequences, in a ‘semi-global alignment’. Local alignment, which aligns pieces of domains to subsequences, is common in high-throughput annotation applications, however. It is a mature technique, with the heuristics and accurate E-values required for screening large databases and evaluating the screening results. Hidden Markov models (HMMs) provide an alternative theoretical framework for semi-global alignment, but their use is limited because they lack heuristic acceleration and accurate E-values. Our new tool, GLOBAL, overcomes some limitations of previous semi-global HMMs: it has accurate E-values and the possibility of the heuristic acceleration required for high-throughput applications. Moreover, according to a standard of truth based on protein structure, two semi-global HMM alignment tools (GLOBAL and HMMer) had comparable performance in identifying complete domains, but distinctly outperformed two tools based on local alignment. When searching for complete protein domains, therefore, GLOBAL avoids disadvantages commonly associated with HMMs, yet maintains their superior retrieval performance.


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