Including biological literature improves homology search.
Annotating the tremendous amount of sequence information being generated requires accurate automated methods for recognizing homology. Although sequence similarity is only one of many indicators of evolutionary homology, it is often the only one used. Here we find that supplementing sequence similarity with information from biomedical literature is successful in increasing the accuracy of homology search results. We modified the PSI-BLAST algorithm to use literature similarity in each iteration of its database search. The modified algorithm is evaluated and compared to standard PSI-BLAST in searching for homologous proteins. The performance of the modified algorithm achieved 32% recall with 95% precision, while the original one achieved 33% recall with 84% precision; the literature similarity requirement preserved the sensitive characteristic of the PSI-BLAST algorithm while improving the precision.
Stanford Medical Informatics, Stanford University, 251 Campus Drive, MSOB X-215, Stanford, CA 94305-5479, USA. jeffrey.chang@stanford.edu
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JT
Chang
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Raychaudhuri
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RB
Altman
D000465:Algorithms; D019295:Computational Biology; D016208:Databases, Factual; D009323:Natural Language Processing; D010506:Periodicals as Topic; D012680:Sensitivity and Specificity; D016415:Sequence Alignment / Q000379:methods* / Q000706:statistics & numerical data; D012984:Software