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Bioinformatics Advance Access published online on September 13, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti657
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© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received March 1, 2005
Revised May 23, 2005
Accepted September 1, 2005

Article

Markov model recognition and classification of DNA/protein sequences within large text databases

Jonathan D. Wren 1*, William H. Hildebrand 2, Sreedevi Chandrasekaran 2, and Ulrich K. Melcher 3

1 Advanced Center for Genome Technology, Stephenson Research and Technology Center, Department of Botany and Microbiology, The University of Oklahoma, 101 David L. Boren Blvd. Rm 2025, Norman, Oklahoma 73019
2 Department of Microbiology and Immunology, The University of Oklahoma Health Sciences Center, Oklahoma
3 Department of Biochemistry and Molecular Biology, Oklahoma State University, Stillwater, Oklahoma

* To whom correspondence should be addressed.
Jonathan D. Wren, E-mail: Jonathan.Wren{at}OU.edu


  Abstract

Motivation: Short sequence patterns frequently define regions of biological interest (e.g. binding sites, immune epitopes, primers, etc.), yet a large fraction of this information exists only within the scientific literature and is thus difficult to locate via conventional means (e.g. keyword queries or manual searches). We describe herein a system to accurately identify and classify sequence patterns from within large corpora using an n-gram Markov Model (MM).

Results: As expected, on test sets we found that identification of sequences with limited alphabets and/or regular structures such as nucleic acids (non-ambiguous) and peptide abbreviations (3-letter) was highly accurate, whereas classification of symbolic (1-letter) peptide strings with more complex alphabets was more problematic. The MM was used to analyze two very large, sequence-containing corpora: Over 7.75 million MEDLINE abstracts and 9,000 full-text articles from J Virology. Performance was benchmarked by comparing the results with J Virology entries in two existing manually curated databases: VirOligo and the HLA Ligand Database. Performance estimates were: 98% ± 2% precision/84% recall for primer identification and classification and 67% ± 6% precision/85% recall for peptide epitopes. We also find a dramatic difference between the amounts of sequence-related data reported in abstracts versus full-text. Our results suggest that automated extraction and classification of sequence elements is a promising, low-cost means of sequence database curation and annotation.

Availability: MM routine and datasets available upon request.


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