Efficient representation and P-value computation for high-order Markov motifs
Motivation: Position weight matrices (PWMs) have become a standard for representing biological sequence motifs. Their relative simplicity has favoured the development of efficient algorithms for diverse tasks such as motif identification, sequence scanning and statistical significance evaluation. Ma...
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Published in | Bioinformatics Vol. 24; no. 16; pp. i160 - i166 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
England
Oxford University Press
15.08.2008
Oxford Publishing Limited (England) Oxford University Press (OUP) |
Subjects | |
Online Access | Get full text |
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Summary: | Motivation: Position weight matrices (PWMs) have become a standard for representing biological sequence motifs. Their relative simplicity has favoured the development of efficient algorithms for diverse tasks such as motif identification, sequence scanning and statistical significance evaluation. Markov chainbased models generalize the PWM model by allowing for interposition dependencies to be considered, at the cost of substantial computational overhead, which may limit their application. Results: In this article, we consider two aspects regarding the use of higher order Markov models for biological sequence motifs, namely, the representation and the computation of P-values for motifs described by a set of occurrences. We propose an efficient representation based on the use of tries, from which empirical position-specific conditional base probabilities can be computed, and extend state-of-the-art PWM-based algorithms to allow for the computation of exact P-values for high-order Markov motif models. Availability: The software is available in the form of a Java objectoriented library from http://www.cin.ufpe.br/~paguso/kmarkov. Contact: paguso@cin.ufpe.br |
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Bibliography: | ark:/67375/HXZ-5HVPF0F9-C ArticleID:btn282 To whom correspondence should be addressed. istex:D69F8286E4BD07BF92D14ACDB8D968E555E2B8F9 ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 ObjectType-Article-2 ObjectType-Feature-1 |
ISSN: | 1367-4803 1460-2059 1367-4811 |
DOI: | 10.1093/bioinformatics/btn282 |