Literature DB >> 18689819

Efficient representation and P-value computation for high-order Markov motifs.

Paulo G S da Fonseca1, Katia S Guimarães, Marie-France Sagot.   

Abstract

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/approxiamtely paguso/kmarkov.

Mesh:

Year:  2008        PMID: 18689819     DOI: 10.1093/bioinformatics/btn282

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  2 in total

1.  Repulsive parallel MCMC algorithm for discovering diverse motifs from large sequence sets.

Authors:  Hisaki Ikebata; Ryo Yoshida
Journal:  Bioinformatics       Date:  2015-01-11       Impact factor: 6.937

2.  Accurate recognition of cis-regulatory motifs with the correct lengths in prokaryotic genomes.

Authors:  Guojun Li; Bingqiang Liu; Ying Xu
Journal:  Nucleic Acids Res       Date:  2009-11-11       Impact factor: 16.971

  2 in total

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