Journal Article

Complexity reduction in context-dependent DNA substitution models

William H. Majoros and Uwe Ohler

in Bioinformatics

Volume 25, issue 2, pages 175-182
Published in print January 2009 | ISSN: 1367-4803
Published online November 2008 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btn598
Complexity reduction in context-dependent DNA substitution models

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Motivation: The modeling of conservation patterns in genomic DNA has become increasingly popular for a number of bioinformatic applications. While several systems developed to date incorporate context-dependence in their substitution models, the impact on computational complexity and generalization ability of the resulting higher order models invites the question of whether simpler approaches to context modeling might permit appreciable reductions in model complexity and computational cost, without sacrificing prediction accuracy.

Results: We formulate several alternative methods for context modeling based on windowed Bayesian networks, and compare their effects on both accuracy and computational complexity for the task of discriminating functionally distinct segments in vertebrate DNA. Our results show that substantial reductions in the complexity of both the model and the associated inference algorithm can be achieved without reducing predictive accuracy.

Contact: bmajoros@duke.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  7581 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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