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Bioinformatics Advance Access published online on October 26, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl528
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Published by Oxford University Press 2006
Received July 24, 2006
Revised September 20, 2006
Accepted October 10, 2006

Article

Adding sequence context to a Markov background model improves the identification of regulatory elements

Nak-Kyeong Kim 1, Kannan Tharakaraman 1, and John L. Spouge 1 *

1 National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894 USA

* To whom correspondence should be addressed.
John L. Spouge, E-mail: spouge{at}ncbi.nlm.nih.gov


  Abstract

Motivation: Many computational methods for identifying regulatory elements use a likelihood ratio between motif and background models. Often, the methods use a background model of independent bases. At least two different Markov background models have been proposed with the aim of increasing the accuracy of predicting regulatory elements. Both Markov background models suffer theoretical drawbacks, so this article develops a third, context-dependent Markov background model from fundamental statistical principles.

Results: Datasets containing known regulatory elements in eukaryotes provided a basis for comparing the predictive accuracies of the different background models. Nonparametric statistical tests indicated that Markov models of order 3 constituted a statistically significant improvement over the background model of independent bases. Our model performed slightly better than the previous Markov background models. We also found that for discriminating between the predictive accuracies of competing background models, the correlation coefficient is a more sensitive measure than the performance coefficient.

Availability: Our C++ program is available at: ftp://ftp.ncbi.nih.gov/pub/spouge/papers/archive/AGLAM/2006-07-19.

Supplementary information: ftp://ftp.ncbi.nih.gov/pub/spouge/papers/archive/AGLAM/2006-07-19.

Associate Editor: John Quackenbush
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