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Bioinformatics Advance Access published online on June 5, 2008
Bioinformatics, doi:10.1093/bioinformatics/btn254
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© The Author (2008). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Modeling Promoter Grammars with Evolving Hidden Markov Models

Kyoung-Jae Won 1,#, Albin Sandelin 1, Troels Torben Marstrand 1 and Anders Krogh 1,*

1 The Bioinformatics Centre, Department of Biology & Biotech Research and Innovation Centre, University of Copenhagen, Ole Maaloes Vej 5, 2200 Copenhagen N, Denmark

*To whom correspondence should be addressed. Prof. Anders Krogh, E-mail: krogh{at}binf.ku.dk


  Abstract

Motivation: Describing and modeling biological features of eukaryotic promoters remains an important and challenging problem within computational biology. The promoters of higher eukaryotes in particular display a wide variation in regulatory features, which are difficult to model. Often several factors are involved in the regulation of a set of co-regulated genes. If so, promoters can be modeled with connected regulatory features, where the network of connections is characteristic for a particular mode of regulation.

Results: With the goal of automatically deciphering such regulatory structures, we present a method that iteratively evolves an ensemble of regulatory grammars using a Hidden Markov Model (HMM) architecture composed of interconnected blocks representing transcription factor binding sites and background regions of promoter sequences. The ensemble approach reduces the risk of over-fitting and generally improves performance. We apply this method to identify transcription factor binding sites and to classify promoters preferentially expressed in macrophages, where it outperforms other methods due to the increased predictive power given by the grammar.

Availability: The software and the data sets are available from http://modem./ucsd.edu/won/eHMM.tar.gz

Contact: krogh{at}binf.ku.dk

Associate Editor: Dr. Alex Bateman

#Present address: Dept of Chemistry & Biochemistry, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0359, USA

Received on January 28, 2008; revised on April 28, 2008; accepted on May 30, 2008

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