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DNA Research Advance Access published online on February 7, 2008
DNA Research, doi:10.1093/dnares/dsm034
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© The Author 2008. Kazusa DNA Research Institute
The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact journals.permissions@oxfordjournals.org

Markov Chain-based Promoter Structure Modeling for Tissue-specific Expression Pattern Prediction

Alexis Vandenbon1, Yuki Miyamoto2, Noriko Takimoto2, Takehiro Kusakabe2 and Kenta Nakai1,3,*

1 Department of Medical Genome Sciences, Graduate School of Frontier Sciences, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan
2 Department of Life Science, University of Hyogo, 3-2-1 Kouto, Kamigori, Ako-gun, Hyogo 678-1297, Japan
3 Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan

Received 17 October 2007 ; accepted 11 December 2007.

Transcriptional regulation is the first level of regulation of gene expression and is therefore a major topic in computational biology. Genes with similar expression patterns can be assumed to be co-regulated at the transcriptional level by promoter sequences with a similar structure. Current approaches for modeling shared regulatory features tend to focus mainly on clustering of cis-regulatory sites. Here we introduce a Markov chain-based promoter structure model that uses both shared motifs and shared features from an input set of promoter sequences to predict candidate genes with similar expression. The model uses positional preference, order, and orientation of motifs. The trained model is used to score a genomic set of promoter sequences: high-scoring promoters are assumed to have a structure similar to the input sequences and are thus expected to drive similar expression patterns. We applied our model on two datasets in Caenorhabditis elegans and in Ciona intestinalis. Both computational and experimental verifications indicate that this model is capable of predicting candidate promoters driving similar expression patterns as the input-regulatory sequences. This model can be useful for finding promising candidate genes for wet-lab experiments and for increasing our understanding of transcriptional regulation.

Key words: regulation of transcription; Markov chain; promoter modeling; in situ hybridization; transcription factor binding site

* To whom correspondence should be addressed. Tel. +81 3-5449-5131. Fax. +81 3-5449-5133. E-mail: knakai{at}ims.u-tokyo.ac.jp

Edited by Katsumi Isono


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