Journal Article

Utilizing gene pair orientations for HMM-based analysis of promoter array ChIP-chip data

Michael Seifert, Jens Keilwagen, Marc Strickert and Ivo Grosse

in Bioinformatics

Volume 25, issue 16, pages 2118-2125
Published in print August 2009 | ISSN: 1367-4803
Published online April 2009 | e-ISSN: 1460-2059 | DOI:

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Motivation: Array-based analysis of chromatin immunoprecipitation (ChIP-chip) data is a powerful technique for identifying DNA target regions of individual transcription factors. The identification of these target regions from comprehensive promoter array ChIP-chip data is challenging. Here, three approaches for the identification of transcription factor target genes from promoter array ChIP-chip data are presented. We compare (i) a standard log-fold-change analysis (LFC); (ii) a basic method based on a Hidden Markov Model (HMM); and (iii) a new extension of the HMM approach to an HMM with scaled transition matrices (SHMM) that incorporates information about the relative orientation of adjacent gene pairs on DNA.

Results: All three methods are applied to different promoter array ChIP-chip datasets of the yeast Saccharomyces cerevisiae and the important model plant Arabidopsis thaliana to compare the prediction of transcription factor target genes. In the context of the yeast cell cycle, common target genes bound by the transcription factors ACE2 and SWI5, and ACE2 and FKH2 are identified and evaluated using the Saccharomyces Genome Database. Regarding A.thaliana, target genes of the seed-specific transcription factor ABI3 are predicted and evaluate based on publicly available gene expression profiles and transient assays performed in the wet laboratory experiments. The application of the novel SHMM to these two different promoter array ChIP-chip datasets leads to an improved identification of transcription factor target genes in comparison to the two standard approaches LFC and HMM.

Availability: The software of LFC, HMM and SHMM, the ABI3 ChIP–chip dataset, and Supplementary Material can be downloaded from


Journal Article.  7261 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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