SHMM

by Michael Seifert, Jens Keilwagen, Marc Strickert, and Ivo Grosse

Description

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.

Paper

The paper Utilizing gene pair orientations for HMM-based analysis of promoter array ChIP-chip data has been published in Bioinformatics.

Download

• Supplementary data: Link to the implementations of LFC, HMM and SHMM.

Related Projects

• ARHMM: integrating local chromosomal dependencies into the analysis of tumor expression profiles
• PHHMM: improved analysis of Array-CGH data
• DSHMM: exploiting prior knowledge and gene distances in the analysis of tumor expression profiles
• MeDIP-HMM: HMM-based analysis of DNA methylation profiles
• HMM Book: Hidden Markov Models with Applications in Computational Biology

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