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

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 a...

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Autores principales: Seifert, Michael, Keilwagen, Jens, Strickert, Marc, Grosse, Ivo
Formato: Texto
Lenguaje:English
Publicado: Oxford University Press 2009
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2722995/
https://www.ncbi.nlm.nih.gov/pubmed/19401402
http://dx.doi.org/10.1093/bioinformatics/btp276
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author Seifert, Michael
Keilwagen, Jens
Strickert, Marc
Grosse, Ivo
author_facet Seifert, Michael
Keilwagen, Jens
Strickert, Marc
Grosse, Ivo
author_sort Seifert, Michael
collection PubMed
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. Availability: The software of LFC, HMM and SHMM, the ABI3 ChIP–chip dataset, and Supplementary Material can be downloaded from http://dig.ipk-gatersleben.de/SHMMs/ChIPchip/ChIPchip.html. Contact: seifert@ipk-gatersleben.de
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spelling pubmed-27229952009-08-07 Utilizing gene pair orientations for HMM-based analysis of promoter array ChIP-chip data Seifert, Michael Keilwagen, Jens Strickert, Marc Grosse, Ivo Bioinformatics German Conference on Bioinformatics 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 http://dig.ipk-gatersleben.de/SHMMs/ChIPchip/ChIPchip.html. Contact: seifert@ipk-gatersleben.de Oxford University Press 2009-08-15 2009-04-28 /pmc/articles/PMC2722995/ /pubmed/19401402 http://dx.doi.org/10.1093/bioinformatics/btp276 Text en http://creativecommons.org/licenses/by-nc/2.0/uk/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle German Conference on Bioinformatics
Seifert, Michael
Keilwagen, Jens
Strickert, Marc
Grosse, Ivo
Utilizing gene pair orientations for HMM-based analysis of promoter array ChIP-chip data
title Utilizing gene pair orientations for HMM-based analysis of promoter array ChIP-chip data
title_full Utilizing gene pair orientations for HMM-based analysis of promoter array ChIP-chip data
title_fullStr Utilizing gene pair orientations for HMM-based analysis of promoter array ChIP-chip data
title_full_unstemmed Utilizing gene pair orientations for HMM-based analysis of promoter array ChIP-chip data
title_short Utilizing gene pair orientations for HMM-based analysis of promoter array ChIP-chip data
title_sort utilizing gene pair orientations for hmm-based analysis of promoter array chip-chip data
topic German Conference on Bioinformatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2722995/
https://www.ncbi.nlm.nih.gov/pubmed/19401402
http://dx.doi.org/10.1093/bioinformatics/btp276
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