Bayesian hierarchical model for transcriptional module discovery by jointly modeling gene expression and ChIP-chip data.

Bayesian hierarchical model for transcriptional module discovery by jointly modeling gene expression and ChIP-chip data. Research Abstract Details 

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Bayesian hierarchical model for transcriptional module discovery by jointly modeling gene expression and ChIP-chip data. Abstract Text:

Xiangdong Liu,Walter J Jessen,Siva Sivaganesan,Bruce J Aronow,Mario Medvedovic,

BACKGROUND: Transcriptional modules (TM) consist of groups of co-regulated genes and transcription factors (TF) regulating their expression. Two high-throughput (HT) experimental technologies, gene expression microarrays and Chromatin Immuno-Precipitation on Chip (ChIP-chip), are capable of producing data informative about expression regulatory mechanism on a genome scale. The optimal approach to joint modeling of data generated by these two complementary biological assays, with the goal of identifying and characterizing TMs, is an important open problem in computational biomedicine. RESULTS: We developed and validated a novel probabilistic model and related computational procedure for identifying TMs by jointly modeling gene expression and ChIP-chip binding data. We demonstrate an improved functional coherence of the TMs produced by the new method when compared to either analyzing expression or ChIP-chip data separately or to alternative approaches for joint analysis. We also demonstrate the ability of the new algorithm to identify novel regulatory relationships not revealed by ChIP-chip data alone. The new computational procedure can be used in more or less the same way as one would use simple hierarchical clustering without performing any special transformation of data prior to the analysis. The R and C-source code for implementing our algorithm is incorporated within the R package gimmR which is freely available at http://eh3.uc.edu/gimm. CONCLUSION: Our results indicate that, whenever available, ChIP-chip and expression data should be analyzed within the unified probabilistic modeling framework, which will likely result in improved clusters of co-regulated genes and improved ability to detect meaningful regulatory relationships. Given the good statistical properties and the ease of use, the new computational procedure offers a worthy new tool for reconstructing transcriptional regulatory networks.

Bayesian hierarchical model for transcriptional module discovery by jointly modeling gene expression and ChIP-chip data. Publishing Authors By Initials

X Liu,WJ Jessen,S Sivaganesan,BJ Aronow,M Medvedovic,

For similar proteins: transcription factors research abstracts see: proteins: transcription factors research

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Bayesian hierarchical model for transcriptional module discovery by jointly modeling gene expression and ChIP-chip data. Journal Published:

PUBLICATION TYPE: Research Support, N.I.H., Extr

Journal: BMC bioinformatics

VOLUME: 8

Page Numbers: 283

Journal Abbreviation: BMC Bioinformatics

ISSN: 1471-2105

DAY: 3

MONTH: 08

YEAR: 2007

Bayesian hierarchical model for transcriptional module discovery by jointly modeling gene expression and ChIP-chip data. Information

Number of References:

LANGUAGE: eng

NlmUniqueID: 100965194

Bayesian hierarchical model for transcriptional module discovery by jointly modeling gene expression and ChIP-chip data. Keywords Mesh Terms:

KEYWORDS: Transcription Factors

MESH TERMS: genetics

Chemical & Substance for Abstract: Bayesian hierarchical model for transcriptional module discovery by jointly modeling gene expression and ChIP-chip data. Information

Substance Name: Transcription Factors

Registry Number: 0

Grant and Affiliation Information for Bayesian hierarchical model for transcriptional module discovery by jointly modeling gene expression and ChIP-chip data.

AFFILIATION: Department of Environmental Health, University of Cincinnati, 3223 Eden Ave, ML 56, Cincinnati, Ohio 45267, USA. xiangdong.liu@cchmc.org

Country: England

AGENCY: United States NLM

GRANT: 1R03LM008248

ACRONYM: LM

MEDLINETA: BMC Bioinformatics

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