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

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 (ChIPchip), 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 demonstr...

• Type: Journal
• Year: 2007
• Where: BMCBI
• Authors: Xiangdong Liu, Walter J. Jessen, Siva Sivaganesan, Bruce J. Aronow, Mario Medvedovic
• Keywords: BMCBI 2007, Co-regulated Genes, Computational Procedure, Gene Expression