Abstract
BackgroundTranscriptional 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.ResultsWe 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.ConclusionOur 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.
Highlights
Transcriptional modules (TM) consist of groups of co-regulated genes and transcription factors (TF) regulating their expression
We demonstrate the ability of this procedure to integrate information from gene expression and TF binding data by assessing the functional coherence of TMs constructed from real-world datasets
Expression datasets we used were the sporulation data set consisting of gene expression measurements throughout the sporulation process for the yeast strain SK1 [8]; the sporulation data set consisting of gene expression measurements during sporulation for the yeast strains SK1 and W303y [23]; the cell cycle data set consisting of gene expression measurements spanning two complete yeast cell cycles [24]; and the combined sporulationcell cycle dataset which we previously used to validate the context-specific infinite mixture model (CSIMM) model [21]
Summary
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. Two key high-throughput (HT) experimental technologies are capable of producing data offering insights into the expression regulatory mechanism on a genome scale. The second technology is the Chromatin Immuno-Precipitation on Chip (ChIP-chip) technology facilitating assessment of transcription factor binding events on a genomic scale [6,7]. Optimal 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
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