Inference of Genetic Regulatory Modules Using ChIP-on-chip and mRNA Expression Data

Abstract

We present here the strategy of data integration for inference of genetic regulatory modules. First, we construct all possible combinations of regulators of genes using chromatin-immunoprecipitation(ChIP)-chip data. Second, hierarchical clustering method is employed to analyze mRNA expression profiles. Third, integration method is applied to both of the data. Finally, we construct a genetic regulatory module which is involved in the function of ribosomal protein synthesis. Keyword: Genetic Regulatory Modules, ChIP-chip, mRNA expression profiles Corresponding author : Doheon Lee (Email: doheon@kaist.ac.kr) This work was supported by the Korea Science and Engineering Foundation(KOSEF) through the National Research Lab. Program (No. 2005-01450), and the Korean Systems Biology Research Grant (2005-00343). Introduction Inference of genetic regulatory modules can help to reduce genetic network complexity without significant loss of explanatory power. Gene modules can be defined in the sense that they are co-bound by the same set of transcription factors and are co-expressed with the same expression pattern at the same time. This can be viewed as that the genes in the module are co-regulated, and hence likely to have a common biological function. Expression profile reflects functional changes in mRNA levels in different conditions. On the other hand, genome-wide binding data suggests other point of view, since this data provides direct evidence of physical interactions. These two data sources can offer complementary information. To determine binding events in genomic location data, researchers have previously used a statistical model and chosen a relatively stringent P-value threshold with the intention of reducing false positives at the expense of false negatives. However the P-values form a continuum and a strict threshold is unlikely to produce good results. In the case of gene expression profiles, the number of clusters is determined quite arbitrarily due to the inherent nature of clustering algorithm. In the work of Bar-Joseph et al., in 2003, they introduced an algorithm which integrated genome-wide binding and expression data and finally showed an improvement than using either data source alone. This is a sort of following for the Bar-Joseph’s work. In order to construct genetic regulatory modules biologically more relevant, we try to utilize genome-wide binding data and expression profiles together without determining parameters of those explicitly.