Identifying Regulatory Targets of Cell Cycle Transcription Factors Using Gene Expression and ChIP-chip Data

Abstract

ChIP-chip data, which indicate binding of transcription factors (TFs) to DNA regions in vivo, are widely used to reconstruct transcriptional regulatory modules. However, the binding of a TF to a gene does not necessarily imply regulation. Thus, it is important to develop methods to identify regulatory targets of TFs from ChIP-chip data. We developed a method, called Temporal Relationship Identification Algorithm (TRIA), which uses gene expression data to identify a TF’s regulatory targets among its binding targets inferred from ChIP-chip data. We applied TRIA to yeast cell cycle microarray data and identified many plausible regulatory targets of cell cycle TFs. We validated our predictions by checking the expression coherence and the enrichments for functional annotation and known cell cycle genes. Moreover, we showed that TRIA performs better than two published methods (MA-Network and MFA).