Inference of transcriptional regulatory networks using CAGE transcriptome dataset of Mus musculus

Abstract

We propose a computational inference of transcriptional regulatory networks from transcription start sites identified by Cap Analysis of Gene Expression (CAGE). Inference of the regulatory networks is a challenging task, and is performed by analyzing observed dependencies of transcription levels. Binding of transcription factors to an upstream sequence of a gene enables transcription initiation from a specific genomic position. The position is called Transcription Start Site (TSS). Eukaryotes have multiple TSSs for a single gene, which reflect diversity in combinatorial regulation by transcription factors. By the CAGE high-throughput profiling genome-wide identification of activated TSSs under various experimental conditions yields CAGE transcriptome datasets. To distinguish transcription responses caused by the diversity in combinatorial regulations, inference that takes multiple TSSs into account will be more effective than the conventional ones using only transcription levels of microarray dataset.