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

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. In this paper we report a feasibility study of inference of transcriptional regulatory network by using CAGE dataset. This is a preliminary study for inference that takes multiple TSSs into account. To perform inference of the regulatory networks, we used Bayesian network model which is appropriate to represent distribution of TSSs observed in CAGE dataset. Using the CAGE dataset published from the FANTOM3 project in RIKEN, we applied the model to inference of the regulatory networks of Mus musculus. For the purpose of the feasibility study, we compared inferred networks from the CAGE dataset with those from microarray dataset. Based on the comparative analysis, we confirmed that network inference by using the CAGE dataset is feasible like the case of conventional inferences by using microarray dataset. Based on the confirmation we discussed what we can reveal through the inference that takes into account multiple TSSs identified by a CAGE dataset. [ABSTRACT FROM AUTHOR]