What's New

• Our paper "Clustering samples characterized by time course gene expression profiles using the mixture of state space models'' will appear in Proc. of the Seventh Annual International Workshop on Bioinformatics and Systems Biology (IBSB) 2007. (28-September-2007)
• Invited poster presentation: "Module-based gene network construction with state-space models'' IEEE Statistical Signal Processing Workshop (SSP 2007) . (28-September-2007)
• Oral Presentation: the 2007 IASC-ARS special conference (June 7-8, 2007, Seoul Korea) (15-March-2007)
• Software: TRANS-MNET has been uploaded (14-March-2007)
• Publication: Our paper "Finding module-based gene networks" has been appered in IEEE Signal Processing Magagine, Vol.24, No.1, p.37-46. (09-March-2007)

Overview

TRANS-MNET available here implements the time series analysis of gene expression profiles with the state space model. The current version (last update 07-March-2007) is executable for Windows only and provided as an executable format. The source codes are written by C language (Osamu Hirose).

The application software provides us an integrated analytic tool for time course gene expression analysis. It contains a wide variety of applications as follows:

• Automatic extraction of genes relevant to temporal process of gene regulatory program.
• Statistical inference of transcriptional modules
• Statistical evaluation for temporal dependency between the estimated transcriptional modules
• Statistical evaluation for temporal dependency between individual genes.

To view and summarize the results, we recommend using R which is a free software environment for statistical computing and graphics, and also distribute a set of R scripts for accelerating summarization of data analysis.




Figure 1: Time series analysis of gene expression profiles with TRANS-MNET and a snapshot of graphical images which are created by R scripts that we developed. The method explores the potential transcriptional modules of genes which are relevant to the temporal gene expression program behind time series gene expression profiles, and simultaneously, evaluates the degree of connectivities across the overall genes.