SIGN: LARGE-SCALE GENE NETWORK ESTIMATION ENVIRONMENT FOR HIGH PERFORMANCE COMPUTING

• TAMADA YOSHINORI

  Human Genome Center, Institute of Medical Science, The University of Tokyo

• SHIMAMURA TEPPEI

  Human Genome Center, Institute of Medical Science, The University of Tokyo

• YAMAGUCHI RUI

  Human Genome Center, Institute of Medical Science, The University of Tokyo

• IMOTO SEIYA

  Human Genome Center, Institute of Medical Science, The University of Tokyo

• NAGASAKI MASAO

  Human Genome Center, Institute of Medical Science, The University of Tokyo

• MIYANO SATORU

  Human Genome Center, Institute of Medical Science, The University of Tokyo RIKEN Computational Science Research Program

Abstract

Our research group is currently developing software for estimating large-scale gene networks from gene expression data. The software, called SiGN, is specifically designed for the Japanese flagship supercomputer “K computer” which is planned to achieve 10 petaflops in 2012, and other high performance computing environments including Human Genome Center (HGC) supercomputer system. SiGN is a collection of gene network estimation software with three different sub-programs: SiGN-BN, SiGN-SSM and SiGN-L1. In these three programs, five different models are available: static and dynamic nonparametric Bayesian networks, state space models, graphical Gaussian models, and vector autoregressive models. All these models require a huge amount of computational resources for estimating large-scale gene networks and therefore are designed to be able to exploit the speed of 10 petaflops. The software will be available freely for “K computer” and HGC supercomputer system users. The estimated networks can be viewed and analyzed by Cell Illustrator Online and SBiP (Systems Biology integrative Pipeline). The software project web site is available at http://sign.hgc.jp/ .

Journal

• Genome Informatics

  Genome Informatics 25 (1), 40-52, 2011

  Japanese Society for Bioinformatics

Keywords

• gene network
• Bayesian network
• state space model
• graphical Gaussian model
• vector autoregressive model
• high performance computing

Details

• CRID: 1390282679465555968
• NII Article ID: 130004567842
• DOI: 10.11234/gi.25.40
• ISSN: 2185842X
• ISSN: 09199454
• Text Lang: en
• Data Source:
  • JaLC
  • CiNii Articles
  • Crossref
• Abstract License Flag: Disallowed