SiGN-SSM is open source gene network estimation software able to run in parallel on PCs and massively parallel supercomputers. The software estimates a state space model (SSM), that is a statistical dynamic model suitable for analyzing short time and/or replicated time series gene expression profiles. SiGN-SSM implements a novel parameter constraint effective to stabilize the estimated models. Also, by using a supercomputer, it is able to determine the gene network structure by the statistical permutation test in a practical time. SiGN-SSM is applicable not only to analyzing temporal regulatory dependencies between genes, but also to extracting the differentially regulated genes from time series expression profiles.

SiGN-SSM is distributed under GNU AFFERO GENERAL PUBLIC LICENCE (GNU AGPL) version 3. The pre-compile binaries for Linux (x86-64), MS Windows, and Mac OS X are also available in addition to the source code. The pre-installed binaries are available on the Human Genome Center (HGC) supercomputer system [6] and the Japanese flagship supercomputer "K computer" [7]. Some functions including statistical permutation test are available only on these supercomputers.


  • Feb. 4, 2014: Rel. 1.0.6 released. Now includes the makefile for AICS K computer. The pre-compiled binary for K computer is also available.
  • Sep. 19, 2012: The application of the SSM published in PLoS ONE. (Yamauchi et al, 2012 [8] in REFERENCE below)
  • Jun. 20, 2011: I noticed that signssm_plot.sh was not included in the package. So I uploaded it separately on the download page.
  • Feb. 23, 2011: Rel. 1.0.2 released. Fixed a bug in the estimation algorithm.
  • Feb. 8, 2011: SiGN-SSM paper [1] has been accepted for publication in journal Bioinformatics as an application note paper.
  • Jan. 21, 2011: Rel. 1.0.1 released and HOW TO USE page is updated.

    SiGN-SSM ...
  • Estimates a state space model from time series (time course) data.
  • Is an open source program so that everyone can access to the source code, and is free to improve it, develop for your own code, and distribute it. Read the license carefully.
  • Is suitable for short time series data measured for irregular time points.
  • Can handle multiple replicate data and missing values appropriately.
  • Runs in parallel with multi-thread on multi-core CPUs, multi-process on MPI, Sun (Oracle) Grid Engine, PC clusters, etc.
  • Implements a new constraint on the system coefficient matrix that is effective to stabilize the estimated parameters.
  • Implements permutation test that extracts the statistically significant gene-to-gene relationships (gene network edges), in addition to the estimation of model parameters.
  • Implements statistical test to evaluate the prediction by the estimated models.
  • Is high-scalable to the number of CPUs in the massively parallel computers with MPI.
  • Is able to output the estimated gene networks in CSML format so that users can analyze them with Cell Illustrator Online.

    About SSM : Describes what State Space Model is.
    How to use SiGN-SSM : Online tutorial of SiGN-SSM.
    Manual : User reference manual.
    Download : Source code and pre-compiled binary executable.
    Contact : Contact information & developer list


    The main paper describing SiGN-SSM is Tamada et al. (2011) [1] in REFERENCE below. Please cite this if you want to refer to SiGN-SSM in your publication, etc. The mathematical details are described in Hirose et al. (2008) [2] and Yamaguchi et al. (2008) [3].

    The example of the application to the real data analysis is Yamauchi et al. (2012) [8].


    SiGN-SSM is developed in the ISLiM (Next-generation integrated simulation of living matter) project in RIKEN Computational Science Research Program [5]. This is based on the previous implementation TRANS-MNET [2]. Computational resources required for the development of SiGN-SSM was provided by the HGC Supercomputer System, Human Genome Center, Institute of Medical Science, The University of Tokyo; and RIKEN Supercomputer system RICC.


    [1] Tamada, Y., Yamaguchi, R., Imoto, S., Hirose, O., Yoshida, R., Nagasaki, M., and Miyano, S. (2011). SiGN-SSM: open source parallel software for estimating gene networks with state space models. Bioinformatics 27 (8), 1172-1173.

    [2] Hirose, O., Yoshida, R., Imoto, S., Yamaguchi, R., Higuchi, T., Charnock-Jones, D.S., Print, C., and Miyano, S. (2008). Statistical inference of transcriptional module-based gene enetworks from time course gene expression profiles by using state space models. Bioinformatics 24 (7), 932-942.

    [3] Yamaguchi, R., Imoto, S., Yamauchi, M., Nagasaki, M., Yoshida, R., Shimamura, T., Hatanaka, Y., Ueno, K., Higuchi T., Gotoh, N., and Miyano, S. (2008). Predicting differences in gene regulatory systems by state space models. Genome Informatics 21, 101-113.

    [4] Wu, L.S.-Y., Pai, J.S, and Hosking J.R.M. (1996). An algorithm for estimating parameters of state-space models. Statistics & Probability Letters 28, 99-106.

    [5] LINK: RIKEN Computational Science Research Program

    [6] LINK: HGC Supercomputer System

    [7] LINK: RIKEN AICS "K computer"

    [8] Yamauchi, M., Yamaguchi, R., Nakata, A., Kohno, T., Nagasaki, M., Shimamura, T., Imoto, S., Saito, A., Ueno, K., Hatanaka, Y., Yoshida, R., Higuchi, T., Nomura, M., Beer, D.G., Yokota, J., Miyano, S., and Gotoh, N. (2012). Epidermal growth factor receptor tyrosine kinase defines critical prognostic genes of stage I lung adenocarcinoma. PLoS ONE 7(9), e43923. (See at PLoS ONE)