SiGN-BN is gene network estimation software using Bayesian network model and B-spline nonparametric regression. It can estimate regulatory dependencies between genes as gene networks from gene expression data such as individual cell samples, gene knocked-down cell samples, drug-stimulated time series (time course) samples, and so on. For dynamic data such as time series data, SiGN-BN estimates a dynamic Bayesian network which takes dependencies between time points into account. For static data, it estimates a static (ordinal) Bayesian network that assumes each sample is independent from each other and does not consider temporal changes in expression data.

SiGN-BN implements several algorithms for estimating gene networks using Bayesian network model. Generally, because a Bayesian network requires huge computational time to learn its structure fitted to given gene expression data, it is not widely used for large-scale gene regulatory network analyses. Our research group develops several algorithms to overcome this problem using supercomputers. Currently, three algorithms are available depending on the size of gene networks: (a) the greedy hill-climbing (HC) algorithm + bootstrap method applicable to up to 1000 genes, (b) the neighbor node sampling & repeat (NNSR) algorithm applicable to genome-wide (whole genome) gene networks, and (c) parallel optimal structure search (Para-OS) algorithm for estimating the mathematically optimal network structures for small networks consisting of up to 37 genes.

Currently, SiGN-BN is freely available for HGC Supercomputer SHIROKANE and R-CCS supercomputer Fugaku users.



HC+Bootstrap: Release 1.8.2

NNSR: Release 0.16.7

Para-OS: Release 0.1.2


How to use SiGN-BN : Online tutorial of SiGN-BN.
Manual : User reference manual.
Download : Download site for the binary distribution.
Contact : Contact information & developer list


See SiGN Publication List for the complete list of our publications using SiGN software. For publications specific to SiGN-BN algorithms, see REFERENCE below.


SiGN-BN is based on the software that was originally developed in Laboratory of DNA Information Analysis, Human Genome Center, Institute of Medical Science, The University of Tokyo, that existed until March 2020, and then it was developed as SiGN-BN in the ISLiM (Next-generation integrated simulation of living matter) project in RIKEN Computational Science Research Program. Computational resources required for the development of SiGN-BN was provided by the HGC Supercomputer System, Human Genome Center, Institute of Medical Science, The University of Tokyo; and RIKEN Supercomputer system RICC, R-CCS K computer, and supercomputer Fugaku.


[1] Imoto, S., Goto, T., and Miyano, S. (2002). Estimation of genetic networks and functional structures between genes by using Bayesian network and nonparametric regression. Pacific Symposium on Biocomputing, 7, 175-186. (PDF on online proceedings)

[2] Tamada, Y., Shimamura, T., Yamaguchi, R., Imoto, S., Nagasaki, M., and Miyano, S. (2011). SiGN: Large-scale gene network estimation environment for high performance computing, Genome Informatics, 25 (1), 40-52.

[3] Tamada, Y., Imoto, S., Araki, H., Nagasaki, M., Print, C., Charnock-Jones, D.S., and Miyano, S. (2011). Estimating genome-wide gene networks using nonparametric Bayesian network models on massively parallel computers, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 8 (3), 683-697. (Pubmed)

[4] Tamada, Y., Imoto, S., and Miyano, S. (2011). Parallel algorithm for learning optimal Bayesian network structure, Journal of Machine Learning Research, 12, 2437-2459.

[5] Honda, H., Tamada, Y., and Suda, R., (2016). Efficient Parallel Algorithm for Optimal DAG Structure Search on Parallel Computer with Torus Network, In Proceedings of the 16th International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP 2016), Lecture Notes in Compute Sicence, 10048, 483-502.

[6] Tamada, Y. (2018). Memory Efficient Parallel Algorithm for Optimal DAG Structure Search using Direct Communication, Journal of Parallel and Distributed Computing, 119, 27-35, 2018.

[7] Tanaka, Y., Tamada, Y., Ikeguchi, M., Yamashita, F., and Okuno, Y. (2020). System-Based Differential Gene Network Analysis for Characterizing a Sample-Specific Subnetwork, Biomolecules, 10(2), 306.