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 the supercomputers, 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 the extraction of the differentially regulated genes from time series expression profiles.
This is the open source and updated version of TRANS-MNET below.
The Cell System Markup Language (CSML) is an XML format for modeling, visualizing and simulating biopathways. CSML supports to represent several pathway types including metabolic, signaling, and genetic regulatory pathways. This project aims to facilitate the exchange of biopathway data in different formats. Effort has been made for data conversion from other XML formats. In addition, to allow extensible and flexible features of CSML, the Cell System Ontology (CSO) has been developed.
Cell Illustrator Online (CIO) enables biologists to draw, model, elucidate and simulate complex biological processes and systems. In conjunction with its outstanding drawing capabilities, CIO allows researchers to model metabolic pathways, signal transduction cascades, gene regulatory pathways and dynamic interactions of various biological entities such as genomic DNA, mRNA and proteins. CIO comes preloaded with TRANSPATH® pathways and chains, providing immediate access to signal transduction and metabolic pathway representations derived from the scientific literature. The integration of TRANSPATH reactions provides direct access to thousands of experimentally demonstrated binding and regulatory relationships – providing a unique set of building blocks for drawing custom networks and pathways. Cell Illustrator models are used to visualize biological pathways, interpret experimental data and test hypotheses. In addition, it provides researchers with model diagrams of publication quality and simulation result charts. You can evaluate the Cell Illustrator Online for one month with registration. Cell Illustrator Online Player (viewer of CSML format) is available without registration.
Cell Illustrator allows users to intuitively model, visualize, and simulate various biological pathways using Cell System Markup Language (CSML). For the detailed introductions and features, please refer to the website of Cell Illustrator. You can download the latest version of Cell Illustrator Pro and Cell Illustration Draw from above website by clicking on the item “Trial License - CI Pro” or “Free License - CI Draw” from the menu bar on the left side. To use CI Pro or CI Draw, you should finish a light registration procedure. After the initial registration and installation, you have the following trial period respectively: CI Pro workes as a 1-month trial version. CI Draw is now free for the users and you have 1-year to trial the product. You can also access to Cell Illustrator Download/Documentation/Support page for download.
We have implemented k-means clustering, hierarchical clustering and self-organizing maps in a single multipurpose open-source library of C routines, callable from other C and C++ programs. Using this library, we have created an improved version of Michael Eisen's well-known Cluster program for Windows, Mac OS X and Linux/Unix. In addition, we generated a Python and a Perl interface to the C Clustering Library, thereby combining the flexibility of a scripting language with the speed of C. The C Clustering Library and the corresponding Python C extension module Pycluster were released under the Python License, while the Perl module Algorithm::Cluster was released under the Artistic License. The GUI code Cluster 3.0 for Windows, Macintosh and Linux/Unix, as well as the corresponding command-line program, were released under the same license as the original Cluster code.
One of the significant challenges in gene expression analysis is to find unknown subtypes of several diseases at the molecular levels. This task can be addressed by grouping gene expression patterns of the collected samples on the basis of a large number of genes. Application of commonly used clustering methods to such a dataset however are likely to fail due to over-learning, because the number of samples to be grouped is much smaller than the data dimension which is equal to the number of genes involved in the dataset. To overcome such difficulty, we developed a novel model-based clustering method, referred to as the mixed factors analysis. The ArrayCluster is a freely available software to perform the mixed factors analysis. It provides us some analytic tools for clustering DNA microarray experiments, data visualization and an automatic detector for module transcriptional of genes that are relevant to the calibrated molecular subtypes and so on.
TRANS-MNET performs State Space Model to time-course microarray data. State Space Model is a statistical model for analyzing time-series data and state space model implemented in TRANS-MNET is optimized for microarray data. The typical microarray time-course data is high dimensional, but has few time-points, TRANS-MNET can use replicated time-courses includes biological and technical replicates. Also, parameter constraint imposed in TRANS-MNET yields the first-order autoregressive representation of state space models that can be viewed as a parsimonius parameterization of vector AR(1). The permutation test can be applied for finding significance of its AR coefficient and this achives gene regulatory networks.
Meta Gene Profiler (MetaGP) is a web application tool for discovering differentially expressed gene sets (meta genes) from the gene set library registered in our database. Once user submits gene expression profiles which are categorized into subtypes of conditioned experiments, or a list of genes with the valid pvalues, MetaGP assigns the integrated p-value to each gene set by combining the statistical evidences of genes that are obtained from gene-level analysis of significance. Currently, our gene set library supports Gene Ontology (GO) terms. A variety of statistical tools have been created so far to associate gene expression measurements to meta gene data, but these tools use the same general idea that computes integrated p-values only based on rank scores of gene-level significance. However, as will be illustrated, such an approach often causes a serious false discovery, referred to as rank transformationinduced pseudo-significance. In order to remove it, a new testing procedure is presented.