Welcome to the Metabolic Engineering and Bioinformatics group software page. Here you can download software tools developed by our group.
Gene ARMADA v. 1.0
Microarray technology allows gene expression profiling at a global level by measuring mRNA abundance. Gene ARMADA (Automated Robust MicroArray Data Analysis) is a MATLAB implemented program with a graphical user interface (GUI) which performs all steps of typical microarray data analysis; starting from importing raw data from several image analysis software outputs as well as text tab delimited files or already processed data that need to undergo statistical testing, ARMADA continues with processes including noise filtering, spot background correction, data normalization, statistical selection of differentially expressed genes based on parametric or non parametric statistics, cluster analysis based on several widely used clustering methods (Hierarchical, k-means, Fuzzy C-means), supervised classification (based on Discriminant Analysis, k-Nearest Neighbors or Support Vector Machines) and annotation steps, resulting in detailed lists of differentially expressed genes, formed clusters and classifier models. Along with the user friendly interface, ARMADA offers a variety of visualization options (MA plots, boxplots, array images, clustering heatmaps etc), a module which allows multiple analyses to be performed in batch mode under a specific analysis workflow and an annotation tool. The optimal number of clusters in any of the supported clustering algorithms can be estimated using the Gap statistic and Principal Component Analysis ability is also provided. Emphasis is given to the output data format which is fully customizable and contains a substantial amount of useful information such as detailed normalized and unnormalized expression values for each gene on each slide replicate along with several statistics concerning expression values for each experimental condition. Output can be also easily customized for later import to numerous other analysis tools (e.g. GenMAPP, RankGO etc.) and the output files can be easily imported in a spreadsheet like software such as MS Excel or in a database for further processing and storage. Analysis results can be saved as .mat files for further possible processing with MATLAB's built-in algorithms.
RankGO is a MATLAB program with a graphical user interface for the statistical analysis of Gene Ontology (GO) terms linked with lists of statistically identified differentially expressed genes, usually derived after a series of microarray experiments. Starting from annotated lists of differentially expressed genes, RankGO creates gene enrichment objects and uses statistical criteria (hypergeometric test, bootstrapping) to assess the significance of the emergent GO terms associated with the respective differentially expressed genes of the list. The program output is a detailed ranked list of significant GO terms associated with the relevant genes, based both on their enrichment plus their respective hypergeometric test p-value scores, thus setting a starting point for further analysis with respect to the identified functions. In order to use RankGO under Matlab you will need Matlab 6.5 or higher installed on your computer. In order to use RankGO as a stand alone application you will need Matlab 7.1 R14SP3 or the Matlab Component Runtime (MCR) 7.3 ( not higher) installed on your computer. MCR can be provided with the RankGO installer. To run RankGO under Matlab you have to add the folder where you will place RankGO to the Matlab path including its subfolders. You can download the following versions of RankGO installation files: