TRANSCRIPTOMICS

Expression Profiler (J. Vilo & M. Kapushesky, European Bioinformatics Institute, England) is a suite of tools for clustering, analysis and visualization of gene expression and other genomic data. The following tools allow one to perform cluster analysis, pattern discovery, pattern visualization, study and search Gene Ontology categories, generate sequence logos, extract regulatory sequences, study protein interactions, as well as to link analysis results to external tools and databases.  Some of the modules are listed below:

EPCLUST Expression Profile data CLUSTering and analysis:  A tool for expression data matrix handling and visualization
SPEXS  Sequence Pattern EXhaustive Search, a sequence pattern discovery tool 
SEQLOGO Sequence logo drawing tool 

ExpressionProfiler - a new algorithm for comparing and visualizing relationships between different clustering results, either flat versus flat, or flat versus hierarchical. When comparing a flat clustering to a hierarchical clustering, the algorithm cuts different branches in the hierarchical tree at different levels to optimize the correspondence between the clusters. (Reference: A. Torrente et al. 2005. Bioinformatics 21: 3993-3999).

GEPAS (Gene Expression Pattern Analysis Suite) - an experiment-oriented pipeline for the analysis of microarray gene expression data. It contains an incredible number of tools for normalization, preprocessing, viewing, clustering, differential expression, supervised classification, and data mining & analysis. (Reference: J.M. Vaquerizas et al. 2005. Nucl. Acids Res. 33: W616-W620).

DNA arrays: Analysis Tools (Centro Nacional de Investigaciones Oncologicas, Spain) includes proprocessing, viewers, unsupervised clustering, differential gene expression, supervised classification, and data mining modules.

Cyber-T (National Center for Genomic Resources, U.S.A.) if you have 2-dye data (such as what would be generated by the usual glass slide arrays probed with cy3/cy5-labelled cDN) use PAIRED DATA, while Control+Experimental is for those with Affymetrix-based data consisting of a separate control and experimental arrays.

NIA Array Analysis Tool - for microarray data analysis, which features the false discovery rate for testing statistical significance and the principal component analysis using the singular value decomposition method for detecting the global trends of gene-expression patterns. Additional features include: analysis of variance with multiple methods for error variance adjustment, correction of cross-channel correlation for two-color microarrays, identification of genes specific to each cluster of tissue samples, biplot of tissues and corresponding tissue-specific genes, clustering of genes that are correlated with each principal component (PC), and three-dimensional graphics. (Reference: A.A. Sharov et al. 2005. Bioinformatics 21: 2548-2549).

M@CBETH - MicroArray Classification Benchmarking Tool on Host server - offers the microarray community a simple tool for making optimal two-class predictions by by using randomizations of the benchmarking dataset. Registration is required in order to use this service. (Reference:  N.L.M.M. Pochet et al. 2005. Bioinformatics 21: 3185 - 3186).

MicroArray Genome Imaging & Clustering Tool (MAGIC Tool) - A JAVA teaching resource developed at Davidson College (U.S.A.) by Laurie Heyer and her undergraduate students (Reference: L. J. Heyer et al. 2005. Bioinformatics 21: 2114 - 2115).

VAMPIRE microarray analysis suite - is a statistical framework that models the dependence of measurement variance on the level of gene expression in the context of a Bayesian hierarchical model. (Reference: A. Hsiao et al. 2005. Nucl. Acids Res. 33: W627-W632).

MIDAW (MIcroarray Data Analysis Web tool)  - this  web tool performs data normalization (global, lowess & variance stabilizing procedures), descriptive analysis (boxplot, density estimation, cluster analysis), feature extraction (hypothesis testing, principal component, partial least square analysis) and discriminant analysis (Prediction Analysis of microarray, PAM). Results are displayed in output pages where figures and results can be easily downloaded. There are tutorial pages for each sessions of data processing.  (Reference: C. Romualdi et al. 2005. Nucl. Acids Res. 33: W644-W649).

GEMS - Gene Expression Mining Server - simple but promising new approach for biclustering based on a Gibbs sampling paradigm. (Reference: C.-J. Wu et al. 2004. Genome Informatics 15: 239-248).