Microarray Software

TM4: A package of Open Source software programs for Microarray analysis TIGR Microarray Data Analysis System (MIDAS) is a microarray data quality filtering and normalization tool that allows raw experimental data to be processed through various data normalizations, filters, and transformations via a user-designed analysis pipeline. Currently implemented normalization and data analysis algorithms include total-intensity normalization, Lowess (Locfit) normalization, flip-dye consistency checking, replicates analysis, intensity-dependent z-score filtering (slice analysis), etc. MIDAS is implemented by Java language and thus a platform-independent application. It requires JDK v1.3 or higher. Refer to the included manual for details. Microarray experiments produce large amounts of data for even the simplest of experiments. In order to analyze data from many experiments that data must be stored in an accessible form, such as in a database. MADAM (MicroArray DAta Manager) is a java-based application designed to load and retrieve microarray data to and from a database (also supplied with the software). MADAM provides data entry forms, data report forms and additional applications necessary to maintain microarray data for further analysis. Madam requires JRE 1.3.1. TIGR MultiExperiment Viewer (MEV) is a Java application designed to allow the analysis of microarray data to identify patterns of gene expression and differentially expressed genes. Numerous normalization, clustering and distance algorithms have been implemented, along with a variety of graphical displays to best present the results. MEV was written to be flexible and expandable, and supports a variety of input and output formats. MEV requires version 1.2 or higher of Sun's JRE and J3D package. TIGR Spotfinder is a software tool designed for Microarray image processing using the TIFF image files generated by most microarray scanners. TIGR Spotfinder was written in C/C++ for PCs running Windows NT/2000/ME/XP.

A software tool designed to facilitate the presentation and analysis of microarray expression data, leading to the identification of genes that are differentially expressed. ArrayViewer is written in Java for cross-platform compatibility and reads and writes data using flat files or a database through stored procedures, See the ArrayViewer Overview as a Adobe Acrobat PDF File. Machines that lack the requirements for the MultiExperiment Viewer may use ArrayViewer for single experiment analysis.