We are proud to announce the release of MeV v4.8. This release includes a new module for the statistical and biological validation of gene clusters, clValid, and updated annotation from Bioconductor. Please download MeV at http://mev.tm4.org/.

The Attract Module has returned to MeV, now with significant improvements to its interface and large changes to its underlying algorithm. The algorithm identifies the core gene expression modules that are differentially activated between cell types or different sample groups, and elucidates the set of expression profiles which describe the range of transcriptional behavior within each module. The work is fully described in Mar JC, Wells CA, Quackenbush J. Defining an informativeness metric for clustering gene expression data. Bioinformatics. 2011 Apr 15;27(8):1094-100.J. PMID: 21330289.

For the short-term, MeV v4.7.4 with Attract is only available for Windows users. Mac and Linux versions are forthcoming.

We have updated MeV with a few minor bug fixes. See the release notes for details.

Working with Clusters

MeV provides a useful management system for evaluating groups of genes and samples.  These groups, or clusters, can be generated through a number of expression analysis or annotation based techniques.  Once stored, they can be visualized, manipulated and evaluated through the Cluster Manager tool, which is accessible from the MeV Result Tree.

The MeV Development team has been working over the last year on a major addition to MeV; we have expanded the program's data model to support data unique to RNA-Seq output: count data, RPKM/FPKM values, library sizes, class codes, etc. A new file loader allows for loading mapped read count or RPKM/FPKM values into MeV from a simple, tab-delimited format. Now all of the tools MeV contains can be applied easily to next-generation genomic data. In addition, we have added four new modules that provide analysis tools optimized for RNA-Seq data.

MeV is open-source software, released under the Artistic License v2.0 and hosted at Sourceforge.net. You are welcome to check out the source code and build and modify it to your heart's content, as long as you conform to the terms of the license.

MeV is a complex package. Here are a few steps to follow that should get you started building the package.

The MeV team has been working furiously to build a version of MeV that can load and analyze next generation sequencing data. Today we are proud to announce the first public beta version of an RNA-Seq capable MeV.

This project has shown that it is, indeed, feasible to adjust MEV's data model and processing functions to handle this new data; that the memory footprint is not untenable, and that the existing features so important to microarray data analysis can easily be applied to the richer datasets now available.

MeV v4.6.1 contains a several fixes for important MeV components. See the release notes for details.