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.

The project also includes four new, mRNA-Seq specific modules: one based on the gene list enrichment package GOSeq, three differential expression analysis packages, based on the R packages DESeq, DGESeq and EdgeR. These modules are built on the same simple user-interface that has made MeV accessible to researchers of all computer literacy levels. They sit alongside the classic modules like K-means clustering, EASE and Bayes Networks.

This project paves the way for future support of other next-generation sequencing data. We will use the lessons we have learned in building it to bring fully-supported next-generation genomic data analysis to future versions of MeV. We soon hope to provide the community the same powerful, graphical tool that has assisted so many in getting the most out of their genomics data.

New Features

New File Loader

MeV can now load summarized RNASeq data from a simple, tab-delimited file format. This format is fully described in the appendix of the MeV user manual. The loader can load count data, RPKM or FPKM, or combinations of the two data types.

GOSeq: GO term enrichment detection for RNASeq data.

GOSEQ is a technique for identifying differentially expressed sets of genes, such as GO terms while accounting for the biases inherent to sequencing data.

EdgeR: differential expression analysis of digital gene expression data

EdgeR is a Bioconductor software package for examining differential expression of replicated count data. An over-dispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of over-dispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated.

DESeq: Digital gene expresion analysis based on the negative binomial distribution

The BioC package DESeq provides a powerful tool to estimate the variance in count data and test for differential expression. It can estimate variance-mean dependence in count data from high-throughput sequencing assays and test for differential expression based on a model using the negative binomial distribution.

DGESeq: An R package to identify differentially expressed genes from RNA-Seq data.

Identify Differentially Expressed Genes from RNA-seq data.

Known Issues

•    The pilot project is currently only available for Windows users. Apologies to our Mac and Linux community; we will fully support RNASeq analysis on your platforms in the next full release of MeV. However, our development time will be much shortened by focusing on only one platform at this beginning stage.
•    We do not yet fully support annotation-dependent modules in the pilot. Therefore, the EASE, BN and LM modules have been disabled for RNASeq data until that support has been implemented. We will fully support those modules in future MeV releases.

Download the Pilot

Questions? Comments?

Please let us know in the MeV forums.