[TM4-Announce] Public beta of the new RNASeq-capable MeV
Announcements for the TM4 Microarray Software Suite (http://www.tm4.org/)
tm4-announce at tm4.org
Fri Dec 3 16:11:04 MST 2010
Introducing the Public Beta of RNASeq-supporting MeV
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
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
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 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
EdgeR: differential expression analysis of digital gene
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
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.
. 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.
Please let us know in the MeV forums
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