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MeV_4_7_1_r2621_win.zip 2011-05-19 104.2 MB 22 weekly downloads
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MeV_4_7_1_readme.txt 2011-05-19 5.8 kB 22 weekly downloads
MeV v4.7.1 May 19, 2011 Bugfixes * A few new validation checks to RNA-Seq file loader * Fixed a bug that showed up if the input RANseq file was incomplete. MeV v4.7 May 16, 2011 New RNA-Seq Features MeV is now capable of loading and analyzing RNA-Seq 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 (Young, et al, 2010). 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 (Robinson et al., 2010). 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 (Anders and Huber, 2010). 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 (L. Wang et al., 2010). Identify differentially expressed genes from RNA-seq data. RNA sequencing is modeled as a random sampling process, in which each read is sampled independently and uniformly from every possible nucleotide in the sample. Under this assumption the number of reads coming from a gene (or transcript isoform) follows a binomial distribution (and could be approximated by a Poisson distribution). Based on this statistical model, Fisher’s exact test, likelihood ratio test and 2 other methods were proposed to identify differentially expressed genes. Other New Features Expression Graphs In the Sample Cluster Manager, a new graph view is available, called Expression Graphs. This option allows the creation of boxplots and bar charts of individual genes or groups of genes, compared across sample groups. Major updates to GSEA user interface Simpler, easier UI allows more intuitive use of the Gene Set Enrichment Module. Several calculation improvements and algorithm fixes have been applied to the newest release. Import File feature added to List Import option in Cluster Manager Clusters can now be created by loading a file containing a gene list. New MeV User Manual We have updated the MEV manual to a web-based format. Now, the help buttons within MeV link directly to a local copy of the user manual. Full information about the linked module is available immediately. R 2.11 All R-dependent MeV functions call R version 2.11 by default. R package auto-download MeV now automatically downloads R support packages after installation. The packages no longer have to be included in the initial download. "Set as Data Source" Option Removed We have removed a feature of the MeV result tree. Previously, the right-click context menu for cluster nodes in the result tree contained an option called "Set as Data Source". Choosing this option would cause the genes in the selected cluster to be treated as the entire MeV source dataset. All subsequent modules and filters run in MeV would be applied only to that subset of the data. We have removed this option because it was redundant and not particularly stable. Users who want to work with only a subset of their data have two options, both of which are more robust and fit better with the MeV data metaphor. Option 1. Launch as new Viewer: Create a gene or sample cluster from the result node of interest, by right-clicking on the viewer window and choosing Store Entire Cluster. Go to the appropriate Cluster Manager (Gene Cluster Manager or Sample Cluster Manager, in the result tree under the node Cluster Manager). Right-click on the cluster you just created, and choose Open/Launch -> Launch MeV Session. This will create a new Multiple Array Viewer containing only the data from the selected cluster. All analyses executed in this MAV will only apply to the selected data. Option 2. Select data cluster during module execution Create a gene or sample cluster from the result node of interest, by right-clicking on the viewer window and choosing Store Entire Cluster. In the module execution dialog, select that cluster from the cluster selection panel. The module will apply its analysis to only the genes/samples in that cluster. Bugfixes * The viewers for single-color Affymetrix data now handle the heatmap display of zero values properly * GSEA bugfixes * Missing HCL header bug resolved. * NMF Plotviewer error fixed. * The GSEA p-value graph viewer now saves and restores state correctly. * The Windows version of MeV v4.6.2 shipped without a MeV executable. While the program was still usable with the tmev.bat file, it was annoying the MeV.exe file has been restored. * Agilent file loader fixed for loading of 1-color data. * Agilent file loader fixed for loading of multiple samples simultaneously. Questions? Comments? Please let us know in the MeV forums. https://sourceforge.net/forum/?group_id=110558
Source: MeV_4_7_1_readme.txt, updated 2011-05-19