News
08/21/08
MeV v4.2.01 is available for download
The latest bugfix release of MeV is available at
http://mev.tm4.org/.
08/01/08
MeV v4.2 is available for download
The MeV development team is proud to announce the release of MeV v4.2. This release adds some useful new analysis tools and some wonderful improvements to the user interface. You can download this latest version at http://mev.tm4.org/.
07/29/08
MeV tutorial at MGED 11
A tutorial on the use of MeV for data analysis will be held in Turino, Italy at the MGED Society conference, on September 1-4 2008. See the MGED program schedule for more details.
02/05/08
AMP v1.2 released on the Compbio Web Site
The Automated Microarray Processor v1.2 is released. Now you can perform GCRMA, MAS5.0/GCOS, and dChip normalizations in addition to RMA. Create quality control images for your data as well. Check out the AMP Page for more details.
01/18/08
MeV v4.1 released at www.tm4.org
MeV v4.1 is now available for download. New features include an annotation file update, new file loaders, NonPaR, Charm, and many smaller improvements and bug-fixes. See the MeV page for downloads and more details.
09/26/06
Oracle $1 Million Gift Helps Dana-Farber To Better Manage And Mine Genomic Data
Oracle's generous gift to DFCI will allow the Computational Biology and Functional Genomics Laboratory to merge genomic information with clinical data. Complete details can be found in the press release.
08/16/06
Use the Compbio helpdesk for Bioinformatics questions
Now you can send us your comments and ask for help using our online helpdesk system. We look forward to hearing from you.
MeV v4.2 is now available for download
Multiexperiment Viewer is a versatile microarray data analysis tool, incorporating sophisticated algorithms for clustering, visualization, classification, statistical analysis and biological theme discovery. Analyze gene expression or CGH microarray data and with MeV's many clustering, statistical analysis and graphical display tools. MeV generates informative and interrelated displays of expression and annotation data from single or multiple experiments.
RESOURCERER
RESOURCERER is a microarray-resource annotation and cross-reference database built using the analysis of expressed sequence tags (ESTs) and gene sequences provided by our Gene Indices (TGI) and Eukaryotic Gene Orthologues (EGO) databases. Researchers using mouse microarrays to profile animal tumors can search the database for microarrays that contain human versions of the mouse genes on their chip. They can then search the scientific literature for potentially relevant studies done with the human microarrays that correspond to the mouse chips.
GCOD
GeneChip Oncology Database is a collection of publicly available microarray gene expression data on Affymetrix GeneChip arrays related to human cancers. You can search the experiments in the collection, perform statistical analysis, and download processed data or to query gene expression profiles.
AMP v1.2 Released
The latest version of the Automated Microarray Processor v1.2 is released at http://compbio.dfci.harvard.edu/amp. You can create and account and log in to upload Affymetrix cel files for normalization. AMP can perform RMA, GCRMA, MAS5.0/GCOS, and dChip normalizations which can then be downloaded for analysis. Multiexperiment Viewer is the recommended analysis tool for viewing and manipulating your normalized data. AMP can also generate quality control images for you as well. Perform RLE and NUSE calculations and view weights and residuals as well as boxplots and histograms of your data.
Who Are We?
Genomics has revolutionized biology, but not in the ways that many of us initially envisioned. While the reference genome sequences and the catalogues of genes that genome projects have provided are useful starting points for understanding the basis for development and disease, the tools and technology spawned by the genome project have had a far greater impact. Our group focuses on the application of functional genomics techniques – including microarrays, proteomics, metabolomics, and other high-throughput approaches – and the development of computational approaches in support of these studies to develop a comprehensive view of human diseases including cancer. Our goal is to develop software, databases, and bioinformatics techniques that will allow the development of new diagnostics and a more complete understanding of the cellular networks that are mechanistically responsible for diseases. Our commitment is to make those tools widely and freely available to the research community to enable research beyond our own.
