Collaborations

The cancer Biomedical Informatics Grid (caBIG™) is the National Cancer Institute's voluntary network of infrastructure, tools, and ideas linking researchers, physicians, and patients throughout the cancer community to speed research discoveries and improve patient outcome. GenePattern's membership in caBIG ensures transparent integration of caBIG data and tools in a flexible and user-friendly environment for the cancer research community.

The MGED Society aims to facilitate biological and biomedical discovery through the development of standards and technologies for data integration. The GenePattern team has made GenePattern compatible with MGED's MAGE-ML standard and is working with MGED developers to add compatibility with MAGE-TAB.

The NIH's National Centers for Biomedical Computing (NCBC) are intended to form the core of a national computational infrastructure for biomedical computing. The GenePattern team has active collaborations with two of these Centers:

National Center for the Multiscale Analysis of Genomic and Cellular Networks (MAGNet) at Columbia University

National Center for Integrative Biomedical Informatics (NCIBI) at the University of Michigan

The Stanford Microarray Database (SMD, http://smd.stanford.edu/) allows researchers around the world to store, annotate, view, analyze and share microarray data. Integrating GenePattern into the SMD gives researchers access to many new analysis tools as well as the ability to integrate and share their own analysis tools (Hubble, J. et al. Nucleic Acids Res. 2008 Oct 25).

MIT Academic Computing (http://web.mit.edu/ist/org/academic/) promotes and enables technology-based education at MIT. Leveraging GenePattern's strength as a workflow management tool, the Academic Computing team uses GenePattern pipelines to bring advanced computational methods and high performance computing resources to the classroom. Benefits provided by the GenePattern pipelines include: decreasing the lab time spent explaining the use of Unix commands and shell script editing, increasing the time used to investigate domain concepts, simplifying the task of preparing and presenting lab exercises, and reducing time spent on system administration of the high-performance computing clusters.