We have developed a software package called GenePattern, which provides a comprehensive environment that can support a broad community of users at all levels of computational experience and sophistication, access to a repository of analytic and visualization tools and easy creation of complex analytic methods from them and the rapid development and dissemination of new methods. Perhaps the most important feature of GenePattern is that it supports a mechanism to guarantee the capture and independent replication of published computational methods and in silico results.
Interactive analysis notebook environments promise to streamline genomics research through interleaving text, multimedia, and executable code into unified, sharable, reproducible “research narratives.” However, current notebook systems require programming knowledge, limiting their wider adoption by the research community. We have developed the GenePattern Notebook environment, to our knowledge the first system to integrate the dynamic capabilities of notebook systems with an investigator-focused, easy-to-use interface that provides access to hundreds of genomic tools without the need to write code.
The Integrative Genomics Viewer (IGV) is a high-performance visualization tool for interactive exploration of large, integrated genomic datasets. It supports a wide variety of data types, including array-based and next-generation sequence data, and genomic annotations.
Gene Set Enrichment Analysis (GSEA) is a computational method that determines whether an a priori defined set of genes shows statistically significant, concordant differences between two biological states (e.g. phenotypes).
The Molecular Signatures Database (MSigDB) is a collection of annotated gene sets for use with GSEA software. From this website, you can search for gene sets by keyword, browse gene sets by name or collection, examine a gene set and its annotations, download gene sets, compute overlaps between your gene set and gene sets in MSigDB, Categorize members of a gene set by gene families, and view the expression profile of a gene set in a provided public expression compendia.
Medulloblastoma is the most common malignant brain cancer in children. Seventy percent of patients survive, but only 10% are able to live independently as adults due to neurologic disabilities from the tumor and treatment - specifically radiation. Understanding the disease can rapidly improve the quality of the life in survivors by more accurately identifying high and low risk tumors. Towards that goal, we and others are working to identify the drivers of medulloblastoma, such as mutations or chromosome alterations, which will enable development of more effective and less toxic targeted therapies. This portal contains data resources and supplementary files from our medulloblastoma publications as well as links to other relevant portals and resources.