The flood of genomic data is revolutionizing our approach to understanding normal cellular processes and the mechanisms of disease. This has driven the development of sophisticated computational methods to enable the analysis of this data, and a new emphasis on integrative techniques. It is critical to empower scientists with domain expertise by giving them direct access to these new technologies and techniques for analysis and interpretation. However, complex computational methods can be difficult to understand and use correctly. They may not easily work together or be reproduced. Since 2004 we have been sharing advanced mathematical methods and computational algorithms for genomic analysis with the research community in a user-friendly, freely available software package, GenePattern. The power of GenePattern is its accessibility to a broad community of users, the ability to access and interoperate a library of analytic and visualization modules, the ease with which the environment supports the rapid development and dissemination of new methods, and the reproducibility of computational research. Our goal for this renewal is to evolve and enhance the GenePattern platform to support the changing face of modern biomedical research brought on by new data acquisition platforms, new methods, and new genomics projects. We propose to broaden the content of the module repository to meet these new scientific challenges, and to serve the community's projects by providing a robust, scalable data processing platform. In addition, we will continue to provide and enhance our user support, training, and documentation.
Aim 1. Expanding GenePattern's module repository and providing interoperability with our Integrative Genomics Viewer to better support users and their research.
Aim 2. Extending GenePattern for general production and next-generation sequencing data processing for use by a wide range of genomics projects.
Aim 3. Training, documentation, continuing maintenance and support for the GenePattern package. Our progress over our previous funding period, extensive experience in software engineering, significant user base, large repository of genomic analysis tools, documentation and training for users make us well poised to carry out the aims of this proposal.
The GenePattern genomic analysis environment puts sophisticated computational methods within the reach of all biomedical researchers. Through the analysis of the increasing amount of available genomic data, GenePattern is used to address a variety of problems at the forefront of biomedical research including patient diagnosis and prognosis, identification of new drug targets, and understanding biological mechanisms. The work in this project will improve the GenePattern software by expanding the tools and methods it contains and enhance its capabilities to make it even better able to support the broadest range of biomedical researchers and large genomics projects tackling the important questions facing them today.
|Archer, Tenley C; Ehrenberger, Tobias; Mundt, Filip et al. (2018) Proteomics, Post-translational Modifications, and Integrative Analyses Reveal Molecular Heterogeneity within Medulloblastoma Subgroups. Cancer Cell 34:396-410.e8|
|Viswanathan, Vasanthi S; Ryan, Matthew J; Dhruv, Harshil D et al. (2017) Dependency of a therapy-resistant state of cancer cells on a lipid peroxidase pathway. Nature 547:453-457|
|Reich, Michael; Tabor, Thorin; Liefeld, Ted et al. (2017) The GenePattern Notebook Environment. Cell Syst 5:149-151.e1|
|Dhingra, Priyanka; Martinez-Fundichely, Alexander; Berger, Adeline et al. (2017) Identification of novel prostate cancer drivers using RegNetDriver: a framework for integration of genetic and epigenetic alterations with tissue-specific regulatory network. Genome Biol 18:141|
|Boulay, Gaylor; Awad, Mary E; Riggi, Nicolo et al. (2017) OTX2 Activity at Distal Regulatory Elements Shapes the Chromatin Landscape of Group 3 Medulloblastoma. Cancer Discov 7:288-301|
|Kim, Jong Wook; Abudayyeh, Omar O; Yeerna, Huwate et al. (2017) Decomposing Oncogenic Transcriptional Signatures to Generate Maps of Divergent Cellular States. Cell Syst 5:105-118.e9|
|Carlin, Daniel; Kosnicki, Kassi; Garamszegi, Sara et al. (2017) A multi-tool recipe to identify regions of protein-DNA binding and their influence on associated gene expression. F1000Res 6:784|
|Silterra, Jacob; Gillette, Michael A; Lanaspa, Miguel et al. (2017) Transcriptional Categorization of the Etiology of Pneumonia Syndrome in Pediatric Patients in Malaria-Endemic Areas. J Infect Dis 215:312-320|
|Durand, Neva C; Robinson, James T; Shamim, Muhammad S et al. (2016) Juicebox Provides a Visualization System for Hi-C Contact Maps with Unlimited Zoom. Cell Syst 3:99-101|
|Zhu, Xiaodong; Girardo, David; Govek, Eve-Ellen et al. (2016) Role of Tet1/3 Genes and Chromatin Remodeling Genes in Cerebellar Circuit Formation. Neuron 89:100-12|
Showing the most recent 10 out of 31 publications