The flood of genomic data is revolutionizing our approach to understanding normal cellular processes and the mechanisms of disease. This has driven the transformation of genomics research into an information science where sophisticated computational methods are critical to the integration and analysis of multiple types of data to inform hypotheses and drive research. Since 2004 GenePattern has provided the biomedical research community with a comprehensive environment for genomic analysis. Through GenePattern we share advanced mathematical methods and computational algorithms in a user-friendly, freely available software package. The power of the software is that it is accessible to a broad community of users, provides a library of analytic and visualization modules that can interoperate, supports the rapid development and dissemination of new methods, and supports reproducibility of computational research. The substantial impact of the software can be gauged by its citations and large user base. However, for many biomedical researchers the challenge still remains to know which methods to use for their study, how to use them correctly, and how to get the kind of guidance sitting side by side with an expert would provide. The goal of our specific aims for this competitive renewal is to address this critical nee through a new approach to how GenePattern delivers its capabilities to the entire research community.
Aim 1. Develop comprehensive GenePattern-Solutions for genomic research. These Solutions represent a new paradigm by providing analysis solutions rather than simply a collection of tools. We will formulate and implement Solutions tied to scientific scenarios and tasks and deliver them to our users through a new interactive, guided user interface.
Aim 2. Enhance GenePattern's interactivity, flexibility, and overall user experience. We will provide closer connectivity between visualization and analysis, add support for mobile devices, and further lower the barrier for other resources to leverage GenePattern's capabilities.
Aim 3. Support and grow the GenePattern user community. Importantly, we will place greater emphasis on open source development and address the high performance computing demands of current analysis approaches through a strategic alliance with Indiana University's (IU) National Center for Genome Analysis Support (NCGAS). Finally we will continue to provide and enhance our outreach activities, user support, training, and documentation. Our progress over our previous funding period, close involvement in genomic research, extensive software engineering experience, and significant user base make us well poised to carry out the aims of this proposal.

Public Health Relevance

The GenePattern genomic analysis environment puts sophisticated computational methods within the reach of all biomedical researchers and, through the analysis of the increasing amount of available genomic data, 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 developing GenePattern-Solutions. This new approach provides analysis solutions, rather than simply a collection of tools, to make GenePattern better able to support the broadest range of biomedical researchers tackling the important questions facing them today.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project (R01)
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Study Section
Biodata Management and Analysis Study Section (BDMA)
Program Officer
Ravichandran, Veerasamy
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University of California, San Diego
Internal Medicine/Medicine
Schools of Medicine
La Jolla
United States
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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

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