UNIVERSITY OF MARYLAND

Large-scale genome sequence analysis has become an integral part of nearly all research areas in biology. Genome sequencing costs have dropped sharply but there has not been a commensurate increase in computational resources to support large-scale data processing and analysis. To address this gap we propose to build the Data Intensive Academic Grid (DIAG), a resource that will include a computational infrastructure, a high-performance storage network, and optimized data sets generated by mining the data from public data repositories like Community Cyberinfrastructure for Advanced Marine Microbial Ecology Research and Analysis (CAMERA) and National Center for Biotechnology Information (NCBI). The DIAG will leverage technologies developed for existing grid resources such as NSF supported TeraGrid, and Open Science Grid (OSG). We will also take advantage of a new technology called Virtual Machines (VMs) that will overcome the limitations that genomics researchers have previously experienced when they used large monolithic grid systems. The DIAG computational infrastructure will include between 800-1200 cores for high throughput computational analysis and between 80-120 cores for high-performance computational analysis. To handle the large amounts of data generated by such experiments we will deploy 500 Terabytes of clustered high-performance storage. The bioinformatics community will access the DIAG using Ergatis, a web based pipeline creation and management tool, bioinformatics oriented VMs, as well as interactive and programmatic access using technologies such as Nimbus, and the Virtual Data Toolkit from the Open Science Grid. These will allow users to build applications that leverage localization of custom genomic data sets and computational resources to perform analyses previously too difficult for groups with limited informatics support. We have recruited a constellation of over 26 genomic analysis service providers, tool developers and scientists with specific research interests in evolutionary analysis, microbial analysis, metagenomics, metatranscriptomics, plant genomics, proteomics and transcriptomics. These collaborators will be provided with an unprecedented level of computational power to accomplish their genomics-related activities and will receive a guaranteed number of computer cycles, a suite of tools to access the DIAG, training and engineering support. We will also partner with other academic grid service providers to develop standards and interoperation strategies to robustly share resources across a decentralized computer network. We will identify the most effective mode of operation for our users and grid partners with the goal of eventually applying for funding to replicate the optimized model across several geographically distributed centers. Our ultimate aim is to create a long-term sustainable analysis paradigm to effectively meet the needs of the larger genomics community. The DIAG will have significant national impact. The researchers and educators who will use the system are distributed geographically across the US. The software produced at our site will be easily utilized by NSF funded projects such as the Globus Workspaces Project, Open Science Grid and other projects involving large multi-institutional collaborations. In addition the DIAG will provide spillover capacity for two other science grids (MG-RAST and CAMERA) located in Illinois and California. To train the next generation of biologists, we have partnered with educators who will use the DIAG to create a unique training environment. This will bring a powerful new analysis system to a total of 340 undergraduate, graduate, and post graduate students annually in 15 different classroom settings. We also have allocated time on the DIAG to over 90 universities with predominantly underrepresented minorities and 75 NSF-funded women researchers in science, greatly enhancing the available computation and computing infrastructure for these groups.

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