NOSI cloud computing Supplement

$245,250P20FY2023GMNIH

Dartmouth College, Hanover NH

Investigators

Linked publications, trials & patents

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Abstract

Project summary The goal of this proposal is to explore and test opportunities to enhance data analytics and the reporting of results to users by incorporating cloud computing and storage capabilities. The overarching goal of the parent grant, Center for Quantitative Biology (CQB): A focus on -omics, from organisms to single cells is to establish a nationally recognized, multidisciplinary center that will enhance Dartmouthâs research profile and extramural funding in the area of single cell âomics, both experimentally and computationally. During Phase 1, the Dartmouth CQB COBRE has established the Data Analytics Core (DAC). The core has been extremely successful in developing data science approaches and cutting edge pipelines to process and analyze single cell and spatial transcriptomic data. This includes state of the art downstream analyses. Operational challenges faced by the core include the high cost of long-term data storage, significant demand for computing resources to process single cell and spatial data, and the high cost of maintaining state of the art compute infrastructure. Leveraging cloud computing and storage could increase our computing capacity and storage while providing a model to make resources available to smaller institutions that may not be able to invest in local resources. Archival storage costs on the cloud are significantly less expensive than local storage costs and on-demand access to the latest cloud compute technology would reduce analyst wait times which would improve turnaround times of data to end users. The overarching goal of this proposal is to determine the potential of cloud computing to meet the increasing data management and analysis needs of biomedical researchers with the following specific aims: 1) Train staff in Google Cloud Platform learning paths. 2) Engineer data reduction workflows to operate in the Google Cloud Platform cloud compute environment. 3) Assess the cost savings and ease of implementing data analyses in the Google Cloud Platform environment. As genomic data becomes more complex, and analysis becomes more computationally intensive cloud compute systems will be part of the solution to optimize data analysis infrastructure.

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