| URL: | http://www.mprabase.com |
| Full name: | A Massively Parallel Reporter Assay Database |
| Description: | MPRAbase, a manually curated database that currently harbors 129 experiments, encompassing 17,718,677 elements tested across 35 cell types and 4 organisms. MPRAbase is a comprehensive, easily accessible database whose goal is to store published MPRA datasets, process them, present them in a user-friendly manner, provide easy to use search tools and rapidly download these datasets. |
| Year founded: | 2023 |
| Last update: | 2023-11 |
| Version: | v1.0 |
| Accessibility: |
Accessible
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| Country/Region: | United States |
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| University/Institution: | University of California San Francisco |
| Address: | 1550 4th St, San Francisco, CA 94158 |
| City: | San Francisco |
| Province/State: | California |
| Country/Region: | United States |
| Contact name (PI/Team): | Nadav Ahituv, PhD |
| Contact email (PI/Helpdesk): | nadav.ahituv@ucsf.edu |
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MPRAbase: A Massively Parallel Reporter Assay Database. [PMID: 38045264]
Massively parallel reporter assays (MPRAs) represent a set of high-throughput technologies that measure the functional effects of thousands of sequences/variants on gene regulatory activity. There are several different variations of MPRA technology and they are used for numerous applications, including regulatory element discovery, variant effect measurement, saturation mutagenesis, synthetic regulatory element generation or characterization of evolutionary gene regulatory differences. Despite their many designs and uses, there is no comprehensive database that incorporates the results of these experiments. To address this, we developed MPRAbase, a manually curated database that currently harbors 129 experiments, encompassing 17,718,677 elements tested across 35 cell types and 4 organisms. The MPRAbase web interface (http://www.mprabase.com) serves as a centralized user-friendly repository to download existing MPRA data for independent analysis and is designed with the ability to allow researchers to share their published data for rapid dissemination to the community. |