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ukbREST: efficient and streamlined data access for reproducible research in large biobanks

SUMMARY: Large biobanks, such as UK Biobank with half a million participants, are changing the scale and availability of genotypic and phenotypic data for researchers to ask fundamental questions about the biology of health and disease. The breadth of the UK Biobank data is enabling discoveries at a...

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Detalles Bibliográficos
Autores principales: Pividori, Milton, Im, Hae Kyung
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6546122/
https://www.ncbi.nlm.nih.gov/pubmed/30395166
http://dx.doi.org/10.1093/bioinformatics/bty925
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author Pividori, Milton
Im, Hae Kyung
author_facet Pividori, Milton
Im, Hae Kyung
author_sort Pividori, Milton
collection PubMed
description SUMMARY: Large biobanks, such as UK Biobank with half a million participants, are changing the scale and availability of genotypic and phenotypic data for researchers to ask fundamental questions about the biology of health and disease. The breadth of the UK Biobank data is enabling discoveries at an unprecedented pace. However, this size and complexity pose new challenges to investigators who need to keep the accruing data up to date, comply with potential consent changes, and efficiently and reproducibly extract subsets of the data to answer specific scientific questions. Here we propose a tool called ukbREST designed for the UK Biobank study (easily extensible to other biobanks), which allows authorized users to efficiently retrieve phenotypic and genetic data. It exposes a REST API that makes data highly accessible inside a private and secure network, allowing the data specification in a human readable text format easily shareable with other researchers. These characteristics make ukbREST an important tool to make biobank’s valuable data more readily accessible to the research community and facilitate reproducibility of the analysis, a key aspect of science. AVAILABILITY AND IMPLEMENTATION: It is implemented in Python using the Flask-RESTful framework for the API, and it is under the MIT license. It works with PostgreSQL and a Docker image is available for easy deployment. The source code and documentation is available in Github: https://github.com/hakyimlab/ukbrest.
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spelling pubmed-65461222019-06-13 ukbREST: efficient and streamlined data access for reproducible research in large biobanks Pividori, Milton Im, Hae Kyung Bioinformatics Applications Notes SUMMARY: Large biobanks, such as UK Biobank with half a million participants, are changing the scale and availability of genotypic and phenotypic data for researchers to ask fundamental questions about the biology of health and disease. The breadth of the UK Biobank data is enabling discoveries at an unprecedented pace. However, this size and complexity pose new challenges to investigators who need to keep the accruing data up to date, comply with potential consent changes, and efficiently and reproducibly extract subsets of the data to answer specific scientific questions. Here we propose a tool called ukbREST designed for the UK Biobank study (easily extensible to other biobanks), which allows authorized users to efficiently retrieve phenotypic and genetic data. It exposes a REST API that makes data highly accessible inside a private and secure network, allowing the data specification in a human readable text format easily shareable with other researchers. These characteristics make ukbREST an important tool to make biobank’s valuable data more readily accessible to the research community and facilitate reproducibility of the analysis, a key aspect of science. AVAILABILITY AND IMPLEMENTATION: It is implemented in Python using the Flask-RESTful framework for the API, and it is under the MIT license. It works with PostgreSQL and a Docker image is available for easy deployment. The source code and documentation is available in Github: https://github.com/hakyimlab/ukbrest. Oxford University Press 2019-06-01 2018-11-05 /pmc/articles/PMC6546122/ /pubmed/30395166 http://dx.doi.org/10.1093/bioinformatics/bty925 Text en © The Author(s) 2018. Published by Oxford University Press. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Applications Notes
Pividori, Milton
Im, Hae Kyung
ukbREST: efficient and streamlined data access for reproducible research in large biobanks
title ukbREST: efficient and streamlined data access for reproducible research in large biobanks
title_full ukbREST: efficient and streamlined data access for reproducible research in large biobanks
title_fullStr ukbREST: efficient and streamlined data access for reproducible research in large biobanks
title_full_unstemmed ukbREST: efficient and streamlined data access for reproducible research in large biobanks
title_short ukbREST: efficient and streamlined data access for reproducible research in large biobanks
title_sort ukbrest: efficient and streamlined data access for reproducible research in large biobanks
topic Applications Notes
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6546122/
https://www.ncbi.nlm.nih.gov/pubmed/30395166
http://dx.doi.org/10.1093/bioinformatics/bty925
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