Krissaane, I., De Niz, C., Gutiérrez-Sacristán, A. et al. (8 more authors) (2020) Scalability and cost-effectiveness analysis of whole genome-wide association studies on Google Cloud Platform and Amazon Web Services. Journal of the American Medical Informatics Association, 27 (9). pp. 1425-1430. ISSN 1067-5027
Abstract
Objective
Advancements in human genomics have generated a surge of available data, fueling the growth and accessibility of databases for more comprehensive, in-depth genetic studies.
Methods
We provide a straightforward and innovative methodology to optimize cloud configuration in order to conduct genome-wide association studies. We utilized Spark clusters on both Google Cloud Platform and Amazon Web Services, as well as Hail (http://doi.org/10.5281/zenodo.2646680) for analysis and exploration of genomic variants dataset.
Results
Comparative evaluation of numerous cloud-based cluster configurations demonstrate a successful and unprecedented compromise between speed and cost for performing genome-wide association studies on 4 distinct whole-genome sequencing datasets. Results are consistent across the 2 cloud providers and could be highly useful for accelerating research in genetics.
Conclusions
We present a timely piece for one of the most frequently asked questions when moving to the cloud: what is the trade-off between speed and cost?
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 The Author(s). Published by Oxford University Press on behalf of the American Medical Informatics Association. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. |
Keywords: | whole genome; genome-wide association study; cloud computing; distributed systems |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Science (Sheffield) > School of Mathematics and Statistics (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 07 Aug 2020 06:39 |
Last Modified: | 24 Jan 2022 12:12 |
Status: | Published |
Publisher: | Oxford University Press (OUP) |
Refereed: | Yes |
Identification Number: | 10.1093/jamia/ocaa068 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:164194 |