Scalability and cost-effectiveness analysis of whole genome-wide association studies on Google Cloud Platform and Amazon Web Services.
J Am Med Inform Assoc
; 27(9): 1425-1430, 2020 09 01.
Article
em En
| MEDLINE
| ID: mdl-32719837
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?Palavras-chave
Texto completo:
1
Temas:
ECOS
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Aspectos_gerais
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Financiamentos_gastos
Bases de dados:
MEDLINE
Assunto principal:
Estudo de Associação Genômica Ampla
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Computação em Nuvem
Tipo de estudo:
Evaluation_studies
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Health_economic_evaluation
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Risk_factors_studies
Limite:
Humans
Idioma:
En
Revista:
J Am Med Inform Assoc
Assunto da revista:
INFORMATICA MEDICA
Ano de publicação:
2020
Tipo de documento:
Article