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Swarm: A federated cloud framework for large-scale variant analysis.
Bahmani, Amir; Ferriter, Kyle; Krishnan, Vandhana; Alavi, Arash; Alavi, Amir; Tsao, Philip S; Snyder, Michael P; Pan, Cuiping.
Afiliación
  • Bahmani A; Stanford Healthcare Innovation Lab, Stanford University, California, United States of America.
  • Ferriter K; Stanford Center for Genomics and Personalized Medicine, Stanford University, California, United States of America.
  • Krishnan V; Department of Genetics, Stanford University, California, United States of America.
  • Alavi A; Stanford Center for Genomics and Personalized Medicine, Stanford University, California, United States of America.
  • Alavi A; Department of Genetics, Stanford University, California, United States of America.
  • Tsao PS; Stanford Center for Genomics and Personalized Medicine, Stanford University, California, United States of America.
  • Snyder MP; Department of Genetics, Stanford University, California, United States of America.
  • Pan C; Stanford Center for Genomics and Personalized Medicine, Stanford University, California, United States of America.
PLoS Comput Biol ; 17(5): e1008977, 2021 05.
Article en En | MEDLINE | ID: mdl-33979321
Genomic data analysis across multiple cloud platforms is an ongoing challenge, especially when large amounts of data are involved. Here, we present Swarm, a framework for federated computation that promotes minimal data motion and facilitates crosstalk between genomic datasets stored on various cloud platforms. We demonstrate its utility via common inquiries of genomic variants across BigQuery in the Google Cloud Platform (GCP), Athena in the Amazon Web Services (AWS), Apache Presto and MySQL. Compared to single-cloud platforms, the Swarm framework significantly reduced computational costs, run-time delays and risks of security breach and privacy violation.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Biología Computacional / Genómica / Nube Computacional Idioma: En Revista: PLoS Comput Biol Asunto de la revista: BIOLOGIA / INFORMATICA MEDICA Año: 2021 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Biología Computacional / Genómica / Nube Computacional Idioma: En Revista: PLoS Comput Biol Asunto de la revista: BIOLOGIA / INFORMATICA MEDICA Año: 2021 Tipo del documento: Article País de afiliación: Estados Unidos