Scalability and cost-effectiveness analysis of whole genome-wide association studies on Google Cloud Platform and Amazon Web Services.
Scalability and cost-effectiveness analysis of whole genome-wide association studies on Google Cloud Platform and Amazon Web Services.
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DOI：
10.1093/jamia/ocaa068
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发表时间：
2020-09-01
期刊：
影响因子：
--
通讯作者：
Avillach P
中科院分区：
文献类型：
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作者：
Krissaane I;De Niz C;Gutiérrez-Sacristán A;Korodi G;Ede N;Kumar R;Lyons J;Manrai A;Patel C;Kohane I;Avillach P
人类基因组学的进步产生了大量可用数据，推动了数据库的增长和可访问性，以进行更全面，更深入的遗传研究。我们提供了一种简单而创新的方法来优化云配置，以进行全基因组关联研究。我们利用Google Cloud Platform和Amazon Web Services上的Spark集群以及Hail（http：//doi.org/10.5281/zenodo.2646680）来分析和探索基因组变异数据集。对众多基于云的集群配置的比较评估表明，在4个不同的全基因组测序数据集上执行全基因组关联研究的速度和成本之间取得了成功和前所未有的折衷。结果在两个云提供商之间是一致的，对于加速遗传学研究非常有用。我们针对迁移到云时最常见的问题之一提出了一个及时的问题：速度和成本之间的权衡是什么？
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. 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. 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. 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?
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影响因子：
64.8
作者：
Bycroft C;Freeman C;Petkova D;Band G;Elliott LT;Sharp K;Motyer A;Vukcevic D;Delaneau O;O'Connell J;Cortes A;Welsh S;Young A;Effingham M;McVean G;Leslie S;Allen N;Donnelly P;Marchini J
通讯作者：
Marchini J
来自1,092个人基因组的遗传变异的综合图。
影响因子：
64.8
作者：
通讯作者：
--
影响因子：
12.3
作者：
Langmead B;Schatz MC;Lin J;Pop M;Salzberg SL
通讯作者：
Salzberg SL
影响因子：
64.8
作者：
Sudmant PH;Rausch T;Gardner EJ;Handsaker RE;Abyzov A;Huddleston J;Zhang Y;Ye K;Jun G;Fritz MH;Konkel MK;Malhotra A;Stütz AM;Shi X;Casale FP;Chen J;Hormozdiari F;Dayama G;Chen K;Malig M;Chaisson MJP;Walter K;Meiers S;Kashin S;Garrison E;Auton A;Lam HYK;Mu XJ;Alkan C;Antaki D;Bae T;Cerveira E;Chines P;Chong Z;Clarke L;Dal E;Ding L;Emery S;Fan X;Gujral M;Kahveci F;Kidd JM;Kong Y;Lameijer EW;McCarthy S;Flicek P;Gibbs RA;Marth G;Mason CE;Menelaou A;Muzny DM;Nelson BJ;Noor A;Parrish NF;Pendleton M;Quitadamo A;Raeder B;Schadt EE;Romanovitch M;Schlattl A;Sebra R;Shabalin AA;Untergasser A;Walker JA;Wang M;Yu F;Zhang C;Zhang J;Zheng-Bradley X;Zhou W;Zichner T;Sebat J;Batzer MA;McCarroll SA;1000 Genomes Project Consortium;Mills RE;Gerstein MB;Bashir A;Stegle O;Devine SE;Lee C;Eichler EE;Korbel JO
通讯作者：
Korbel JO
影响因子：
16.6
作者：
Cirulli, Elizabeth T.;White, Simon;Washington, Nicole L.
通讯作者：
Washington, Nicole L.