Design and implementation of a hybrid cloud system for large-scale human genomic research.

IF 1 Q4 GENETICS & HEREDITY
Masao Nagasaki, Yayoi Sekiya, Akihiro Asakura, Ryo Teraoka, Ryoko Otokozawa, Hiroki Hashimoto, Takahisa Kawaguchi, Keiichiro Fukazawa, Yuichi Inadomi, Ken T Murata, Yasuyuki Ohkawa, Izumi Yamaguchi, Takamichi Mizuhara, Katsushi Tokunaga, Yuji Sekiya, Toshihiro Hanawa, Ryo Yamada, Fumihiko Matsuda
{"title":"Design and implementation of a hybrid cloud system for large-scale human genomic research.","authors":"Masao Nagasaki, Yayoi Sekiya, Akihiro Asakura, Ryo Teraoka, Ryoko Otokozawa, Hiroki Hashimoto, Takahisa Kawaguchi, Keiichiro Fukazawa, Yuichi Inadomi, Ken T Murata, Yasuyuki Ohkawa, Izumi Yamaguchi, Takamichi Mizuhara, Katsushi Tokunaga, Yuji Sekiya, Toshihiro Hanawa, Ryo Yamada, Fumihiko Matsuda","doi":"10.1038/s41439-023-00231-2","DOIUrl":null,"url":null,"abstract":"<p><p>In the field of genomic medical research, the amount of large-scale information continues to increase due to advances in measurement technologies, such as high-performance sequencing and spatial omics, as well as the progress made in genomic cohort studies involving more than one million individuals. Therefore, researchers require more computational resources to analyze this information. Here, we introduce a hybrid cloud system consisting of an on-premise supercomputer, science cloud, and public cloud at the Kyoto University Center for Genomic Medicine in Japan as a solution. This system can flexibly handle various heterogeneous computational resource-demanding bioinformatics tools while scaling the computational capacity. In the hybrid cloud system, we demonstrate the way to properly perform joint genotyping of whole-genome sequencing data for a large population of 11,238, which can be a bottleneck in sequencing data analysis. This system can be one of the reference implementations when dealing with large amounts of genomic medical data in research centers and organizations.</p>","PeriodicalId":36861,"journal":{"name":"Human Genome Variation","volume":null,"pages":null},"PeriodicalIF":1.0000,"publicationDate":"2023-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9908893/pdf/","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Human Genome Variation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1038/s41439-023-00231-2","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"GENETICS & HEREDITY","Score":null,"Total":0}
引用次数: 2

Abstract

In the field of genomic medical research, the amount of large-scale information continues to increase due to advances in measurement technologies, such as high-performance sequencing and spatial omics, as well as the progress made in genomic cohort studies involving more than one million individuals. Therefore, researchers require more computational resources to analyze this information. Here, we introduce a hybrid cloud system consisting of an on-premise supercomputer, science cloud, and public cloud at the Kyoto University Center for Genomic Medicine in Japan as a solution. This system can flexibly handle various heterogeneous computational resource-demanding bioinformatics tools while scaling the computational capacity. In the hybrid cloud system, we demonstrate the way to properly perform joint genotyping of whole-genome sequencing data for a large population of 11,238, which can be a bottleneck in sequencing data analysis. This system can be one of the reference implementations when dealing with large amounts of genomic medical data in research centers and organizations.

Abstract Image

Abstract Image

Abstract Image

为大规模人类基因组研究设计和实施混合云系统。
在基因组医学研究领域,由于高性能测序和空间 omics 等测量技术的进步,以及涉及 100 多万人的基因组队列研究取得的进展,大规模信息量不断增加。因此,研究人员需要更多的计算资源来分析这些信息。在此,我们介绍了日本京都大学基因组医学中心的混合云系统,该系统由内部超级计算机、科学云和公共云组成,是一种解决方案。该系统可以灵活处理各种异构计算资源需求的生物信息学工具,同时扩展计算能力。在混合云系统中,我们演示了如何对 11,238 个庞大人群的全基因组测序数据正确进行联合基因分型,这可能是测序数据分析中的一个瓶颈。该系统可作为研究中心和机构处理大量基因组医疗数据时的参考实施方案之一。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Human Genome Variation
Human Genome Variation Biochemistry, Genetics and Molecular Biology-Genetics
CiteScore
2.30
自引率
0.00%
发文量
39
审稿时长
13 weeks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信