1. Design and implementation of a hybrid cloud system for large-scale human genomic research.
- Author
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Nagasaki, Masao, Sekiya, Yayoi, Asakura, Akihiro, Teraoka, Ryo, Otokozawa, Ryoko, Hashimoto, Hiroki, Kawaguchi, Takahisa, Fukazawa, Keiichiro, Inadomi, Yuichi, Murata, Ken T., Ohkawa, Yasuyuki, Yamaguchi, Izumi, Mizuhara, Takamichi, Tokunaga, Katsushi, Sekiya, Yuji, Hanawa, Toshihiro, Yamada, Ryo, and Matsuda, Fumihiko
- Subjects
HYBRID cloud computing ,DEEP learning ,SUPERCOMPUTERS ,GRAPHICS processing units ,HUMAN experimentation ,CLOUD computing ,IMAGE analysis - 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. Genomics: Designing and deploying a hybrid cloud computing system Researchers in Japan have set up a hybrid cloud computing system for genomic analysis. A team headed by Masao Nagasaki of Kyoto University has designed and developed a scalable bioinformatics system and demonstrated its effectiveness by analyzing over 11,000 human genome sequences. The system combines local computing resources with supercomputers at Japanese universities and cloud computing from Amazon Web Services. The analysis pipelines were designed to be distributed across multiple sites while also ensuring reproducibility. Computing resources can be added as needed, for example, nodes based on graphics processing unit could be added for workflows that rely on deep learning, such as analysis of pathology images. This hybrid cloud computing platform not only provides a tool for researchers in Japan but also serves as a reference point for designing similar systems in other institutes or countries. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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