Koreeda Tatsuya, Honda Hiroshi, Onami Jun-Ichi
CLINIC FOR Group, Nagisa Terrace 4F, 3-1-32 Shibaura, Minato-ku, Tokyo 108-0023, Japan.
Kao Corporation, Bunka, Sumida-ku, Tokyo 131-8501, Japan.
Genes (Basel). 2024 Dec 28;16(1):34. doi: 10.3390/genes16010034.
With the increasing speed of genomic, transcriptomic, and metagenomic data generation driven by the advancement and widespread adoption of next-generation sequencing technologies, the management and analysis of large-scale, diverse data in the fields of life science and biotechnology have become critical challenges. In this paper, we thoroughly discuss the use of cloud data warehouses to address these challenges. Specifically, we propose a data management and analysis framework using Snowflake, a SaaS-based data platform. We further demonstrate its convenience and effectiveness through concrete examples, such as disease variant analysis and in silico drug discovery. By introducing Snowflake, researchers can efficiently manage and analyze a wide array of biological data, enabling the discovery of new biological insights through integrated analysis. Through these specific methodologies and application examples, we aim to accelerate research progress in the field of bioinformatics.
随着下一代测序技术的进步和广泛应用，基因组学、转录组学和宏基因组学数据生成的速度不断加快，生命科学和生物技术领域大规模、多样化数据的管理和分析已成为关键挑战。在本文中，我们深入探讨了使用云数据仓库来应对这些挑战。具体而言，我们提出了一个使用基于软件即服务（SaaS）的数据平台Snowflake的数据管理和分析框架。我们通过具体示例，如疾病变异分析和计算机辅助药物发现，进一步证明了其便利性和有效性。通过引入Snowflake，研究人员可以高效地管理和分析各种生物数据，通过综合分析发现新的生物学见解。通过这些具体方法和应用示例，我们旨在加速生物信息学领域的研究进展。
