大規模で多様なバイオデータ管理・解析のためのSnowflakeデータウェアハウス
次世代シーケンシング技術の進歩に伴い、ゲノムデータの生成速度は急速に増加しており、さらにバイオデータの多様性も加わり、その管理と解析が現代の研究者にとって重要な課題となっている。本稿では、大規模かつ多様なバイオデータ解析におけるクラウドデータウェアハウスの利用方法を詳細に論じ、特にSnowflakeを用いたデータ管理および解析のフレームワークを提案する。また、疾患バリアント解析やin silico創薬の具体例を通じて、その利便性と効果を示す。Snowflakeの導入によって、研究者は多様なバイオデータを効率的に管理・解析し、統合的な解析を通じて新たな生物学的知見を得ることが可能となる。これらの...
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| Published in | JSBi Bioinformatics Review Vol. 5; no. 2; pp. 35 - 43 |
|---|---|
| Main Author | |
| Format | Journal Article |
| Language | Japanese |
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特定非営利活動法人 日本バイオインフォマティクス学会
2024
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| Abstract | 次世代シーケンシング技術の進歩に伴い、ゲノムデータの生成速度は急速に増加しており、さらにバイオデータの多様性も加わり、その管理と解析が現代の研究者にとって重要な課題となっている。本稿では、大規模かつ多様なバイオデータ解析におけるクラウドデータウェアハウスの利用方法を詳細に論じ、特にSnowflakeを用いたデータ管理および解析のフレームワークを提案する。また、疾患バリアント解析やin silico創薬の具体例を通じて、その利便性と効果を示す。Snowflakeの導入によって、研究者は多様なバイオデータを効率的に管理・解析し、統合的な解析を通じて新たな生物学的知見を得ることが可能となる。これらの具体的な手法や応用事例を通じて、バイオインフォマティクス分野の研究進展を加速させることを目指す。 |
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| AbstractList | 次世代シーケンシング技術の進歩に伴い、ゲノムデータの生成速度は急速に増加しており、さらにバイオデータの多様性も加わり、その管理と解析が現代の研究者にとって重要な課題となっている。本稿では、大規模かつ多様なバイオデータ解析におけるクラウドデータウェアハウスの利用方法を詳細に論じ、特にSnowflakeを用いたデータ管理および解析のフレームワークを提案する。また、疾患バリアント解析やin silico創薬の具体例を通じて、その利便性と効果を示す。Snowflakeの導入によって、研究者は多様なバイオデータを効率的に管理・解析し、統合的な解析を通じて新たな生物学的知見を得ることが可能となる。これらの具体的な手法や応用事例を通じて、バイオインフォマティクス分野の研究進展を加速させることを目指す。 |
| Author | 是枝, 達也 |
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| Copyright | 2024 日本バイオインフォマティクス学会 |
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Minds Mach (Dordr). 2020; 30: 681-694. doi:10.1007/S11023-020-09548-1/FIGURES/5 [12] Shakil KA, Alam M. Cloud Computing in Bioinformatics and Big Data Analytics: Current Status and Future Research. Advances in Intelligent Systems and Computing. 2018; 654: 629-640. doi:10.1007/978-981-10-6620-7_60 [28] 1000 Genomes Phase 3 Reanalysis with DRAGEN 3.5, 3.7, 4.0, and 4.2 - Registry of Open Data on AWS. [cited 31 Jul 2024]. Available: https://registry.opendata.aws/ilmn-dragen-1kgp [20] Kimball R, Ross M. The data warehouse toolkit: the complete guide to dimensional modeling. 2011 [cited 31 Jul 2024]. Available: https://books.google.com/books?hl=ja&lr=lang_ja|lang_en&id=XoS2oy1IcB4C&oi=fnd&pg=PR1&ots=1EGbozlLiB&sig=3XNc8T1OWE9zwNOSCaHsJoRiHhc [11] Langmead B, Nellore A. Cloud computing for genomic data analysis and collaboration. Nature Reviews Genetics 2018 19: 4. 2018; 19: 208-219. doi:10.1038/nrg.2017.113 [17] Shared Responsibility Model - Amazon Web Services (AWS). [cited 31 Jul 2024]. Available: https://aws.amazon.com/compliance/shared-responsibility-model/?nc1=h_ls [13] Hanussek M, Bartusch F, Ger JK. Performance and scaling behavior of bioinformatic applications in virtualization environments to create awareness for the efficient use of compute resources. PLoS Comput Biol. 2021; 17: e1009244. doi:10.1371/JOURNAL.PCBI.1009244 [15] Rehman MH, Rajkumar M. On-Premise or Cloud Computing: An Integrated Novel Approach to Study the Adoption of Software Product's Deployment Model with Different Scopes. Lecture Notes in Networks and Systems. 2023; 516: 53-61. doi:10.1007/978-981-19-5221-0_6 [35] Kim S, Chen J, Cheng T, Gindulyte A, He J, He S, et al. PubChem 2023 update. Nucleic Acids Res. 2023; 51: D1373-D1380. doi:10.1093/nar/gkac956 [10] Kumuthini J, Chimenti M, Nahnsen S, Peltzer A, Meraba R, McFadyen R, et al. Ten simple rules for providing effective bioinformatics research support. PLoS Comput Biol. 2020; 16: e1007531. doi:10.1371/JOURNAL.PCBI.1007531 [36] Sterling T, Irwin JJ. ZINC 15 - Ligand Discovery for Everyone. J Chem Inf Model. 2015; 55: 2324-2337. doi:10.1021/ACS.JCIM.5B00559 [3] Innovation at Illumina: The road to the $600 human genome. [cited 31 Jul 2024]. Available: https://www.nature.com/articles/d42473-021-00030-9 [26] Understanding overall cost | Snowflake Documentation. [cited 31 Jul 2024]. Available: https://docs.snowflake.com/user-guide/cost-understanding-overall [9] Tabaie A, Orenstein EW, Kandaswamy S, Kamaleswaran R. Integrating structured and unstructured data for timely prediction of bloodstream infection among children. Pediatric Research 2022 93: 4. 2022; 93: 969-975. doi:10.1038/s41390-022-02116-6 [38] Streamlit in Snowflake. [cited 1 Aug 2024]. Available: https://www.snowflake.com/en/data-cloud/overview/streamlit-in-snowflake [2] Stephens ZD, Lee SY, Faghri F, Campbell RH, Zhai C, Efron MJ, et al. Big Data: Astronomical or Genomical? PLoS Biol. 2015; 13: e1002195. doi:10.1371/JOURNAL.PBIO.1002195 [6] Xia X. Bioinformatics and Drug Discovery. Curr Top Med Chem. 2017; 17: 1709. doi:10.2174/1568026617666161116143440 [31] Karczewski KJ, Francioli LC, Tiao G, Cummings BB, Alföldi J, Wang Q, et al. The mutational constraint spectrum quantified from variation in 141, 456 humans. Nature 2020 581: 7809. 2020; 581: 434-443. doi:10.1038/s41586-020-2308-7 [37] About Snowflake Notebooks | Snowflake Documentation. [cited 1 Aug 2024]. Available: https://docs.snowflake.com/en/user-guide/ui-snowsight/notebooks [41] Dwork C. Differential Privacy. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2006; 4052 LNCS: 1-12. doi:10.1007/11787006_1 [34] Davies M, Nowotka M, Papadatos G, Dedman N, Gaulton A, Atkinson F, et al. ChEMBL web services: Streamlining access to drug discovery data and utilities. Nucleic Acids Res. 2015; 43: W612-W620. doi:10.1093/NAR/GKV352 [18] Martins TGDS, Rangel F de S. Data warehouse and medical research. einstein (São Paulo). 2022; 20: eED6324. doi:10.31744/EINSTEIN_JOURNAL/2022ED6324 [1] Marx V. The big challenges of big data. Nature 2013 498:7453. 2013;498: 255-260. doi:10.1038/498255a [40] Data Privacy in Life Sciences: How Snowflake Data Clean Rooms Make It Happen. [cited 1 Aug 2024]. Available: https://www.snowflake.com/blog/data-privacy-life-sciences-clean-rooms [5] Seng KP, Ang L, Liew AW-C, Gao J. Multimodal Information Processing and Big Data Analytics in a Digital World. Multimodal Analytics for Next-Generation Big Data Technologies and Applications. 2019; 3-9. doi:10.1007/978-3-319-97598-6_1 [21] Dhaouadi A, Bousselmi K, Gammoudi MM, Monnet S, Hammoudi S. Data Warehousing Process Modeling from Classical Approaches to New Trends: Main Features and Comparisons. Data 2022, Vol 7, Page 113. 2022; 7: 113. doi:10.3390/DATA7080113 [14] Dai L, Gao X, Guo Y, Xiao J, Zhang Z. Bioinformatics clouds for big data manipulation. Biol Direct. 2012; 7: 1-7. doi:10.1186/1745-6150-7-43/TABLES/1 [24] Chaudhuri S, Dayal U. An overview of data warehousing and OLAP technology. ACM SIGMOD Record. 1997; 26: 65-74. doi:10.1145/248603.248616 [43] Thirunavukarasu AJ, Ting DSJ, Elangovan K, Gutierrez L, Tan TF, Ting DSW. Large language models in medicine. Nature Medicine 2023 29: 8. 2023; 29: 1930-1940. doi:10.1038/s41591-023-02448-8 [22] Batwada RK, Mittal N, Pilli ES. Uncovering Data Warehouse Issues and Challenges in Big Data Management. Communications in Computer and Information Science. 2020; 1317: 48-59. doi:10.1007/978-3-030-62625-9_5 [39] Snowpark Container Services | Snowflake Documentation. [cited 1 Aug 2024]. Available: https://docs.snowflake.com/en/developer-guide/snowpark-container-services/overview [30] CREATE STAGE | Snowflake Documentation. [cited 31 Jul 2024]. Available: https://docs.snowflake.com/en/sql-reference/sql/create-stage [16] Juhasz Z. Quantitative cost comparison of on-premise and cloud infrastructure based EEG data processing. Cluster Comput. 2021; 24: 625-641. doi:10.1007/S10586-020-03141-Y/TABLES/5 [25] Snowflakeで実現するAll-in-One Bioinformatics Platform @ Bio”Pack”athon2024#7 | TogoTV. [cited 31 Jul 2024]. Available: https://togotv.dbcls.jp/20240712.html [27] Introduction to unstructured data | Snowflake Documentation. [cited 31 Jul 2024]. Available: https://docs.snowflake.com/user-guide/unstructured-intro [33] 1000 Genomes - Registry of Open Data on AWS. [cited 31 Jul 2024]. Available: https://registry.opendata.aws/1000-genomes [4] Muir P, Li S, Lou S, Wang D, Spakowicz DJ, Salichos L, et al. The real cost of sequencing: Scaling computation to keep pace with data generation. Genome Biol. 2016; 17: 1-9. doi:10.1186/S13059-016-0917-0/FIGURES/4 [23] O'Leary DE. Embedding AI and crowdsourcing in the big data lake. IEEE Intell Syst. 2014; 29: 70-73. doi:10.1109/MIS.2014.82 [44] Yang X, Chen A, PourNejatian N, Shin HC, Smith KE, Parisien C, et al. A large language model for electronic health records. npj Digital Medicine 2022 5: 1. 2022; 5: 1-9. doi:10.1038/s41746-022-00742-2 |
| References_xml | – reference: [7] Brooksbank C, Bergman MT, Apweiler R, Birney E, Thornton J. The European Bioinformatics Institute's data resources 2014. Nucleic Acids Res. 2014; 42: D18-D25. doi:10.1093/NAR/GKT1206 – reference: [27] Introduction to unstructured data | Snowflake Documentation. [cited 31 Jul 2024]. Available: https://docs.snowflake.com/user-guide/unstructured-intro – reference: [35] Kim S, Chen J, Cheng T, Gindulyte A, He J, He S, et al. PubChem 2023 update. Nucleic Acids Res. 2023; 51: D1373-D1380. doi:10.1093/nar/gkac956 – reference: [5] Seng KP, Ang L, Liew AW-C, Gao J. Multimodal Information Processing and Big Data Analytics in a Digital World. Multimodal Analytics for Next-Generation Big Data Technologies and Applications. 2019; 3-9. doi:10.1007/978-3-319-97598-6_1 – reference: [3] Innovation at Illumina: The road to the $600 human genome. [cited 31 Jul 2024]. Available: https://www.nature.com/articles/d42473-021-00030-9 – reference: [4] Muir P, Li S, Lou S, Wang D, Spakowicz DJ, Salichos L, et al. The real cost of sequencing: Scaling computation to keep pace with data generation. Genome Biol. 2016; 17: 1-9. doi:10.1186/S13059-016-0917-0/FIGURES/4 – reference: [24] Chaudhuri S, Dayal U. An overview of data warehousing and OLAP technology. ACM SIGMOD Record. 1997; 26: 65-74. doi:10.1145/248603.248616 – reference: [8] Bellandi V, Ceravolo P, Maghool S, Siccardi S. Toward a General Framework for Multimodal Big Data Analysis. Big Data. 2022; 10: 408-424. doi:10.1089/BIG.2021.0326/ASSET/IMAGES/BIG.2021.0326_FIGURE6.JPG – reference: [30] CREATE STAGE | Snowflake Documentation. [cited 31 Jul 2024]. Available: https://docs.snowflake.com/en/sql-reference/sql/create-stage – reference: [43] Thirunavukarasu AJ, Ting DSJ, Elangovan K, Gutierrez L, Tan TF, Ting DSW. Large language models in medicine. Nature Medicine 2023 29: 8. 2023; 29: 1930-1940. doi:10.1038/s41591-023-02448-8 – reference: [20] Kimball R, Ross M. The data warehouse toolkit: the complete guide to dimensional modeling. 2011 [cited 31 Jul 2024]. Available: https://books.google.com/books?hl=ja&lr=lang_ja|lang_en&id=XoS2oy1IcB4C&oi=fnd&pg=PR1&ots=1EGbozlLiB&sig=3XNc8T1OWE9zwNOSCaHsJoRiHhc – reference: [23] O'Leary DE. Embedding AI and crowdsourcing in the big data lake. IEEE Intell Syst. 2014; 29: 70-73. doi:10.1109/MIS.2014.82 – reference: [10] Kumuthini J, Chimenti M, Nahnsen S, Peltzer A, Meraba R, McFadyen R, et al. Ten simple rules for providing effective bioinformatics research support. PLoS Comput Biol. 2020; 16: e1007531. doi:10.1371/JOURNAL.PCBI.1007531 – reference: [32] Genome Aggregation Database (gnomAD) - Registry of Open Data on AWS. [cited 31 Jul 2024]. Available: https://registry.opendata.aws/broad-gnomad/ – reference: [21] Dhaouadi A, Bousselmi K, Gammoudi MM, Monnet S, Hammoudi S. Data Warehousing Process Modeling from Classical Approaches to New Trends: Main Features and Comparisons. Data 2022, Vol 7, Page 113. 2022; 7: 113. doi:10.3390/DATA7080113 – reference: [16] Juhasz Z. Quantitative cost comparison of on-premise and cloud infrastructure based EEG data processing. Cluster Comput. 2021; 24: 625-641. doi:10.1007/S10586-020-03141-Y/TABLES/5 – reference: [28] 1000 Genomes Phase 3 Reanalysis with DRAGEN 3.5, 3.7, 4.0, and 4.2 - Registry of Open Data on AWS. [cited 31 Jul 2024]. Available: https://registry.opendata.aws/ilmn-dragen-1kgp/ – reference: [17] Shared Responsibility Model - Amazon Web Services (AWS). [cited 31 Jul 2024]. Available: https://aws.amazon.com/compliance/shared-responsibility-model/?nc1=h_ls – reference: [33] 1000 Genomes - Registry of Open Data on AWS. [cited 31 Jul 2024]. Available: https://registry.opendata.aws/1000-genomes/ – reference: [1] Marx V. The big challenges of big data. Nature 2013 498:7453. 2013;498: 255-260. doi:10.1038/498255a – reference: [38] Streamlit in Snowflake. [cited 1 Aug 2024]. Available: https://www.snowflake.com/en/data-cloud/overview/streamlit-in-snowflake/ – reference: [36] Sterling T, Irwin JJ. ZINC 15 - Ligand Discovery for Everyone. J Chem Inf Model. 2015; 55: 2324-2337. doi:10.1021/ACS.JCIM.5B00559 – reference: [39] Snowpark Container Services | Snowflake Documentation. [cited 1 Aug 2024]. Available: https://docs.snowflake.com/en/developer-guide/snowpark-container-services/overview – reference: [34] Davies M, Nowotka M, Papadatos G, Dedman N, Gaulton A, Atkinson F, et al. ChEMBL web services: Streamlining access to drug discovery data and utilities. Nucleic Acids Res. 2015; 43: W612-W620. doi:10.1093/NAR/GKV352 – reference: [15] Rehman MH, Rajkumar M. On-Premise or Cloud Computing: An Integrated Novel Approach to Study the Adoption of Software Product's Deployment Model with Different Scopes. Lecture Notes in Networks and Systems. 2023; 516: 53-61. doi:10.1007/978-981-19-5221-0_6 – reference: [31] Karczewski KJ, Francioli LC, Tiao G, Cummings BB, Alföldi J, Wang Q, et al. The mutational constraint spectrum quantified from variation in 141, 456 humans. Nature 2020 581: 7809. 2020; 581: 434-443. doi:10.1038/s41586-020-2308-7 – reference: [29] Registry of Open Data on AWS. [cited 31 Jul 2024]. Available: https://registry.opendata.aws/ – reference: [13] Hanussek M, Bartusch F, Ger JK. Performance and scaling behavior of bioinformatic applications in virtualization environments to create awareness for the efficient use of compute resources. PLoS Comput Biol. 2021; 17: e1009244. doi:10.1371/JOURNAL.PCBI.1009244 – reference: [25] Snowflakeで実現するAll-in-One Bioinformatics Platform @ Bio”Pack”athon2024#7 | TogoTV. [cited 31 Jul 2024]. Available: https://togotv.dbcls.jp/20240712.html – reference: [18] Martins TGDS, Rangel F de S. Data warehouse and medical research. einstein (São Paulo). 2022; 20: eED6324. doi:10.31744/EINSTEIN_JOURNAL/2022ED6324 – reference: [12] Shakil KA, Alam M. Cloud Computing in Bioinformatics and Big Data Analytics: Current Status and Future Research. Advances in Intelligent Systems and Computing. 2018; 654: 629-640. doi:10.1007/978-981-10-6620-7_60 – reference: [26] Understanding overall cost | Snowflake Documentation. [cited 31 Jul 2024]. Available: https://docs.snowflake.com/user-guide/cost-understanding-overall – reference: [42] Floridi L, Chiriatti M. GPT-3: Its Nature, Scope, Limits, and Consequences. Minds Mach (Dordr). 2020; 30: 681-694. doi:10.1007/S11023-020-09548-1/FIGURES/5 – reference: [2] Stephens ZD, Lee SY, Faghri F, Campbell RH, Zhai C, Efron MJ, et al. Big Data: Astronomical or Genomical? PLoS Biol. 2015; 13: e1002195. doi:10.1371/JOURNAL.PBIO.1002195 – reference: [40] Data Privacy in Life Sciences: How Snowflake Data Clean Rooms Make It Happen. [cited 1 Aug 2024]. Available: https://www.snowflake.com/blog/data-privacy-life-sciences-clean-rooms/ – reference: [6] Xia X. Bioinformatics and Drug Discovery. Curr Top Med Chem. 2017; 17: 1709. doi:10.2174/1568026617666161116143440 – reference: [9] Tabaie A, Orenstein EW, Kandaswamy S, Kamaleswaran R. Integrating structured and unstructured data for timely prediction of bloodstream infection among children. Pediatric Research 2022 93: 4. 2022; 93: 969-975. doi:10.1038/s41390-022-02116-6 – reference: [19] Inmon: Building the data warehouse - Google Scholar. [cited 31 Jul 2024]. Available: https://scholar.google.com/scholar_lookup?title=Building+the+Data+Warehouse&author=Inmon,+W.H.&publication_year=1996 – reference: [44] Yang X, Chen A, PourNejatian N, Shin HC, Smith KE, Parisien C, et al. A large language model for electronic health records. npj Digital Medicine 2022 5: 1. 2022; 5: 1-9. doi:10.1038/s41746-022-00742-2 – reference: [14] Dai L, Gao X, Guo Y, Xiao J, Zhang Z. Bioinformatics clouds for big data manipulation. Biol Direct. 2012; 7: 1-7. doi:10.1186/1745-6150-7-43/TABLES/1 – reference: [11] Langmead B, Nellore A. Cloud computing for genomic data analysis and collaboration. Nature Reviews Genetics 2018 19: 4. 2018; 19: 208-219. doi:10.1038/nrg.2017.113 – reference: [41] Dwork C. Differential Privacy. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2006; 4052 LNCS: 1-12. doi:10.1007/11787006_1 – reference: [22] Batwada RK, Mittal N, Pilli ES. Uncovering Data Warehouse Issues and Challenges in Big Data Management. Communications in Computer and Information Science. 2020; 1317: 48-59. doi:10.1007/978-3-030-62625-9_5 – reference: [37] About Snowflake Notebooks | Snowflake Documentation. [cited 1 Aug 2024]. Available: https://docs.snowflake.com/en/user-guide/ui-snowsight/notebooks |
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| ispartofPNX | JSBi Bioinformatics Review, 2024, Vol.5(2), pp.35-43 |
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