Development of methods for comprehensive characterization of somatic variants causing transcription aberrations and their application to genomic medicine
| Project/Area Number |
18H03327
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Review Section |
Basic Section 62010:Life, health and medical informatics-related
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| Research Institution | National Cancer Center Japan |
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Principal Investigator |
Yuichi Shiraishi 国立研究開発法人国立がん研究センター, 研究所, ユニット長 (70516880)
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| Co-Investigator(Kenkyū-buntansha) |
片岡 圭亮 国立研究開発法人国立がん研究センター, 研究所, 分野長 (90631383)
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| Project Period (FY) |
2018-04-01 – 2021-03-31
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| Project Status |
Completed (Fiscal Year 2020)
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| Budget Amount *help |
¥13,910,000 (Direct Cost: ¥10,700,000、Indirect Cost: ¥3,210,000)
Fiscal Year 2020: ¥4,810,000 (Direct Cost: ¥3,700,000、Indirect Cost: ¥1,110,000)
Fiscal Year 2019: ¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000)
Fiscal Year 2018: ¥4,810,000 (Direct Cost: ¥3,700,000、Indirect Cost: ¥1,110,000)
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| Keywords | ゲノム / 統計科学 / がん / トランスクリプトーム / スプライシング / 大規模データ解析 / ゲノム変異 / がんゲノム / クラウド / 機械学習 / 後天的変異 |
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| Outline of Final Research Achievements |
We have summarized the research results of SAVNet, which we had been developing, and contributed to The Pan-Cancer Analysis of Whole Genomes project led by the International Cancer Genome Consortium. In addition, by applying SAVNet to data on rare diseases, we have succeeded in discovering several novel genetic mutations. In order to utilize large-scale transcriptome data in public databases such as Sequence Read Archive, we have developed an algorithm and software to identify splicing-related variants from transcriptome data alone. In addition, we developed an information analysis infrastructure for large-scale transcriptomes by integrating technologies such as container virtualization and cloud computing.
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| Academic Significance and Societal Importance of the Research Achievements |
ゲノムシークエンス技術の革新により、様々な疾患の新規原因遺伝子変異が同定され、さらに、個人のゲノムのシークエンスを行い、診断、治療に役立てる「ゲノム医療」の試みが進んでいる。一方で、特に、非エキソン領域における変異の機能的意義付けが困難であり、未だにゲノムデータが有するポテンシャルを人類は十分に活かしきれていない。本研究の目的は、ゲノム変異における最も重要なクラスの一つである、スプライシングの異常を引き起こす変異に着目し、当該変異を同定するための方法論の開発を進め、大規模データ解析を通じたデータベース化を行い、ゲノム医療に役立つ基盤技術・リソースの開発を行うものである。
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Report
(4 results)
Research Products
(19 results)
