| Project/Area Number |
16K09933
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Infectious disease medicine
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| Research Institution | Osaka University |
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Principal Investigator |
Nakamura Shota 大阪大学, 微生物病研究所, 特任准教授(常勤) (90432434)
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| Project Period (FY) |
2016-04-01 – 2019-03-31
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| Project Status |
Completed (Fiscal Year 2018)
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| Budget Amount *help |
¥4,550,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥1,050,000)
Fiscal Year 2018: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2017: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
Fiscal Year 2016: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
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| Keywords | メタゲノミクス / 病原体検出 / 次世代シークエンス / 感染症診断 / メタゲノム |
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| Outline of Final Research Achievements |
With the appearance of next generation sequencing (NGS) technologies, the volume of data presented to biologists has exploded. The NGS data is hard to analyze due to its sheer volume and often requires supercomputers to process in a timely manner. To face this increase, researchers have been using multi-threading solutions like MapReduce in a Hadoop-powered solution with HDFS, Hadoop distributed file system. However putting data into HDFS and then manipulating it is still very time-consuming. To address this issue, we developed Spark-BLAST, a system using the recently published in-memory cluster management software, Apache Spark. Spark-BLAST demonstrates the high efficiency of cluster computing, while proving quicker and more linear speed up than the existing solution. This offers a way to perform time-consuming similarity search with low-cost infrastructures; effectively providing the ability to handle the massive data from NGS to smaller-scale laboratories.
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| Academic Significance and Societal Importance of the Research Achievements |
国内では未だに小児科、救急、院内感染、臓器移植などの医療現場では感染症が疑われる不明疾患が多い。これら不明疾患において、既知・未知問わずメタゲノム解析により病原体を検出し、病態との関連性を各医療機関で個別に明らかにすることができれば今後の医療現場への貢献は計り知れない。次世代シークエンス技術の普及が順調に進行している中、メタゲノム解析による病原体検出の一般化が達成されない理由は明らかに解析過程がボトルネックになっていることは明白である。これまで一部の大型研究施設で可能であった本方法論の個別化を達成することができれば、感染症診断の大きな進歩に繋がる可能性がある。
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