Microbiome mining: machine learning for discovery of genetic dark matter, metabolic pathways, and ecological processes from metagenomes
| Project/Area Number |
18H03367
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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 63010:Environmental dynamic analysis-related
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| Research Institution | National Institute of Advanced Industrial Science and Technology |
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Principal Investigator |
延 優 国立研究開発法人産業技術総合研究所, 生命工学領域, 研究員 (40805644)
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| Project Period (FY) |
2018-04-01 – 2023-03-31
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| Project Status |
Granted (Fiscal Year 2021)
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| Budget Amount *help |
¥17,420,000 (Direct Cost: ¥13,400,000、Indirect Cost: ¥4,020,000)
Fiscal Year 2021: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
Fiscal Year 2020: ¥2,990,000 (Direct Cost: ¥2,300,000、Indirect Cost: ¥690,000)
Fiscal Year 2019: ¥3,120,000 (Direct Cost: ¥2,400,000、Indirect Cost: ¥720,000)
Fiscal Year 2018: ¥8,190,000 (Direct Cost: ¥6,300,000、Indirect Cost: ¥1,890,000)
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| Keywords | メタゲノム解析 / 微生物 / 代謝 / ゲノム / メタゲノム / 遺伝子 / Metagenomics / Metabolism / Genomics / ゲノム解析 / 機械学習 / Machine Learning / Machine learning / Syntrophy |
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| Outline of Annual Research Achievements |
Through machine-learning-based analysis of aromatic compound degradation genes across the prokaryotic domains, novel genes in catabolism of xenobiotics were identified. The analysis was focused on several phyla known to utilize aromatic compounds. Xenobiotics-degrading genes were often clustered together in genomes. They were often modular, being paired with different genes depending on the natural habitat of the organism. Interestingly, they also displayed recognizable trends in their evolutionary history, suggesting that this may be an effective tool in predicting novel genes supporting xenobiotics catabolism.
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| Current Status of Research Progress |
Current Status of Research Progress
1: Research has progressed more than it was originally planned.
Reason
The research is proceeding as planned.
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| Strategy for Future Research Activity |
In the coming year, the analysis will be expanded to genes involved in other high-profile metabolisms and catabolic pathways that may be unique to a specific domain (bacteria vs archaea). Targets include other genes for degrading non-aromatic xenobiotics and high-priority contaminants (detected frequently and persistent) with unknown genes and catabolic pathways that differentiate/distinguish bacteria and archaea. Bacteria and Archaea are distinct phylogenetic lineages, yet we understand very little about how they differ in physiology and way of life. Machine-learning-based approaches will be a novel approach to tackling this fundamental question in biology.
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Report
(2 results)
Research Products
(4 results)
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[Journal Article] Isolation of an archaeon at the prokaryote-eukaryote interface2020
Author(s)
Imachi H., Nobu M. K., Nakahara N., Morono Y., Ogawara M., Takaki Y., Takano Y., Uematsu K., Ikuta T., Ito M., Matsui Y., Miyazaki M., Murata K., Saito Y., Sakai S., Song C., Tasumi E., Yamanaka Y., Yamaguchi T., Kamagata Y., Tamaki H., Takai K.
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Journal Title
Nature
Volume: 577
Pages: 519-525
DOI
Related Report
Peer Reviewed / Open Access
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