| Project/Area Number |
18570216
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Evolutionary biology
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| Research Institution | Nagahama Institute of Bio-Science and Technology |
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Principal Investigator |
IKEMURA Toshimichi Nagahama Institute of Bio-Science and Technology, Faculty of Bio-Science, Professor (50025475)
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| Co-Investigator(Kenkyū-buntansha) |
ABE Takashi Nagahama Institute of Bio-Science and Technology, Faculty of Bio-Science, Assistant Professor (30390628)
洞田 慎一 総合研究大学院大学, 葉山高等研究センター, 上級研究員 (70377125)
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| Project Period (FY) |
2006 – 2007
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| Project Status |
Completed (Fiscal Year 2007)
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| Budget Amount *help |
¥4,020,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥420,000)
Fiscal Year 2007: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
Fiscal Year 2006: ¥2,200,000 (Direct Cost: ¥2,200,000)
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| Keywords | Bioinformatics / Environmental microorganisms / SOM / Protein function prediction / Phylogenetic prediction / Self-Organizing Map / Earth Simulator / Methagenome / メタゲノム解析 / オリゴヌクレオチド頻度 / 自己組織化マツプ / SOM / 生物系統推定 / タンパク質機能推定 / 環境ゲノム / トリペプチド頻度 |
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| Research Abstract |
The homology search of base and amino acid sequences has become a basic tool in the bioinformatics field, which is essential not only for analyzing the evolution and phylogeny of genes and proteins but also for estimating the function of each protein gene in sequenced genomes. While sequence homology search is obviously useful, this method cannot estimate protein functions for almost half of genes in newly sequenced genomes, especially of novel species. To complement the sequence homology search, it is urgently required to establish methods for estimating protein functions based on different principles. By applying the BLSOM (Batch-learning SOM) method to amino-acid sequences of proteins, we established a function estimation method that utilizes clustering based on the similarity of oligopeptides frequencies. The BL-SOM for oligonucleotide frequencies could recognize genome-specific characteristics of oligonucleotide frequencies in individual genomes, permitting classification (self-organization) of genomic fragment sequences according to species without the need for species information during the calculation. We applied the BLSOMs to phylogenetic and functional prediction of a massive amount of environmental genomic sequences obtained by metagenomic analyses.
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