| Project/Area Number |
17018026
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| Research Category |
Grant-in-Aid for Scientific Research on Priority Areas
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| Allocation Type | Single-year Grants |
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| Review Section |
Biological Sciences
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| Research Institution | Kyushu University |
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Principal Investigator |
KUHARA Satoru Kyushu University, 大学院・農学研究院, 教授 (00153320)
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| Co-Investigator(Kenkyū-buntansha) |
内山 郁夫 自然科学研究機構岡崎共通研究施設, 計算科学研究センター, 助教 (90243089)
黒川 顕 奈良先端科学技術大学院大学, 情報科学研究科, 准教授 (20343246)
平川 英樹 九州大学, 大学院・システム生命科学府, 特任助教 (80372746)
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| Co-Investigator(Renkei-kenkyūsha) |
KUROKAWA Ken 東京工業大学, 大学院・生命理工学研究科, 教授 (20343246)
UCHIYAMA Ikuo 大学共同利用機関法人自然科学研究機構, 助教 (90243089)
HIRAKAWA Hideki かずさDNA研究所, 植物ゲノム研究部, 研究員 (80372746)
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| Project Period (FY) |
2005 – 2009
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| Project Status |
Completed (Fiscal Year 2009)
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| Budget Amount *help |
¥81,800,000 (Direct Cost: ¥81,800,000)
Fiscal Year 2009: ¥16,000,000 (Direct Cost: ¥16,000,000)
Fiscal Year 2008: ¥16,000,000 (Direct Cost: ¥16,000,000)
Fiscal Year 2007: ¥15,900,000 (Direct Cost: ¥15,900,000)
Fiscal Year 2006: ¥15,900,000 (Direct Cost: ¥15,900,000)
Fiscal Year 2005: ¥18,000,000 (Direct Cost: ¥18,000,000)
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| Keywords | モデル化 / 遺伝子 / ゲノム / 進化 / 発現解析 / 発現制御 |
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| Research Abstract |
Our intensive research using comparative genomics and computational analysis was responsible for the following successes :
1) We improved the MBGD database allowing the identification of core structures amongst moderately related microbial genomes based on multiple genome alignments. We constructed a comparative genome alignment analysis tool for the visualization of complex evolutionary changes between closely related genomes.
2) We created systematic tools for meta-genome analysis and performed a large-scale comparative meta-genomic analysis of fecal samples. Our data clearly demonstrated a difference in overall composition and gene repertoire between adult- and infant-type gut microbiomes.
3) We developed and applied a new filter based approach to gene subset selection for kernel-based classifiers. We derived kernel forms from several well-known class separability criteria and applied gene subset selection based on the kernelized criteria to microarray cancer classification. The results have demonstrated that our proposed strategy performs better than gene ranking and the conventional filter approach.
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