| Project/Area Number |
16300095
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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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| Research Field |
Bioinformatics/Life informatics
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| Research Institution | KEIO UNIVERSITY |
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Principal Investigator |
SAKAKIBARA Yasubumi Keio University, FACULTY OF SCIENCE AND TECHNOLOGY, PROFESSOR, 理工学部, 教授 (10287427)
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| Co-Investigator(Kenkyū-buntansha) |
IMOTO Masaya Keio University, FACULTY OF SCIENCE AND TECHNOLOGY, PROFESSOR, 理工学部, 教授 (60213253)
SATO Kengo Keio University, FACULTY OF SCIENCE AND TECHNOLOGY, ASSISTANT PROFESSOR, 理工学部, 助手 (20365472)
YUGI Katsuyuki Keio University, FACULTY OF SCIENCE AND TECHNOLOGY, ASSISTANT PROFESSOR, 理工学部, 助手 (70433745)
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| Project Period (FY) |
2004 – 2006
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| Project Status |
Completed (Fiscal Year 2006)
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| Budget Amount *help |
¥12,800,000 (Direct Cost: ¥12,800,000)
Fiscal Year 2006: ¥2,700,000 (Direct Cost: ¥2,700,000)
Fiscal Year 2005: ¥3,100,000 (Direct Cost: ¥3,100,000)
Fiscal Year 2004: ¥7,000,000 (Direct Cost: ¥7,000,000)
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| Keywords | transcription biding site / transcriptional regulation / hidden Markov model / promoter sequence / chromatin immunoprecipitation / EGFR / 主成分分析 / クラスタリング / モチーフ変異体 / 決定木 / レポーターアッセイ / バイオインフォマティクス / 分子細胞生物学 / 転写因子結合部位 / ゲルシフトアッセイ |
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| Research Abstract |
Context dependencies of transcriptional activations among multiple transcription factors are important to understand the mechanisms of complex transcriptional regulations. Computational identifications of binding sites of transcription factors and computational analyses of cis-elements in promoter regions have been investigated to model cooperative binding activities of multiple transcription factors. We propose a novel discriminative detection method for precisely identifying transcription factor binding sites and their functional variants from both positive and negative samples. Our genome-wide experimental results on yeast Saccharomyces cerevisiae show that our method has presented significant performances for detecting experimentally verified consensus sequences compared with the existing motif detecting methods.
In addition, we use protein-protein interaction data to infer synergistic binding of cooperative transcription factors. Our fundamental idea is based on the assumption that genes contributing to a similar biological process are regulated under the same control mechanism. Our computational experiments in yeast show that predictions made by our method have successfully identified eight pairs of cooperative transcription factors that have literature evidences but could not be identified by the previous method.
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