| Project/Area Number |
17310114
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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 |
基礎ゲノム科学
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| Research Institution | The University of Tokyo |
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Principal Investigator |
NAKAI Kenta The University of Tokyo, Institute of Medical Science, Professor (60217643)
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| Co-Investigator(Kenkyū-buntansha) |
KUSAKABE Takehiro University of Hyogo, Graduate School of Life Science, Associate Professor (40280862)
YAMASHITA Riu University of Tokyo, Institute of Medical Science, Researcher (10401259)
木下 賢吾 東京大学, 医科学研究所, 助教授 (60332293)
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| Project Period (FY) |
2005 – 2006
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| Project Status |
Completed (Fiscal Year 2006)
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| Budget Amount *help |
¥15,500,000 (Direct Cost: ¥15,500,000)
Fiscal Year 2006: ¥7,500,000 (Direct Cost: ¥7,500,000)
Fiscal Year 2005: ¥8,000,000 (Direct Cost: ¥8,000,000)
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| Keywords | ascidians / promoters / Markov chain / model building / in situ hybridization / muscle-specific expression / カタユウレイボヤ / バイオインフォマティクス / モチーフ / 組織特異的発現 / 組織特異的プロモーター / モチーフ抽出 / データベース / シスエレメント / ゲノム配列解析 |
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| Research Abstract |
Transcriptional regulation is the first level of regulation of gene expression and is therefore a major topic in computational biology. For the studies of DNA regions that specify tissue-specific gene expression, we chose tunicates as our main model organisms and constructed a database of transcription in tunicates (esp. Ciona intestinalis): DBTGR (http: //dbtgr.hgc.jp/). Then, we introduced a Markov chain-based promoter structure model that uses both shared motifs and shared features from an input set of promoter sequences to predict candidate genes with similar expression. The model uses positional preference, order, and orientation of motifs. The trained model is used to score a genomic set of promoter sequences: high-scoring promoters are assumed to have a structure similar to the input sequences and are thus expected to drive similar expression patterns. We applied our model on two datasets in Caenorhabditis elegans and in Ciona intestinalis. Both computational and experimental verifications indicate that this model is capable of predicting candidate promoters driving similar expression patterns as the input-regulatory sequences. This model can be useful for finding promising candidate genes for wet-lab experiments and for increasing our understanding of transcriptional regulation
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