| Project/Area Number |
11680388
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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 |
Intelligent informatics
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| Research Institution | Osaka University |
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Principal Investigator |
MATSUDA Hideo Graduate School of Engineering Science, Osaka University Associate Professor, 大学院・基礎工学研究科, 助教授 (50183950)
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| Project Period (FY) |
1999 – 2000
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| Project Status |
Completed (Fiscal Year 2000)
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| Budget Amount *help |
¥3,600,000 (Direct Cost: ¥3,600,000)
Fiscal Year 2000: ¥1,700,000 (Direct Cost: ¥1,700,000)
Fiscal Year 1999: ¥1,900,000 (Direct Cost: ¥1,900,000)
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| Keywords | genetic sequence analysis / gene classification / DNA chip / gene expression pattern / metabolic pathway / multiple alignment / Bioinformatics |
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| Research Abstract |
Development of the following systems and algorithms have been carried out for the construction of genetic regulatory network with computational life.
1. A system for exploring commonly conserved regions between gene sequences. Given a family of gene sequences of which function is the same as or similar to each other, we have developed a system for identifying sequence patterns that are conserved between the sequences. Our system detects such patterns by : construct a graph whose vertices are every possible fixed-length regions of the sequences, and whose edges are drawn if any two of regions have similarity more than a given cutoff ; and extract a maximum density subgraph in the graph. The effectiveness of our system is demonstrated by examining known conserved regions, such as Escherichia coli two-component systems, transcription regulation gene family, and head shock proteins.
2. A clustering system of genes using gene expression profile. DNA chip technology has been widely used for gene expression analyses. The experimental noises, however, hindered the effective use of the expression data. To cope with this issue, we have developed a system for reducing such noises by transforming time-series expression data into the frequency domain by the Fourier and the wavelet transformations and removing the high-frequency components of the signals.
3. A multiple alignment algorithm of metabolic pathways. Metabolic pathways have been recognized as one of the most well-known molecular networks. We have developed an algorithm for finding commonly-conserved reaction patterns between several pathways by comparing sequences of reactions represented by the EC numbers. The effectiveness of our algorithms is demonstrated by finding conserved reactions between sugar metabolism pathways and those between amino acid biosynthesis pathways.
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