2007 Fiscal Year Final Research Report Summary
Bioinformatics in silico by the Unification of Symobols and Patterns
| Project/Area Number |
17200016
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| Research Category |
Grant-in-Aid for Scientific Research (A)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Sensitivity informatics/Soft computing
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| Research Institution | Waseda University |
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Principal Investigator |
MATSUYAMA Yasuo Waseda University, Faculty of Science and Engineering, Professor (60125804)
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| Co-Investigator(Kenkyū-buntansha) |
YANAGISAWA Masao Waseda University, Faculty of Science and Engineering, Professor (30170781)
YAMANA Hayato Waseda University, Faculty of Science and Engineeering, Professor (40230502)
KURUMIZAKA Hitoshi Waseda University, Faculty of Science and Engineering, Associate Professor (80300870)
INOUE Masato Waseda University, Faculty of Science and Engineeing, Associate Professor (70376953)
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| Project Period (FY) |
2005 – 2007
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| Keywords | bioinformatics / soft computing / DNA sequence analysis / promoter / transcription start site prediction / protein function analysis / tertiary structure prediction / proteome information processing |
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| Research Abstract |
This project was started towards the development of computational intelligence algorithms for finding soft patterns existing in DNA and amino acid sequences. The main methodology is in Aim. Wet biologists are included in this group so that overly abstract problems are suppressed. The unification between compute-based information scientists and test-tube-based life scientists still requires time, however, a steady step towards such collaboration was enhanced by this project with the following results :
(1) Prediction methods fir the transcription start site were established. On human .genome which is a representative of eukaryotes, a combination of the spectrum kernel, hidden Markov models, and FFT integrated by a support vector machine was presented. This mechanism yielded a top class ROC curves. On the prediction of E.coli which is a representative of prokaryotes, a combination of the independent component analysis and a support vector machine revealed the best prediction performance to date.
(2) Anew effective algorithm on the multiple sequence alignment was developed. This new method suppresses the appearance of multiple gaps in the same column. The gap extension can be regulated by piecewise linear penalties. The total algorithm is realized as the software named PRIME. The PRIME showed better performances than ClustalW and T-Coffee in the sense of resulting alignments and computational speed.
(3) The wet biology team hind an evidence on Rad5l which repairs cut double strands of DNA. The binding site of Rad51 is altered in breast cancer patients.
As was explained above, this research brought about fruitful results on post genome topics : The prediction of promoters and transcription start sites, a new multiple sequence alignment method leading to tertiary structure prediction, and a cancer property caused by protein functions.
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Research Products
(23 results)
