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Bioinformatics Advance Access published online on June 24, 2004
Bioinformatics, doi:10.1093/bioinformatics/bth305
Bioinformatics © Oxford University Press 2004; all rights reserved
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Received September 9, 2003
Revised March 24, 2004
Accepted April 28, 2004

Article

SCA db: Spinocerebellar ataxia candidate gene database

Liu Y.-F. 1 Yang U.-C. 2*

1 Institute of Biochemistry, National Yang-Ming University, No. 155, Sec. 2, Li-Noun St., Taipei, Taiwan 11221, R.O.C.
2 Institute of Biochemistry, National Yang-Ming University, No. 155, Sec. 2, Li-Noun St., Taipei, Taiwan 11221, R.O.C.; Bioinformatics Research Center, National Yang-Ming University, No. 155, Sec. 2, Li-Noun St., Taipei, Taiwan 11221, R.O.C.; Institute of Bioinformatics, National Yang-Ming University, No. 155, Sec. 2, Li-Noun St., Taipei, Taiwan 11221, R.O.C.

* To whom correspondence should be addressed. E-mail: yang{at}ym.edu.tw.


  Abstract

The positional candidate gene approach accelerates the discovery of genes involved in disease. However, the properties of such disease genes are very diverse and the sample size of known disease genes is too small and does not warrant success by the use of a machine learning approach. A user-defined scoring system may thus help to determine the priority of candidate genes. Spinocerebellar ataxia (SCA) is a good model to test this approach because most SCA subtypes are caused by an expansion of short tandem repeats (STR). The SCA db is a candidate gene database for SCA, which collected 3,185 genes for 17 types of SCA. Those SCA subtypes that have known disease genes can be used as positive controls to optimize the parameters. The users may browse the candidate genes of a given SCA subtype by using the default parameters. The known disease genes were found to be the top 3 candidates using the default parameters. Alternatively, the users may score the candidate genes by changing the weight or the scores on the basis of their own working hypothesis. This database is available at http://ymbc.ym.edu.tw/sca/.


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