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Mining genome variation to associate disease with transcription factor binding site alteration

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6 Author(s)
J. Ponomarenko ; Inst. of Cytology & Genetics, Novosibirsk, Russia ; T. Merkulova ; G. Orlova ; E. Gorshkova
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During the post genome era, single nucleotide polymorphism (SNP) analysis becomes the crossroad of bioinformatics, bioengineering and human health care. We have developed a data mining system, rSNP-Guide,, devoted to predict the transcription factor (TF) binding sites on DNA, alterations of which are associated with disease. rSNP-Guide formalizes the disease-referred experimental data on the alterations in the DNA binding to unknown TF, estimates the abilities of the DNA with mutations associated with disease to bind to each known TFs examined so that to separate one of them, which TF site is altered by the mutations in the best consistence with that of the unknown TF experimentally associated with diseases. The rSNP-Guide has been control tested on the SNPs with known site-disease relationships. Two TF sites associated with diseases were predicted and confirmed experimentally, namely: GATA site in K-ras gene (lung tumor) and YY1 site in TDO2 gene (mental disorders).

Published in:

Bioinformatics and Bioengineering Conference, 2001. Proceedings of the IEEE 2nd International Symposium on

Date of Conference:

4-6 Nov. 2001