AI Article Synopsis
- Analyzing gene regulatory networks is a complex challenge in postgenomic research, with a focus on transcription factor (TF) binding sites that can impact disease phenotypes.
- The rSNP_Guide system helps predict these TF binding sites and has been validated by established connections between TF sites and diseases, as well as experimental data.
- The tool identifies potential TF sites in similar genes based on known alterations, classifying their TF-DNA interactions as 'present', 'weak', or 'absent', with statistical significance for each classification.
Article Abstract
The analysis of gene regulatory networks has become one of the most challenging problems of the postgenomic era. Earlier we developed rSNP_Guide (http://util.bionet.nsc.ru/databases/rsnp.html), a computer system and database devoted to prediction of transcription factor (TF) binding sites (TF sites), which can be responsible for disease phenotypes. The prediction results were confirmed by 70 known relationships between TF sites and diseases, as well as by site-directed mutagenesis data. The rSNP_Guide is being investigated as a tool for TF site annotation. Previously analyzed and characterized cases of altered TF sites were used to annotate potential sites of the same type and at the same location in homologous genes. Based on 20 TF sites with known alterations in TF binding to DNA, we localized 245 potential TF sites in homologous genes. For these potential TF sites, rSNP_Guide estimates TF-DNA interaction according to three categories: 'present', 'weak', and 'absent'. The significance of each assignment is statistically measured.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC165559 | PMC |
| http://dx.doi.org/10.1093/nar/gkg112 | DOI Listing |
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