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1.  Detection of Regulatory SNPs in Human Genome Using ChIP-seq ENCODE Data 
PLoS ONE  2013;8(10):e78833.
A vast amount of SNPs derived from genome-wide association studies are represented by non-coding ones, therefore exacerbating the need for effective identification of regulatory SNPs (rSNPs) among them. However, this task remains challenging since the regulatory part of the human genome is annotated much poorly as opposed to coding regions. Here we describe an approach aggregating the whole set of ENCODE ChIP-seq data in order to search for rSNPs, and provide the experimental evidence of its efficiency. Its algorithm is based on the assumption that the enrichment of a genomic region with transcription factor binding loci (ChIP-seq peaks) indicates its regulatory function, and thereby SNPs located in this region are more likely to influence transcription regulation. To ensure that the approach preferably selects functionally meaningful SNPs, we performed enrichment analysis of several human SNP datasets associated with phenotypic manifestations. It was shown that all samples are significantly enriched with SNPs falling into the regions of multiple ChIP-seq peaks as compared with the randomly selected SNPs. For experimental verification, 40 SNPs falling into overlapping regions of at least 7 TF binding loci were selected from OMIM. The effect of SNPs on the binding of the DNA fragments containing them to the nuclear proteins from four human cell lines (HepG2, HeLaS3, HCT-116, and K562) has been tested by EMSA. A radical change in the binding pattern has been observed for 29 SNPs, besides, 6 more SNPs also demonstrated less pronounced changes. Taken together, the results demonstrate the effective way to search for potential rSNPs with the aid of ChIP-seq data provided by ENCODE project.
doi:10.1371/journal.pone.0078833
PMCID: PMC3812152  PMID: 24205329
2.  Ortho-Aminoazotoluene activates mouse Constitutive Androstane Receptor (mCAR) and increases expression of mCAR target genes 
2'-3-dimethyl-4-aminoazobenzene (ortho-aminoazotoluene, OAT) is an azo dye and a rodent carcinogen that has been evaluated by the International Agency for Research on Cancer (IARC) as a possible (class 2B) human carcinogen. Its mechanism of action remains unclear. We examined the role of the xenobiotic receptor Constitutive Androstane Receptor (CAR, NR1I3) as a mediator of the effects of OAT. We found that OAT increases mouse CAR (mCAR) transactivation in a dose-dependent manner. This effect is specific because another closely related azo dye, 3'-methyl-4-dimethyl-aminoazobenzene (3'MeDAB), did not activate mCAR. Real-time Q-PCR analysis in wild-type C57BL/6 mice revealed that OAT induces the hepatic mRNA expression of the following CAR target genes: Cyp2b10, Cyp2c29, Cyp3a11, Ugt1a1, Mrp4, Mrp2 and c-Myc. CAR-null (Car−/−) mice showed no increased expression of these genes following OAT treatment, demonstrating that CAR is required for their OAT dependent induction. The OAT-induced CAR-dependent increase of Cyp2b10 and c-Myc expression was confirmed by Western blotting. Immunohistochemistry analysis of wild-type and Car−/− livers showed that OAT did not acutely induce hepatocyte proliferation, but at much later time points showed an unexpected CAR-dependent proliferative response. These studies demonstrate that mCAR is an OAT xenosensor, and indicate that at least some of the biological effects of this compound are mediated by this nuclear receptor.
doi:10.1016/j.taap.2011.05.019
PMCID: PMC3148291  PMID: 21672546
Ortho-Aminoazotoluene (OAT); Constitutive Androstane Receptor (CAR); CYP450s; c-Myc; hepatocyte proliferation
3.  Effective transcription factor binding site prediction using a combination of optimization, a genetic algorithm and discriminant analysis to capture distant interactions 
BMC Bioinformatics  2007;8:481.
Background
Reliable transcription factor binding site (TFBS) prediction methods are essential for computer annotation of large amount of genome sequence data. However, current methods to predict TFBSs are hampered by the high false-positive rates that occur when only sequence conservation at the core binding-sites is considered.
Results
To improve this situation, we have quantified the performance of several Position Weight Matrix (PWM) algorithms, using exhaustive approaches to find their optimal length and position. We applied these approaches to bio-medically important TFBSs involved in the regulation of cell growth and proliferation as well as in inflammatory, immune, and antiviral responses (NF-κB, ISGF3, IRF1, STAT1), obesity and lipid metabolism (PPAR, SREBP, HNF4), regulation of the steroidogenic (SF-1) and cell cycle (E2F) genes expression. We have also gained extra specificity using a method, entitled SiteGA, which takes into account structural interactions within TFBS core and flanking regions, using a genetic algorithm (GA) with a discriminant function of locally positioned dinucleotide (LPD) frequencies.
To ensure a higher confidence in our approach, we applied resampling-jackknife and bootstrap tests for the comparison, it appears that, optimized PWM and SiteGA have shown similar recognition performances. Then we applied SiteGA and optimized PWMs (both separately and together) to sequences in the Eukaryotic Promoter Database (EPD). The resulting SiteGA recognition models can now be used to search sequences for BSs using the web tool, SiteGA.
Analysis of dependencies between close and distant LPDs revealed by SiteGA models has shown that the most significant correlations are between close LPDs, and are generally located in the core (footprint) region. A greater number of less significant correlations are mainly between distant LPDs, which spanned both core and flanking regions. When SiteGA and optimized PWM models were applied together, this substantially reduced false positives at least at higher stringencies.
Conclusion
Based on this analysis, SiteGA adds substantial specificity even to optimized PWMs and may be considered for large-scale genome analysis. It adds to the range of techniques available for TFBS prediction, and EPD analysis has led to a list of genes which appear to be regulated by the above TFs.
doi:10.1186/1471-2105-8-481
PMCID: PMC2265442  PMID: 18093302
4.  rSNP_Guide, a database system for analysis of transcription factor binding to DNA with variations: application to genome annotation 
Nucleic Acids Research  2003;31(1):118-121.
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.
PMCID: PMC165559  PMID: 12519962
5.  rSNP_Guide, a database system for analysis of transcription factor binding to target sequences: application to SNPs and site-directed mutations 
Nucleic Acids Research  2001;29(1):312-316.
rSNP_Guide is a novel curated database system for analysis of transcription factor (TF) binding to target sequences in regulatory gene regions altered by mutations. It accumulates experimental data on naturally occurring site variants in regulatory gene regions and site-directed mutations. This database system also contains the web tools for SNP analysis, i.e., active applet applying weight matrices to predict the regulatory site candidates altered by a mutation. The current version of the rSNP_Guide is supplemented by six sub-databases: (i) rSNP_DB, on DNA–protein interaction caused by mutation; (ii) SYSTEM, on experimental systems; (iii) rSNP_BIB, on citations to original publications; (iv) SAMPLES, on experimentally identified sequences of known regulatory sites; (v) MATRIX, on weight matrices of known TF sites; (vi) rSNP_Report, on characteristic examples of successful rSNP_Tools implementation. These databases are useful for the analysis of natural SNPs and site-directed mutations. The databases are available through the Web, http://wwwmgs.bionet.nsc.ru/mgs/systems/rsnp/.
PMCID: PMC29847  PMID: 11125123

Results 1-5 (5)