9, none of the molecules reported as possessing either binding or antimicrobial

Here, we extend this pipeline to identify potential novel drug targets among the predicted candidate genes by associating drug information extracted from publicly available drug databases. The three databases INK 128 臨床試験 sourced in this study were DrugBank, the Pharmacogenomics Knowledgebase and the Therapeutic Target Database. The feasibility of this approach is again illustrated for the seven complex dis eases investigated by the WTCCC. This study shows that it is possible to identify therapeutics for treatment of specific complex diseases from genetic loci via the Gen trepid candidate gene prediction tool. Thus, in combina tion with drug target information, candidate gene prediction systems can be utilized as drug discovery tools to identify therapeutics which may be repositioned as novel treatments for complex diseases.<br><br> Methods We implemented a computational workflow to enable repositioning of drugs by using Gentrepid as a bioinfor matic candidate gene discovery platform, with drug data sourced from online databases. The two KU-57788 臨床試験 data sets integrated were, 1. A candidate gene data set obtained by integration of genotype phenotype data from the WTCCC GWAS study on seven complex phenotypes, with bioinformatic data on structural domains and systems biology, identify ing proteins that share common features, or participate in the same complex or pathway , 2. A drug gene target association data set obtained from three drug databases namely TTD, DrugBank and PharmGKB. Candidate gene data set In previous work, we predicted a total of 1,497 candidate genes for seven complex diseases by careful reanalysis of the WTCCC GWAS data using the Gentrepid candi date gene prediction system.<br><br> In the original analysis, a highly stringent significance threshold was used in an attempt to correct for multiple testing. This conservative statistical approach, Linsitinib 分子量 combined with the selection of the nearest neighboring gene to the significant SNP, resulted in identification of only a small number of loci associated with each phenotype, with modest cumulative heritabil ity. We specifically addressed these two issues in our reana lysis of this noisy data by Considering a series of four thresholds of decreasing stringency, starting with the highly significant threshold used in the original study, and decreasing to weakly significant. This resulted in a series of four SNP sets containing up to 1064 SNPs being considered for each phenotype.<br><br> The num ber of loci and SNPs considered in the four data sets for each phenotype is shown in Table S1. Creating six different search spaces around each SNP based locus, three of fixed widths and three proxi mity based, for analysis by our candidate gene predic tion system. Thus, for each of the seven phenotypes, twenty four search spaces were constructed, using four SNP signifi cance thresholds to obtain the loci, and six gene selection methods to construct the gene search spaces. In total, 168 search spaces ranging in size from 2 to 4,431 genes were analyzed. Gentrepid uses two modules, Common Pathway Scan ning and Common Module Profiling to make candidate gene predictions.