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Ozisik, O.

Publications and source records attributed to Ozisik, O..

2 recordsLinked to original sources

A Scoring System to Evaluate the Impact of SNPs in a Path Related Context to Study Behçet’s Disease Aetiology in Japanese Population

MotivationGenome-wide association study (GWAS) is a powerful method that can provide a list of single nucleotide polymorphisms (SNPs) that are significantly related to the pathogenesis of a disease. Even though in Mendelian diseases strong associations can be found for certain SNPs, in most of the complex diseases only modest associations can be identified from the GWAS. Therefore, the main challenge in such studies is to understand how multiple SNPs that have modest association with the phenotype interact and contribute to its aetiology. This can only be done via pathway based analysis of modestly associated SNPs and the genes that are affected by these changes.\n\nResultsIn this study, we propose DAPath, a Disease Associated Path analyzer tool for discovering signaling paths and the pathways that contain these paths which are subjected to cumulative impact of modestly associated variants. We applied our proposed method on Behcets disease (BD) GWAS dataset from Japanese population. Antigen Processing and Presentation pathway is ranked first with 16 highly affected paths. Th17 cell differentiation, Natural killer cell mediated cytotoxicity, Jak-STAT signaling, and Circadian rhythm pathways are also found to be containing highly affected paths.\n\nAvailabilityThe proposed method is available as a Cytoscape plug-in through https://github.com/ozanozisik/DAPath

bioinformatics

pathfindR: An R Package for Pathway Enrichment Analysis Utilizing Active Subnetworks

SummaryPathfindR is a tool for pathway enrichment analysis utilizing active subnetworks. It identifies gene sets that form active subnetworks in a protein-protein interaction network using a list of genes provided by the user. It then performs pathway enrichment analyses on the identified gene sets. Further, using the R package pathview, it maps the user data on the enriched pathways and renders pathway diagrams with the mapped genes. Because many of the enriched pathways are usually biologically related, pathfindR also offers functionality to cluster these pathways and identify representative pathways in the clusters. PathfindR is built as a stand-alone package but it can easily be integrated with other tools, such as differential expression/methylation analysis tools, for building fully automated pipelines. In this article, an overview of pathfindR is provided and an example application on a rheumatoid arthritis dataset is presented and discussed.\n\nAvailabilityThe package is freely available under MIT license at: https://github.com/egeulgen/pathfindR

bioinformatics