Biology subjects

Sezerman, O. U.

Publications and source records attributed to Sezerman, O. U..

3 recordsLinked to original sources

bioRxiv2018

A harmonized meta-knowledgebase of clinical interpretations of cancer genomic variants

Precision oncology relies on the accurate discovery and interpretation of genomic variants to enable individualized diagnosis, prognosis, and therapy selection. We found that knowledgebases containing clinical interpretations of somatic cancer variants are highly disparate in interpretation content, structure, and supporting primary literature, impeding consensus when evaluating variants and their relevance in a clinical setting. With the cooperation of experts of the Global Alliance for Genomics and Health (GA4GH) and six prominent cancer variant knowledgebases, we developed a framework for aggregating and harmonizing variant interpretations to produce a meta-knowledgebase of 12,856 aggregate interpretations covering 3,437 unique variants in 415 genes, 357 diseases, and 791 drugs. We demonstrated large gains in overlap between resources across variants, diseases, and drugs as a result of this harmonization. We subsequently demonstrated improved matching between a patient cohort and harmonized interpretations of potential clinical significance, observing an increase from an average of 33% per individual knowledgebase to 56% in aggregate. Our analyses illuminate the need for open, interoperable sharing of variant interpretation data. We also provide an open and freely available web interface (search.cancervariants.org) for exploring the harmonized interpretations from these six knowledgebases.

bioinformatics↗

bioRxiv2018

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↗

bioRxiv2018

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↗