Biology subjects

Sezerman, O. U.

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

7 recordsLinked to original sources

bioRxiv2025

Transcriptomic Analysis Of The Relationship Between Pancreatic Ductal Adenocarcinoma Stromal Subtypes And Neural Invasion

We investigated the association between pancreatic ductal adenocarcinoma (PDAC) subtypes and neural invasion (NI) status in order to assess what factors influence the inter-tumor heterogeneity of NI. We built single-sample classifiers for prominent subtype schemes using multiple approaches. We predicted the subtype labels of samples with known NI status using the classifiers based on pairwise gene expression comparisons, which were consistently the best-performing models. These classifiers were made available in a Shiny app (https://salihaey.shinyapps.io/pcsubtypes/). Stromal subtypes were found to be most significantly associated with NI, with activated stroma being enriched in neuro-invasive samples. We compared the results of differential expression and gene set enrichment analyses between NI and stromal subtypes from both bulk and single-cell RNA-seq data, which revealed a prominent role of myCAFs and common signatures between activated stroma and NI (e.g., ECM remodeling and TGF{beta} signaling) as well as signatures that changed in divergent directions for activated stroma and NI (e.g., iCAF markers). Our study presents support for the clinical implications of PDAC stromal subtypes by revealing their association with NI incidence, while providing biological insights into the interactions between diverse genetic programs that influence NI emergence.

cancer biology↗

bioRxiv2023

Collaborative network analysis for the interpretation of transcriptomics data in rare diseases, an application to Huntington's disease

BackgroundRare diseases may affect the quality of life of patients and in some cases be life-threatening. Therapeutic opportunities are often limited, in part because of the lack of understanding of the molecular mechanisms that can cause disease. This can be ascribed to the low prevalence of rare diseases and therefore the lower sample sizes available for research. A way to overcome this is to integrate experimental rare disease data with prior knowledge using network-based methods. Taking this one step further, we hypothesized that combining and analyzing the results from multiple network-based methods could provide data-driven hypotheses of pathogenicity mechanisms from multiple perspectives. ResultsWe analyzed a Huntingtons disease (HD) transcriptomics dataset using six network-based methods in a collaborative way. These methods either inherently reported enriched annotation terms or their results were fed into enrichment analyses. The resulting significantly enriched Reactome pathways were then summarized using the ontological hierarchy which allowed the integration and interpretation of outputs from multiple methods. Among the resulting enriched pathways, there are pathways that have been shown previously to be involved in HD and pathways whose direct contribution to disease pathogenesis remains unclear and requires further investigation. ConclusionsIn summary, our study shows that collaborative network analysis approaches are well-suited to study rare diseases, as they provide hypotheses for pathogenic mechanisms from multiple perspectives. Applying different methods to the same case study can uncover different disease mechanisms that would not be apparent with the application of a single method.

bioinformatics↗

bioRxiv2022

predatoR: an R package for network-based mutation impact prediction

MotivationClassification of a mutation is important for variant prioritization and diagnostics. However, it is still a challenging task that many mutations are classified as variant of unknown significance. Therefore, in silico tools are required for classifying variants with unknown significance. Over the past decades, several computational methods have been developed but they usually have limited accuracy and high false-positive rates. To address these needs, we developed a new machine learning-based method for calculating the impact of a mutation by converting protein structures to networks and using network properties of the mutated site. ResultsHere, we propose a novel machine learning-based method, predatoR, for mutation impact prediction. The model was trained using both VariBench and ClinVar datasets and benchmarked against currently available methods using the Missense3D datasets. predatoR outperformed 32 different mutation impact prediction methods with an AUROC value of 0.941. AvailabilitypredatoR tool is available as an open-source R package at GitHub (https://github.com/berkgurdamar/predatoR). Contactberkgurdamar@gmail.com

bioinformatics↗

bioRxiv2022

Determination of secretion type associated motifs from pathogenic protein sequences

Currently, potential vaccine candidates are determined using protein localization predictors to identify surface or secreted protein sequences. The pathogenic bacterial sequences constitute only a tiny percentage of this database. They search for general motifs found in bacteria and miss out on pathogen-specific motifs, which is crucial for identifying vaccine candidates. We developed a motif search algorithm derived from pathogenic bacterial sequences in this work. Using this method, we identified secretion motifs of pathogenic bacteria for six different secretion pathways and non-specific secretion motifs. To this end, we established a database of immunogenic sequences from patented vaccine sequences, AntigenDb, and PUB-MED. The motifs are filtered out according to their presence in a cytoplasmic dataset to keep motifs that are present only in secreted sequences. These sequences are validated on newly identified novel vaccine candidates from 20 pathogenic bacterial immunoproteomics data.

bioinformatics↗

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↗