Biology subjects

Uzun, A.

Publications and source records attributed to Uzun, A..

7 recordsLinked to original sources

bioRxiv2026

OncoContour: An Interactive Platform for Geographic Visualization and Demographic Analysis of Cancer Incidence.

Cancer incidence varies substantially across geographic regions and demographic groups, yet translating large-scale surveillance datasets into accessible, interpretable visualizations remains a challenge for researchers and public health professionals without computational expertise. We developed OncoContour, an interactive web-based platform that enables geographic visualization and demographic analysis of cancer incidence data through a browser-accessible interface. To demonstrate its capabilities, we analyzed publicly available cancer incidence data from the United States Cancer Statistics database via CDC WONDER, covering five major cancer types across four northeastern U.S. metropolitan statistical areas from 2017 through 2022, supplemented by demographic data from the U.S. Census Bureau American Community Survey. OncoContour integrates population distribution heatmaps, per-capita cancer incidence heatmaps, interactive multi-city temporal trend charts, structured cancer data tables, and demographic visualizations covering race, ethnicity, age, and sex distributions into a single dynamically generated HTML report. The platform is implemented in Python using Flask, Folium, Plotly, and Matplotlib, and is containerized using Docker for reproducible local deployment. Across all four metropolitan areas, breast and prostate cancers accounted for the highest incidence counts over the study period, while a decline in reported cases observed in 2020 is consistent with documented disruptions to cancer screening during the COVID-19 pandemic. By integrating geospatial mapping, temporal analysis, and demographic visualization within a unified, no-code interface, OncoContour aims to support cancer surveillance, epidemiological investigation, and targeted public health planning. OncoContour is freely available at https://github.com/alperuzun/oncocontour_docker.

bioinformatics↗

bioRxiv2024

A comparative network analysis of high-risk MYCN amplified and high-risk MYCN non-amplified neuroblastoma patients

Neuroblastoma is the most common extracranial solid tumor in children. Under contemporary staging guidelines, amplification of MYCN, defined as possessing 10 or more copies of the MYCN gene, is sufficient to classify a patient as high-risk. We aim to investigate the complex genetic landscape of neuroblastoma through a network biology perspective, focusing on complex protein-protein interaction (PPI) networks. Reanalyzing three historical neuroblastoma RNA-Seq datasets, we conducted a comparative network analysis of high-risk MYCN amplified patients and high-risk MYCN non-amplified patients to identify PPI networks that serve as predictive biomarkers for developing high-risk disease. We used R scripts to extract the top 100 most highly expressed genes from each dataset and then analyzed the expression profiles using Proteinarium, a network analysis tool that identifies clusters of patients with shared PPI networks. Statistically significant clusters were annotated and analyzed for network similarity between datasets. Our study isolated four significant clusters with majority high-risk MYCN amplified patients. We annotated the genes unique to high-risk MYCN amplified patients and identified several potential network biomarkers for this particular genotype of neuroblastoma, including ribosome biogenesis (RPL7A, RPL30, RPL35, RPS8), heat shock protein (HSPA8, HSP90AA1, HSP90AB1), matrix metallopeptidase (MMP2, MMP9, MMP13), and collagen (COL2A1, COL5A3, COL6A2, COL11A1, COL16A1) gene networks. The results of this study provide potential therapeutic targets for high-risk MYCN amplified neuroblastoma.

bioinformatics↗

bioRxiv2024

THRESHOLD: A Comprehensive Transcriptomic Analysis Tool for Evaluating Gene Saturation and Impact in Disease Progression

Gene expression studies serve as a foundational tool in molecular biology, providing insights into developmental, physiological, and pathological processes. Variations in gene expression can indicate disease states, which are vital in understanding disease progression, subtype manifestations, and identifying therapeutic targets based on detailed expression patterns. To effectively investigate gene expression patterns, especially in large datasets, a robust and precise analysis tool is crucial. In response to this critical analytical need, we developed THRESHOLD, a novel tool that goes beyond traditional gene expression analysis by introducing the concept of gene saturation. Unlike conventional methods that focus on absolute expression levels or binary differential expression, THRESHOLD quantifies the consistency of gene expression across patients, revealing co-regulation patterns that may otherwise be overlooked. This novel metric offers a unique perspective on gene expression patterns by highlighting consistent trends across patient samples, which are critical for understanding disease mechanisms and stratifying patients based on molecular signatures. The tool offers several features, including user-defined parameters, statistical comparisons, and interactive data visualization. THRESHOLD has uncovered compelling insights into disease progression using TCGA Cancer Datasets. For instance, bladder urothelial carcinoma demonstrated increasing upregulated gene saturation in progressive cancer stages (p < 0.00001). Moreover, THRESHOLD identified heightened gene saturation in patients with earlier onset of prostate adenocarcinoma (p < 0.0001) and revealed a critical fusion transcript, SLC45A2-AMACR, implicated in prostate adenocarcinoma progression, recurrence, and metastasis. Additionally, novel biomarkers and potential candidates for drug therapies were identified through protein-protein interaction networks and functional analyses of saturation data in colon adenocarcinoma and breast invasive carcinoma. Collectively, THRESHOLD advances our understanding of patient stratification and molecular signatures by offering a more detailed view of gene expression dynamics. The THRESHOLD tool is publicly available at: https://github.com/alperuzun/THRESHOLD.

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bioRxiv2023

Variant Graph Craft (VGC): A Comprehensive Tool for Analyzing Genetic Variation and Identifying Disease-Causing Variants.

The Variant Call Format (VCF) file is providing a structured and comprehensive text file that contains essential information about variant positions in the genome, as well as other critical details, such as alleles, genotype calls, and quality scores. Due to its rich data and format, the VCF file has become an increasingly popular resource for researchers and clinicians alike, enabling them to interpret and understand genomic variation data. However, analyzing and visualizing these files poses significant challenges, demanding access to diverse resources and a robust set of features for in-depth exploration. We introduce Variant Graph Craft (VGC), a VCF file visualization and analysis tool offering a wide range of features for exploring genetic variations, including extraction of variant data, intuitive visualization of variants, and the provision of a graphical representation of samples, complete with genotype information. Furthermore, VGC seamlessly integrates with external resources to offer valuable insights into gene function and variant frequencies in sample data. VGC offers gene function and pathway information from Molecular Signatures Database (MSigDB) for GO terms, as well as KEGG, Biocarta, Pathway Interaction Database, and Reactome. Additionally, the tool also provides a dynamic link to gnomAD for variant information, and includes ClinVar data for pathogenic variant information. VGC operates locally, assuring users of data security and privacy by eliminating the need for cloud-based VCF uploads. It supports the Human Genome Assembly Hg37, ensuring compatibility with a wide range of data sets. With its versatility, VGC accommodates various approaches exploring genetic variation data, and can be tailored to the specific needs of the user by using optional phenotype input data. In conclusion, VGC is a useful resource for exploring genetic variation in a secure and user-friendly environment. With its user-tailored set of features, this tool enables researchers and clinicians to easily explore and understand genomic variation data in a comprehensive and accessible manner. From identifying specific genetic mutations to analyzing patterns of variation across the genome, the VCF file visualization and analysis tool is an essential tool for those working in the field of genomics. VGC is freely available at https://sites.brown.edu/gmilab/variantgraphcraft/

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bioRxiv2022

MMR Deficiency Defines Distinct Molecular Subtype of Breast Cancer with Unique Proteomic Networks and Variable Clinical Significance

Mismatch repair (MMR) alterations are important prognostic and predictive biomarkers in a variety of cancer subtypes including colorectal and endometrial. However, in breast cancer (BC), the distinction and clinical significance of MMR is largely unknown. This may be due in part to the fact that genetic alterations in MMR genes are rare, and only seen to occur in around 3% of BCs. In the present study we analyzed TCGA data using a multi-sample protein-protein interactions (PPI) analysis tool, Proteinarium, and showed a distinct separation in the MMR deficient and intact specific networks. MMR deficient tumor specific networks have a highly connected cluster of histone genes, identified by unique PPI. We also found that distribution of MMR deficient breast cancer is more prevalent in HER2-enriched and triple-negative (TN) BC subtypes compared to luminal BCs. Poorer survival was seen in patients with HER2-enriched BCs with MMR deficiency, whereas an improved survival was seen in TNBCs with MMR deficiency. We recommend defining MMR deficient breast cancer by next generation sequencing (NGS) when any somatic mutation is detected in one of the 7 MMR genes found in our study. Our recommendations include labeling patients with variants of undetermined significance (VUS) as MMR deficient supported by findings from distinct clusters of patients based on our network analysis. MMR may have a role in guiding the use of immunotherapy for both TN as well as HER2-enriched BC.

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bioRxiv2019

VIVA (VIsualization of VAriants): A VCF file visualization tool

The volume and pace of data accumulation from high-throughput sequencing studies have been amplified by recent rapid technological advances in biological sciences. Visualization of genomic data is essential for quality control, exploration, and interpretation. Here, we describe a user-friendly visualization tool for variant call format (VCF) files with which the users can interactively evaluate and share genomic data, as well as create publication quality graphics.

bioinformatics↗

bioRxiv2019

Proteinarium: Multi-Sample Protein-Protein Interaction Visualization and Analysis Tool

BackgroundData analysis has become crucial in the post genomic era where the accumulation of genomic information is mounting exponentially. Analyzing protein-protein interactions in the context of the interactome is a powerful approach to understanding disease phenotypes. ResultsWe describe Proteinarium, a multi-sample protein-protein interaction network analysis and visualization tool. Proteinarium can be used to analyze data for samples with dichotomous phenotypes, multiple samples from a single phenotype or a single sample. Then, by similarity clustering, the network-based relations of samples are identified and clusters of related samples are presented as a dendrogram. Each branch of the dendrogram is built based on network similarities of the samples. The protein-protein interaction networks can be analyzed and visualized on any branch of the dendrogram. Proteinariums input can be derived from transcriptome analysis, whole exome sequencing data or any high-throughput screening approach. Its strength lies in use of gene lists for each sample as a distinct input which are further analyzed through protein interaction analyses. Proteinarium output includes the gene lists of visualized networks and PPI interaction files where users can analyze the network(s) on other platforms such as Cytoscape. In addition, since the dendrogram is written in Newick tree format, users can visualize it in other software platforms like Dendroscope, ITOL. ConclusionsProteinarium, through the analysis and visualization of PPI networks, allows researchers to make important observations on high throughput data for a variety of research questions. Proteinarium identifies significant clusters of patients based on their shared network similarity for the disease of interest and the associated genes. Proteinarium is a command-line tool written in Java with no external dependencies and it is freely available at https://github.com/Armanious/Proteinarium.

bioinformatics↗