Biology subjects

Uzun, E. D.

Publications and source records attributed to Uzun, E. D..

3 recordsLinked to original sources

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/

bioinformatics↗

bioRxiv2021

LYRUS: A Machine Learning Model for Predicting the Pathogenicity of Missense Variants

Single amino acid variations (SAVs) are a primary contributor to variations in the human genome. Identifying pathogenic SAVs can aid in the diagnosis and understanding of the genetic architecture of complex diseases, such as cancer. Most approaches for predicting the functional effects or pathogenicity of SAVs rely on either sequence or structural information. Nevertheless, previous analyses have shown that methods that depend on only sequence or structural information may have limited accuracy. Recently, researchers have attempted to increase the accuracy of their predictions by incorporating protein dynamics into pathogenicity predictions. This study presents < Lai Yang Rubenstein Uzun Sarkar > (LYRUS), a machine learning method that uses an XGBoost classifier selected by TPOT to predict the pathogenicity of SAVs. LYRUS incorporates five sequence-based features, six structure-based features, and four dynamics-based features. Uniquely, LYRUS includes a newly-proposed sequence co-evolution feature called variation number. LYRUSs performance was evaluated using a dataset that contains 4,363 protein structures corresponding to 20,307 SAVs based on human genetic variant data from the ClinVar database. Based on our dataset, the LYRUS classifier has a higher accuracy, specificity, F-measure, and Matthews correlation coefficient (MCC) than alternative methods including PolyPhen2, PROVEAN, SIFT, Rhapsody, EVMutation, MutationAssessor, SuSPect, FATHMM, and MVP. Variation numbers used within LYRUS differ greatly between pathogenic and neutral SAVs, and have a high feature weight in the XGBoost classifier employed by this method. Applications of the method to PTEN and TP53 further corroborate LYRUSs strong performance. LYRUS is freely available and the source code can be found at https://github.com/jiaying2508/LYRUS.

bioinformatics↗

bioRxiv2021

Somatic Mutations in Collagens are Associated with a Distinct Tumor Environment and Overall Survival in Gastric Cancer

BackgroundGastric cancer is a heterogeneous disease with poorly understood genetic and microenvironmental factors. Mutations in collagen genes are associated with genetic diseases that compromise tissue integrity, but their role in tumor progression has not been extensively reported. Aberrant collagen expression has been long associated with malignant tumor growth, invasion, chemoresistance, and patient outcomes. We hypothesized that somatic mutations in collagens could functionally alter the tumor extracellular matrix. MethodsWe used publicly available datasets including The Tumor Cancer Genome Atlas (TCGA) to interrogate somatic mutations in collagens in stomach adenocarcinomas. To demonstrate that collagens were significantly mutated above background mutation rates, we used a moderated Kolmogorov-Smirnov test along with combination analysis with a bootstrap approach to define the background accounting for mutation rates. Association between mutations and clinicopathological features was evaluated by Fisher or chi-squared tests. Association with overall survival was assessed by Kaplan-Meier and the Cox-Proportional Hazards Model. Gene Set Enrichment Analysis was used to interrogate pathways. Immunohistochemistry and in situ hybridization tested expression of COL7A1 in stomach tumors. ResultsIn stomach adenocarcinomas, we identified individual collagen genes and sets of collagen genes harboring somatic mutations at a high frequency compared to background in both microsatellite stable, and microsatellite instable tumors in TCGA. Many of the missense mutations resemble the same types of loss of function mutations in collagenopathies that disrupt tissue formation and destabilize cells providing guidance to interpret the somatic mutations. We identified combinations of somatic mutations in collagens associated with overall survival, with a distinctive tumor microenvironment marked by lower matrisome expression and immune cell signatures. Truncation mutations were strongly associated with improved outcomes suggesting that loss of expression of secreted collagens impact tumor progression and treatment response. Germline collagenopathy variants guided interpretation of impactful somatic mutations on tumors. ConclusionsThese observations highlight that many collagens, expressed in non-physiologically relevant conditions in tumors, harbor impactful somatic mutations in tumors, suggesting new approaches for classification and therapy development in stomach cancer. In sum, these findings demonstrate how classification of tumors by collagen mutations identified strong links between specific genotypes and the tumor environment. Research HighlightsCollagen mutations are prevalent in stomach cancer Collagen somatic missense mutations resemble collagenopathy mutations Collagen mutations associate with overall survival in stomach cancer Tumors with collagen mutations have distinct molecular pathways and tumor microenvironments

cancer biology↗