Abstract
BackgroundGenetic disposition is considered critical for identifying subjects at high risk for disease development. Investigating disease-causing and high and low expressed genes can support finding the root causes of uncertainties in patient care. However, independent and timely high-throughput next-generation sequencing data analysis is still a challenge for non-computational biologists and geneticists.ResultsIn this manuscript, we present a findable, accessible, interactive, and reusable (FAIR) bioinformatics platform, i.e., GVViZ (visualizing genes with disease-causing variants). GVViZ is a user-friendly, cross-platform, and database application for RNA-seq-driven variable and complex gene-disease data annotation and expression analysis with a dynamic heat map visualization. GVViZ has the potential to find patterns across millions of features and extract actionable information, which can support the early detection of complex disorders and the development of new therapies for personalized patient care. The execution of GVViZ is based on a set of simple instructions that users without a computational background can follow to design and perform customized data analysis. It can assimilate patients’ transcriptomics data with the public, proprietary, and our in-house developed gene-disease databases to query, easily explore, and access information on gene annotation and classified disease phenotypes with greater visibility and customization. To test its performance and understand the clinical and scientific impact of GVViZ, we present GVViZ analysis for different chronic diseases and conditions, including Alzheimer’s disease, arthritis, asthma, diabetes mellitus, heart failure, hypertension, obesity, osteoporosis, and multiple cancer disorders. The results are visualized using GVViZ and can be exported as image (PNF/TIFF) and text (CSV) files that include gene names, Ensembl (ENSG) IDs, quantified abundances, expressed transcript lengths, and annotated oncology and non-oncology diseases.ConclusionsWe emphasize that automated and interactive visualization should be an indispensable component of modern RNA-seq analysis, which is currently not the case. However, experts in clinics and researchers in life sciences can use GVViZ to visualize and interpret the transcriptomics data, making it a powerful tool to study the dynamics of gene expression and regulation. Furthermore, with successful deployment in clinical settings, GVViZ has the potential to enable high-throughput correlations between patient diagnoses based on clinical and transcriptomics data.
Highlights
Over the past few years, genomic sequencing technologies have improved the clinical diagnosis of genetic disorders and continue to expand the potential of basic sciences in developing insights into human genetic variations and their biological consequences
Experts in clinics and researchers in life sciences can use Visualizing genes with disease-causing variants (GVViZ) to visualize and interpret the transcriptomics data, making it a powerful tool to study the dynamics of gene expression and regulation
To advance our clinical genomics and precision medicine study, we modeled and implemented an annotated disease-gene-variants database that includes but is not limited to data collected from several genomics databases worldwide [7], including PAS [14, 15], ClinVar [16], GeneCards [17], MalaCard [18], DISEASES [19], HGMD [20], Disease Ontology [21], DiseaseEnhancer [22], DisGeNET [23], eDGAR [24], GTR [25], OMIM [26], miR2Disease [27], DNetDB [28], GTR, CNVD, Ensembl, GenCode, Novoseek, Swiss-Prot, LncRNADisease, Orphanet, and Catalogue Of Somatic Mutations In Cancer (COSMIC) [29]
Summary
Over the past few years, genomic sequencing technologies have improved the clinical diagnosis of genetic disorders and continue to expand the potential of basic sciences in developing insights into human genetic variations and their biological consequences. Gene expression analysis is a widely adopted method to identify abnormalities in normal function and physiologic regulation [1]. It supports expression profiling and transcriptomic analyses to identify, measure, and compare genes and transcripts in multiple conditions and in different tissues and individuals. Microarrays are based on traditional microarray platforms for transcriptional profiling that quantify a set of predetermined whole transcriptome sequences [3], while RNA-seq identifies, characterizes, and quantifies differentially modulated transcriptomes [4]. Due to recent advancements in next-generation sequencing (NGS) technologies and the development of new bioinformatics applications, RNA-seq has become the most widely used method for gene expression analysis [5]. Genetic disposition is considered critical for identifying subjects at high risk for disease development. Independent and timely high-throughput next-generation sequencing data analysis is still a challenge for non-computational biologists and geneticists
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
