Advancing clinical genomics and precision medicine with GVViZ: FAIR bioinformatics platform for variable gene-disease annotation, visualization, and expression analysis

Abstract

Background Genetic 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. Results In 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. Conclusions We 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.

Genomics pipelines to investigate susceptibility in whole genome and exome sequenced data for variant discovery, annotation, prediction and genotyping

“…We need to implement personalized approaches to improve the traditional symptomdriven medical practice with disease-causal genetic variant discovery Ahmed et al (2021). Major challenges need to be addressed include but not limited to: developing diseasespecific cohorts based on patients' genomics profiles; searching for the underlying immunity genes, and common and the rare disease-causal variants, imputations, and haplotype resolutions; finding variant's agnostic of certain predetermined pathologieslike, similarities between disease states not classically considered related at a molecular level; and predicting genetic variants not only affecting targeted but other disorders in particular subjects, which can then be extended to the overall population.…”

RNA-seq driven expression and enrichment analysis to investigate CVD genes with associated phenotypes among high-risk heart failure patients

“…All computational results were stored in a designated database, using an in-house programmed command line data parser. The expression data were illustrated using the Gene Variant Visualization (GVViZ) environment, another bioinformatics application [ 37 ] developed in-house for efficient high-volume sequence data visualization.

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RNA‐seq‐driven expression analysis to investigate cardiovascular disease genes with associated phenotypes among atrial fibrillation patients

“…It reveals all genes annotated with their associated clinical AF phenotype using gene–disease association. 2 , 5 This expression analysis was expanded to visualize the classification of protein‐ and non‐coding genes in detail as gender‐ and race‐based. First, we looked across the AF‐annotated genes to identify protein‐ and non‐coding genes together and found 71 genes related to AF and relative diseases (Additional file 3 : Complete Gene List).…”

“… 9 This might accelerate our ability to leverage and extend the information contained within the original data and to model patient‐specific genomics and clinical data for significant transcriptional correlations, highlighting the association of genetic variants to clinical outcomes of treatment in AF and other CVD. 5 , 9 , 10…”