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.

Keywords: Annotation; Disease; Expression; Gene; Heat map; RNA-seq.

Fig. 1

GVViZ workflow design. Overall tasks include the following: (1) database connection, (2) data selection, (3) gene selection, (4) querying database, (5) heat map customization, (6) heat map visualization, and (7) exporting results

Fig. 2

GVViZ graphical user interface. Sequence of screenshots explaining the overall interactive interfaces

Fig. 3

RNA-seq data processing pipeline. We use FastQC for quality checking, Trimmomatic to remove adapters and low-quality sequences, SAMtools to sort and index sequences, MarkDuplicates to remove duplicates, CollectInsertSizeMetrics to compute the size distribution and read orientation of paired-end libraries, HISAT with Bowtie2 to align the sequences to the human reference genome, and RSEM to quantify and identify differentially expressed genes by aligning reads to reference de novo transcriptome assemblies. Furthermore, our RNA-seq pipeline utilizes an in-house developed software application to automatically parse the outcome files of the pipelines and upload the results into a modeled relational database, which are then used by GVViZ for data annotation, analysis, and visualization

Fig. 4

GVViZ gene-disease annotations, expression analyses, and heat map visualizations of different chronic diseases and conditions. Genes identified for A Alzheimer’s disease, B arthritis, C asthma, D diabetes mellitus, E obesity, F osteoporosis, G heart failure, H hypertension, and I multiple cancer disorders