review
Bevorzugte PräsentationsartVortrag
TitelaRgus: a versatile tool for variant visualization and advanced prediction score modeling in inherited metabolic diseases
Einleitung

The increasing application of high-throughput sequencing techniques for diagnosis of inherited metabolic diseases (IMDs) generates an expanding number of variants of uncertain significance and novel candidate genes. For many IMDs, no biochemical markers exist or in-vitro variant assessment is intricate. A variety of scoring algorithms and tools have been developed for in-silico prediction of functional variant effects but the majority of these data are abstract and hardly accessible without advanced bioinformatic understanding. We therefore developed the user-friendly online tool aRgus for exploitation and visualization of complex genetic and predictive information.

Patient/en und Methoden

aRgus is a stand-alone R/shiny web server application for compilation and visualization of multilevel gene, protein, variant, and in-silico prediction data from the publicly available databases ENSEMBL, dbNSFP, gnomAD, Simple ClinVar, and UniProt.

Ergebnisse

aRgus automatically determines the canonical transcript based on the user-supplied HGNC gene symbol and gathers all relevant data. The user can choose from a panel of six visualizations: 1.) unspliced transcript plot; 2.) protein plot; 3.) and 4.) the mutational constraint plots of pathogenic and likely pathogenic ClinVar variants, as well as tolerated gnomAD variants, respectively; 5.) a polynomial regression model with position-coded heatmap depiction of all annotated prediction score values; and 6) groupwise statistical comparison of scores as violin plots. An interactive table shows the retrieved ClinVar variants and annotated non-synonymous single nucleotide variants with color-coded prediction score values.

Schlussfolgerung/Diskussion

aRgus enables gene- and position-specific prediction score modeling for assessment of proteins and identification of regions susceptible to missense variation up to single amino acid resolution. In previous studies on metabolic conditions such as mevalonic aciduria, SSADH deficiency, and LARS1 deficiency, we could show that the aRgus workflow represents a powerful tool within the scope of enhanced variant interpretation in IMDs. It can be applied for interpretation of new variants in well-described IMDs as well as variants in poorly described genes or candidate genes associated with suspected IMDs. Additionally, aRgus can be used for the design of in-vitro experiments including knockout models due to the facilitated identification of protein regions with putative functional importance.

review