aRgus: multilevel visualization of non-synonymous single nucleotide variants & advanced pathogenicity score modeling for genetic vulnerability assessment

• Verfasst von: Schröter, Julian [VerfasserIn]
• Verfasst von: Dattner, Tal [VerfasserIn]
• Verfasst von: Hüllein, Jennifer [VerfasserIn]
• Verfasst von: Jayme, Alejandra [VerfasserIn]
• Verfasst von: Heuveline, Vincent [VerfasserIn]
• Verfasst von: Hoffmann, Georg F. [VerfasserIn]
• Verfasst von: Kölker, Stefan [VerfasserIn]
• Verfasst von: Lenz, Dominic [VerfasserIn]
• Verfasst von: Opladen, Thomas [VerfasserIn]
• Verfasst von: Popp, Bernt [VerfasserIn]
• Verfasst von: Schaaf, Christian P. [VerfasserIn]
• Verfasst von: Staufner, Christian [VerfasserIn]
• Verfasst von: Syrbe, Steffen [VerfasserIn]
• Verfasst von: Uhrig, Sebastian [VerfasserIn]
• Verfasst von: Hübschmann, Daniel [VerfasserIn]
• Verfasst von: Brennenstuhl, Heiko [VerfasserIn]
• Titel: aRgus: multilevel visualization of non-synonymous single nucleotide variants & advanced pathogenicity score modeling for genetic vulnerability assessment
• Verf.angabe: Julian Schroeter, Tal Dattner, Jennifer Huellein, Alejandra Jayme, Vincent Heuveline, Georg F. Hoffmann, Stefan Koelker, Dominic Lenz, Thomas Opladen, Bernt Popp, Christian P. Schaaf, Christian Staufner, Steffen Syrbe, Sebastian Uhrig, Daniel Huebschmann, Heiko Brennenstuhl
• Jahr: 2023
• Umfang: 7 S.
• Fussnoten: Gesehen am 11.04.2023
• Titel Quelle: Enthalten in: Computational and structural biotechnology journal
• Ort Quelle: Gotenburg : Research Network of Computational and Structural Biotechnology (RNCSB), 2012
• Jahr Quelle: 2023
• Band/Heft Quelle: 21(2023), Seite 1077-1083
• ISSN Quelle: 2001-0370
• DOI: doi:10.1016/j.csbj.2023.01.027
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Computational genetics
• Sach-SW: Pathogenicity scores
• Sach-SW: Variant assessment
• Sach-SW: Variant effect prediction
• K10plus-PPN: 184200820X

Abstract

The widespread use of high-throughput sequencing techniques is leading to a rapidly increasing number of disease-associated variants of unknown significance and candidate genes. Integration of knowledge concerning their genetic, protein as well as functional and conservational aspects is necessary for an exhaustive assessment of their relevance and for prioritization of further clinical and functional studies investigating their role in human disease. To collect the necessary information, a multitude of different databases has to be accessed and data extraction from the original sources commonly is not user-friendly and requires advanced bioinformatics skills. This leads to a decreased data accessibility for a relevant number of potential users such as clinicians, geneticist, and clinical researchers. Here, we present aRgus (https://argus.urz.uniheidelberg.de/), a standalone webtool for simple extraction and intuitive visualization of multi-layered gene, protein, variant, and variant effect prediction data. aRgus provides interactive exploitation of these data within seconds for any known gene of the human genome. In contrast to existing online platforms for compilation of variant data, aRgus complements visualization of chromosomal exon-intron structure and protein domain annotation with ClinVar and gnomAD variant distributions as well as position-specific variant effect prediction score modeling. aRgus thereby enables timely assessment of protein regions vulnerable to variation with single amino acid resolution and provides numerous applications in variant and protein domain interpretation as well as in the design of in vitro experiments. (c) 2023 Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).