Journal Article

DKFZ-2023-00356

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png aRgus: Multilevel visualization of non-synonymous single nucleotide variants & advanced pathogenicity score modeling for genetic vulnerability assessment.

Schröter, J. ; Dattner, T. ; Hüllein, J. ; Jayme, A. ; Heuveline, V. ; Hoffmann, G. F. ; Kölker, S. ; Lenz, D. ; Opladen, T. ; Popp, B. ; Schaaf, C. P. ; Staufner, C. ; Syrbe, S. ; Uhrig, S. ; Hübschmann, D.DKFZ* ; Brennenstuhl, H.

2023
Research Network of Computational and Structural Biotechnology (RNCSB) Gotenburg

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Please use a persistent id in citations: doi:10.1016/j.csbj.2023.01.027

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.uni-heidelberg.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.

Keyword(s): Computational genetics ; Pathogenicity scores ; Variant assessment ; Variant effect prediction

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Contributing Institute(s):

1. DKTK HD zentral (HD01)

Research Program(s):

1. 899 - ohne Topic (POF4-899) (POF4-899)

Appears in the scientific report 2023

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Database coverage:
; ; Article Processing Charges ; Clarivate Analytics Master Journal List ; DOAJ Seal ; Essential Science Indicators ; Fees ; IF >= 5 ; JCR ; PubMed Central ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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