GenOtoScope

Towards automating ACMG classification of variants associated with congenital hearing loss

verfasst von

Damianos P. Melidis, Christian Landgraf, Gunnar Schmidt, Anja Schöner-Heinisch, Sandra von Hardenberg, Anke Lesinski-Schiedat, Wolfgang Nejdl, Bernd Auber

Abstract

Since next-generation sequencing (NGS) has become widely available, large gene panels containing up to several hundred genes can be sequenced cost-efficiently. However, the interpretation of the often large numbers of sequence variants detected when using NGS is laborious, prone to errors and is often difficult to compare across laboratories. To overcome this challenge, the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP) have introduced standards and guidelines for the interpretation of sequencing variants. Additionally, disease-specific refinements have been developed that include accurate thresholds for many criteria, enabling highly automated processing. This is of particular interest for common but heterogeneous disorders such as hearing impairment. With more than 200 genes associated with hearing disorders, the manual inspection of possible causative variants is particularly difficult and time-consuming. To this end, we developed the open-source bioinformatics tool GenOtoScope, which automates the analysis of all ACMG/AMP criteria that can be assessed without further individual patient information or human curator investigation, including the refined loss of function criterion (“PVS1”). Two types of interfaces are provided: (i) a command line application to classify sequence variants in batches for a set of patients and (ii) a user-friendly website to classify single variants. We compared the performance of our tool with two other variant classification tools using two hearing loss data sets, which were manually annotated either by the ClinGen Hearing Loss Gene Curation Expert Panel or the diagnostics unit of our human genetics department. GenOtoScope achieved the best average accuracy and precision for both data sets. Compared to the second-best tool, GenOtoScope improved the accuracy metric by 25.75% and 4.57% and precision metric by 52.11% and 12.13% on the two data sets, respectively. The web interface is accessible via: genotoscope.mh-hannover.de and the command line interface via: github.com/damianosmel/GenOtoScope.

Organisationseinheit(en)

Forschungszentrum L3S
Fachgebiet Wissensbasierte Systeme

Externe Organisation(en)

Medizinische Hochschule Hannover (MHH)

Typ

Artikel

Journal

PLoS Computational Biology

Band

ISSN

1553-734X

Publikationsdatum

21.09.2022

Publikationsstatus

Veröffentlicht

Peer-reviewed

ASJC Scopus Sachgebiete

Ökologie, Evolution, Verhaltenswissenschaften und Systematik, Modellierung und Simulation, Ökologie, Molekularbiologie, Genetik, Zelluläre und Molekulare Neurowissenschaften, Theoretische Informatik und Mathematik

Ziele für nachhaltige Entwicklung

SDG 3 – Gute Gesundheit und Wohlergehen

Elektronische Version(en)

https://doi.org/10.1371/journal.pcbi.1009785 (Zugang: Offen)