In:
PLOS Computational Biology, Public Library of Science (PLoS), Vol. 18, No. 9 ( 2022-9-21), p. e1009785-
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: http://genotoscope.mh-hannover.de:5000 and the command line interface via: https://github.com/damianosmel/GenOtoScope .
Type of Medium:
Online Resource
ISSN:
1553-7358
DOI:
10.1371/journal.pcbi.1009785
DOI:
10.1371/journal.pcbi.1009785.g001
DOI:
10.1371/journal.pcbi.1009785.g002
DOI:
10.1371/journal.pcbi.1009785.g003
DOI:
10.1371/journal.pcbi.1009785.g004
DOI:
10.1371/journal.pcbi.1009785.g005
DOI:
10.1371/journal.pcbi.1009785.g006
DOI:
10.1371/journal.pcbi.1009785.g007
DOI:
10.1371/journal.pcbi.1009785.g008
DOI:
10.1371/journal.pcbi.1009785.g009
DOI:
10.1371/journal.pcbi.1009785.g010
DOI:
10.1371/journal.pcbi.1009785.g011
DOI:
10.1371/journal.pcbi.1009785.g012
DOI:
10.1371/journal.pcbi.1009785.t001
DOI:
10.1371/journal.pcbi.1009785.t002
DOI:
10.1371/journal.pcbi.1009785.t003
DOI:
10.1371/journal.pcbi.1009785.s001
DOI:
10.1371/journal.pcbi.1009785.s002
DOI:
10.1371/journal.pcbi.1009785.s003
DOI:
10.1371/journal.pcbi.1009785.s004
DOI:
10.1371/journal.pcbi.1009785.s005
DOI:
10.1371/journal.pcbi.1009785.s006
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2022
detail.hit.zdb_id:
2193340-6
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