Variant Impact Predictor database (VIPdb), version 2: trends from three decades of genetic variant impact predictors.

Genomic variant Genotype–phenotype relationship Indel SNV SV VIPdb Variant Effect Predictor (VEP) Variant Impact Predictor (VIP) Variant interpretation

Journal

Human genomics
ISSN: 1479-7364
Titre abrégé: Hum Genomics
Pays: England
ID NLM: 101202210

Informations de publication

Date de publication:
28 Aug 2024
Historique:
received: 22 06 2024
accepted: 19 08 2024
medline: 31 8 2024
pubmed: 31 8 2024
entrez: 28 8 2024
Statut: epublish

Résumé

Variant interpretation is essential for identifying patients' disease-causing genetic variants amongst the millions detected in their genomes. Hundreds of Variant Impact Predictors (VIPs), also known as Variant Effect Predictors (VEPs), have been developed for this purpose, with a variety of methodologies and goals. To facilitate the exploration of available VIP options, we have created the Variant Impact Predictor database (VIPdb). The Variant Impact Predictor database (VIPdb) version 2 presents a collection of VIPs developed over the past three decades, summarizing their characteristics, ClinGen calibrated scores, CAGI assessment results, publication details, access information, and citation patterns. We previously summarized 217 VIPs and their features in VIPdb in 2019. Building upon this foundation, we identified and categorized an additional 190 VIPs, resulting in a total of 407 VIPs in VIPdb version 2. The majority of the VIPs have the capacity to predict the impacts of single nucleotide variants and nonsynonymous variants. More VIPs tailored to predict the impacts of insertions and deletions have been developed since the 2010s. In contrast, relatively few VIPs are dedicated to the prediction of splicing, structural, synonymous, and regulatory variants. The increasing rate of citations to VIPs reflects the ongoing growth in their use, and the evolving trends in citations reveal development in the field and individual methods. VIPdb version 2 summarizes 407 VIPs and their features, potentially facilitating VIP exploration for various variant interpretation applications. VIPdb is available at  https://genomeinterpretation.org/vipdb.

Sections du résumé

BACKGROUND BACKGROUND
Variant interpretation is essential for identifying patients' disease-causing genetic variants amongst the millions detected in their genomes. Hundreds of Variant Impact Predictors (VIPs), also known as Variant Effect Predictors (VEPs), have been developed for this purpose, with a variety of methodologies and goals. To facilitate the exploration of available VIP options, we have created the Variant Impact Predictor database (VIPdb).
RESULTS RESULTS
The Variant Impact Predictor database (VIPdb) version 2 presents a collection of VIPs developed over the past three decades, summarizing their characteristics, ClinGen calibrated scores, CAGI assessment results, publication details, access information, and citation patterns. We previously summarized 217 VIPs and their features in VIPdb in 2019. Building upon this foundation, we identified and categorized an additional 190 VIPs, resulting in a total of 407 VIPs in VIPdb version 2. The majority of the VIPs have the capacity to predict the impacts of single nucleotide variants and nonsynonymous variants. More VIPs tailored to predict the impacts of insertions and deletions have been developed since the 2010s. In contrast, relatively few VIPs are dedicated to the prediction of splicing, structural, synonymous, and regulatory variants. The increasing rate of citations to VIPs reflects the ongoing growth in their use, and the evolving trends in citations reveal development in the field and individual methods.
CONCLUSIONS CONCLUSIONS
VIPdb version 2 summarizes 407 VIPs and their features, potentially facilitating VIP exploration for various variant interpretation applications. VIPdb is available at  https://genomeinterpretation.org/vipdb.

Identifiants

pubmed: 39198917
doi: 10.1186/s40246-024-00663-z
pii: 10.1186/s40246-024-00663-z
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

90

Subventions

Organisme : NIH HHS
ID : U24 HG007346
Pays : United States

Informations de copyright

© 2024. The Author(s).

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Auteurs

Yu-Jen Lin (YJ)

Department of Molecular and Cell Biology, University of California, Berkeley, CA, 94720, USA.
Center for Computational Biology, University of California, Berkeley, CA, 94720, USA.

Arul S Menon (AS)

Department of Molecular and Cell Biology, University of California, Berkeley, CA, 94720, USA.
College of Computing, Data Science, and Society, University of California, Berkeley, CA, 94720, USA.

Zhiqiang Hu (Z)

Department of Plant and Microbial Biology, University of California, 111 Koshland Hall #3102, Berkeley, CA, 94720-3102, USA.
Illumina, Foster City, CA, 94404, USA.

Steven E Brenner (SE)

Department of Molecular and Cell Biology, University of California, Berkeley, CA, 94720, USA. brenner@compbio.berkeley.edu.
Center for Computational Biology, University of California, Berkeley, CA, 94720, USA. brenner@compbio.berkeley.edu.
College of Computing, Data Science, and Society, University of California, Berkeley, CA, 94720, USA. brenner@compbio.berkeley.edu.
Department of Plant and Microbial Biology, University of California, 111 Koshland Hall #3102, Berkeley, CA, 94720-3102, USA. brenner@compbio.berkeley.edu.

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