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Publications

Journal Article Preprint Epublish

PubMed: 38979289 DOI: 10.1101/2024.06.25.600283

Variant Impact Predictor database (VIPdb), version 2: Trends from 25 years of genetic variant impact predictors.

bioRxiv

Authors: Yu-Jen Lin, Arul S Menon, Zhiqiang Hu, Steven E Brenner

Affiliations

  • Department of Molecular and Cell Biology, University of California, Berkeley, California 94720, USA. Center for Computational Biology, University of California, Berkeley, California 94720, USA.
  • Department of Molecular and Cell Biology, University of California, Berkeley, California 94720, USA. College of Computing, Data Science, and Society, University of California, Berkeley, California 94720, USA.
  • Department of Plant and Microbial Biology, University of California, Berkeley, California 94720, USA.
  • Currently at: Illumina, Foster City, California 94404, USA.
  • Department of Molecular and Cell Biology, University of California, Berkeley, California 94720, USA.
  • Center for Computational Biology, University of California, Berkeley, California 94720, USA.
  • College of Computing, Data Science, and Society, University of California, Berkeley, California 94720, USA.

Abstract

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

The Variant Impact Predictor database (VIPdb) version 2 presents a collection of VIPs developed over the past 25 years, 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 186 VIPs, resulting in a total of 403 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

VIPdb version 2 summarizes 403 VIPs and their features, potentially facilitating VIP exploration for various variant interpretation applications.

Availability

VIPdb version 2 is available at https://genomeinterpretation.org/vipdb.