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. 2024 Aug 28;18(1):90.
doi: 10.1186/s40246-024-00663-z.

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

Yu-Jen Lin #  1   2 , Arul S Menon #  1   3 , Zhiqiang Hu  4   5 , Steven E Brenner  6   7   8   9
Affiliations

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

Yu-Jen Lin et al. Hum Genomics. .

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 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: 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.

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

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Conflict of interest statement

Zhiqiang Hu is employed at Illumina and has a financial interest in Illumina.

Figures

Fig. 1
Fig. 1
VIP variant type focus
Fig. 2
Fig. 2
Citation and publication analysis of 407 VIPs. a Citations each year for 278 core VIPs (blue) and 129 non-core VIPs (gray). b Histogram of total citations for core VIPs (blue) and non-core VIPs (gray). c VIPs published per year, with original publications in light blue (core) and light gray (non-core), and subsequent publications in dark blue (core) and dark gray (non-core)
Fig. 3
Fig. 3
Citation trend of 278 core VIPs (1993–2023). Word clouds representing core VIPs over a specific time period, using cumulative citations for core VIPs with multiple publications. Font sizes in the word clouds correspond to the logarithm of citation counts for each period, and cloud heights are scaled by the logarithm of the annual citation averages. The top 10 most cited core VIPs during the period are listed. Note: Core VIPs are methods primarily designed for variant impact prediction and are not classified as databases
Fig. 4
Fig. 4
Citation trend of the top 15 most cited core VIPs in the year 2023. Note: Core VIPs are methods primarily designed for variant impact prediction and are not classified as databases
See this image and copyright information in PMC

Update of

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