Evidence map›Paper›PMID 38979289›Full record

ArticlebioRxiv : the preprint server for biology2024

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

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

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Yu-Jen LinDepartment of Molecular and Cell Biology, University of California, Berkeley, California 94720, USA.ORCID 0000-0002-0224-0281
Arul S MenonDepartment of Molecular and Cell Biology, University of California, Berkeley, California 94720, USA.ORCID 0009-0008-7552-3195
Zhiqiang HuDepartment of Plant and Microbial Biology, University of California, Berkeley, California 94720, USA.ORCID 0000-0001-8854-3410
Steven E BrennerDepartment of Molecular and Cell Biology, University of California, Berkeley, California 94720, USA.ORCID 0000-0001-7559-6185

Funding

Center for Critical Assessment of Genome InterpretationU24HG007346 · NHGRI · UNIVERSITY OF CALIFORNIA BERKELEY · PI BRENNER, STEVEN E, IOANNIDIS, NILAH MONNIER · 2020 to 2025
$3.8M
Component 2 - Resource ProjectU41HG007346 · NHGRI · UNIVERSITY OF CALIFORNIA BERKELEY · PI BRENNER, STEVEN E · 2015 to 2017
$1.7M
NHGRI NIH HHS U24 HG007346NHGRI NIH HHS U41 HG007346
6 · The paper itself

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.

Indexed as

genomic variantgenotype-phenotype relationshipindelSNVSVvariant effect predictor (VEP)variant impact predictor (VIP)variant interpretationVIPdb

Identifiers

PMID38979289
PMCPMC11230257

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.