ArticleHuman genomics2024
Variant Impact Predictor database (VIPdb), version 2: trends from three decades of genetic variant impact predictors.
Article in Human genomics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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.
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.
Who cites it
14 citing papers in PubMed.
- Why variant effect predictors and multiplexed assays agree and disagree.Nature communications · 2026Article
- Calibration of in-frame indel variant effect predictors for clinical variant classification.bioRxiv : the preprint server for biology · 2026Article
- Ancestry-specific performance of variant effect predictors in clinical variant classification.bioRxiv : the preprint server for biology · 2026Article
- Predicting interaction-specific protein-protein interaction perturbations by missense variants with MutPred-PPI.bioRxiv : the preprint server for biology · 2026Article
- Literature-driven extraction and computational prediction of causal statements linking genetic variants to biological processes, pathways and phenotypes.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2026Article
- Deep DNA and protein level feature integration for robust clinical variant interpretation using probabilistic gradient boosting.Frontiers in digital health · 2026Article
- Calibration of additional computational tools expands ClinGen recommendation options for variant classification with PP3/BP4 criteria.Genetics in medicine : official journal of the American College of Medical Genetics · 2025Article
- Variant effect predictor correlation with functional assays is reflective of clinical classification performance.Genome biology · 2025Article
- Guidelines for releasing a variant effect predictor.Genome biology · 2025Article
- DTreePred: an online viewer based on machine learning for pathogenicity prediction of genomic variants.BMC bioinformatics · 2025Article
- Critical assessment of missense variant effect predictors on disease-relevant variant data.Human genetics · 2025Article
- PON-P3: Accurate Prediction of Pathogenicity of Amino Acid Substitutions.International journal of molecular sciences · 2025Article
- Calibration of additional computational tools expands ClinGen recommendation options for variant classification with PP3/BP4 criteria.bioRxiv : the preprint server for biology · 2024Article
- Machine learning models for pharmacogenomic variant effect predictions - recent developments and future frontiers.PharmacogenomicsReview
Corrections and comments
- Update of
Authors and funding
4 authors.
Funding
Abstract
backgroundVariant 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).
resultsThe 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.
conclusionsVIPdb 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.
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Registered trials
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.