ArticleHuman genomics2017
Variant effect prediction tools assessed using independent, functional assay-based datasets: implications for discovery and diagnostics.
Article in Human genomics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers, 1 of them a synthesis that pooled it.
What it found
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Who cites it
53 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Empirically determined baseline masking strategies and other considerations for gene-level burden tests.Nature genetics · 2026Pooled it
- Genomic landscape of drug binding and pharmacogenetic variation across diverse populations using SNPdrug3D.Nature communications · 2026Article
- Comprehensive evaluation of ACMG/AMP-based variant classification tools.Bioinformatics (Oxford, England) · 2026Article
- Mutational landscape of normal breast tissues adjacent to invasive breast cancer.Cell reports. Medicine · 2026Article
- Further characterisation of immortalised human lymphatic endothelial cells to explore their transcriptomic profile and VEGFC response.Scientific reports · 2025Article
- Comparison of missing data handling methods for variant pathogenicity predictors.NAR genomics and bioinformatics · 2025Article
- Classification models distinguish functional and trafficking effects of KCNQ1 variants to enhance variant interpretation.bioRxiv : the preprint server for biology · 2025Article
- AI cancer driver mutation predictions are valid in real-world data.Nature communications · 2025Article
- Computational modeling of human genetic variants in mice.bioRxiv : the preprint server for biology · 2025Article
- Dynamicasome-a molecular dynamics-guided and AI-driven pathogenicity prediction catalogue for all genetic mutations.Communications biology · 2025Article
- Variant effect predictor correlation with functional assays is reflective of clinical classification performance.Genome biology · 2025Article
- Translating pharmacogenomic sequencing data into drug response predictions-How to interpret variants of unknown significance.British journal of clinical pharmacology · 2025Review
- Meta-EA: a gene-specific combination of available computational tools for predicting missense variant effects.Nature communications · 2025Article
- Ensemble and consensus approaches to prediction of recessive inheritance for missense variants in human disease.Cell reports methods · 2024Article
- Understanding the heterogeneous performance of variant effect predictors across human protein-coding genes.Scientific reports · 2024Article
- PanEffect: a pan-genome visualization tool for variant effects in maize.Bioinformatics (Oxford, England) · 2024Article
- Development and validation of animal variant classification guidelines to objectively evaluate genetic variant pathogenicity in domestic animals.Frontiers in veterinary science · 2024Article
- In vivo versus in silico assessment of potentially pathogenic missense variants in human reproductive genes.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Model performance and interpretability of semi-supervised generative adversarial networks to predict oncogenic variants with unlabeled data.BMC bioinformatics · 2023Review
- Genome interpretation using in silico predictors of variant impact.Human genetics · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundGenetic variant effect prediction algorithms are used extensively in clinical genomics and research to determine the likely consequences of amino acid substitutions on protein function. It is vital that we better understand their accuracies and limitations because published performance metrics are confounded by serious problems of circularity and error propagation. Here, we derive three independent, functionally determined human mutation datasets, UniFun, BRCA1-DMS and TP53-TA, and employ them, alongside previously described datasets, to assess the pre-eminent variant effect prediction tools.
resultsApparent accuracies of variant effect prediction tools were influenced significantly by the benchmarking dataset. Benchmarking with the assay-determined datasets UniFun and BRCA1-DMS yielded areas under the receiver operating characteristic curves in the modest ranges of 0.52 to 0.63 and 0.54 to 0.75, respectively, considerably lower than observed for other, potentially more conflicted datasets.
conclusionsThese results raise concerns about how such algorithms should be employed, particularly in a clinical setting. Contemporary variant effect prediction tools are unlikely to be as accurate at the general prediction of functional impacts on proteins as reported prior. Use of functional assay-based datasets that avoid prior dependencies promises to be valuable for the ongoing development and accurate benchmarking of such tools.
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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.