ArticleScientific reports2024
Understanding the heterogeneous performance of variant effect predictors across human protein-coding genes.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- A heritability-optimized method for functional prioritization of rare coding variants in complex traits.Nature genetics · 2026Article
- Comparative phylogenetic analysis of breast cancer susceptibility genes reveals evolutionary conservation supporting diagnostic barcode design.Annals of medicine and surgery (2012) · 2026Article
- Why variant effect predictors and multiplexed assays agree and disagree.Nature communications · 2026Article
- Rare coding variant architecture and gene discovery from 130,000 sequenced cases of atrial fibrillation.Research square · 2026Article
- <italic>MKRN3</italic> Variants in Central Precocious Puberty as an Example of the Complexity to Classify Missense Variants in Imprinted Genes as Pathogenic.Hormone research in paediatrics · 2026Article
- Determining the intra-residue correlation of missense variant impact using MAVE scores: implications for the ACMG/AMP PM5 criterion for DNA variant classification.Genome medicine · 2026Article
- Pathogenicity Prediction of Missense Variations in Hereditary Cancer Genes.International journal of molecular sciences · 2026Article
- A near-complete map of human cytosolic degrons and their relevance for disease.Science advances · 2026Article
- DBP-CanPred: a machine learning model for predicting cancer-causing mutations in DNA-binding proteins.Frontiers in bioinformatics · 2026Article
- Calibrated Variant Effect Prediction at the Residue Level Using Conditional Score Distributions.bioRxiv : the preprint server for biology · 2025Article
- Overview of exosomal non-coding RNAs in cardiovascular disease using high throughput sequencing.European journal of pharmacology · 2025Review
- Classification models distinguish functional and trafficking effects of KCNQ1 variants to enhance variant interpretation.bioRxiv : the preprint server for biology · 2025Article
- Assessing variant effect predictors and disease mechanisms in intrinsically disordered proteins.PLoS computational biology · 2025Article
- Variant effect predictor correlation with functional assays is reflective of clinical classification performance.Genome biology · 2025Article
- AFFIPred: AlphaFold2 structure-based Functional Impact Prediction of missense variations.Protein science : a publication of the Protein Society · 2025Article
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2 authors.
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Abstract
Variant effect predictors (VEPs) are computational tools developed to assess the impacts of genetic mutations, often in terms of likely pathogenicity, employing diverse algorithms and training data. Here, we investigate the performance of 35 VEPs in the discrimination between pathogenic and putatively benign missense variants across 963 human protein-coding genes. We observe considerable gene-level heterogeneity as measured by the widely used area under the receiver operating characteristic curve (AUROC) metric. To investigate the origins of this heterogeneity and the extent to which gene-level VEP performance is predictable, for each VEP, we train random forest models to predict the gene-level AUROC. We find that performance as measured by AUROC is related to factors such as gene function, protein structure, and evolutionary conservation. Notably, intrinsic disorder in proteins emerged as a significant factor influencing apparent VEP performance, often leading to inflated AUROC values due to their enrichment in weakly conserved putatively benign variants. Our results suggest that gene-level features may be useful for identifying genes where VEP predictions are likely to be more or less reliable. However, our work also shows that AUROC, despite being independent of class balance, still has crucial limitations when used for comparing VEP performance across different genes.
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