ArticleMolecular systems biology2020
Using deep mutational scanning to benchmark variant effect predictors and identify disease mutations.
Article in Molecular systems biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 105 papers.
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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.
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Who cites it
105 citing papers in PubMed.
- Evolution-guided yeast complementation reveals functional differences in human PSPH variants.FEBS open bio · 2026Article
- Engineering selective amyloid precursor protein inhibitors by machine learning and deep mutational scanning.Protein science : a publication of the Protein Society · 2026Article
- Deep Learning and Experimental Validation of BRCA1-Associated Protein-1 Missense Variant Pathogenicity in Uveal Melanoma.Ophthalmology science · 2026Article
- Machine learning framework for cost effective deep mutational scanning through targeted substitution profiling.BMC bioinformatics · 2026Article
- Deep Prior Framework: integrating functional specificity with general plausibility for targeted protein evolution.Briefings in bioinformatics · 2026Article
- Intrinsic dataset features drive mutational effect prediction by protein language models.bioRxiv : the preprint server for biology · 2026Article
- Creating an atlas of variant effects to resolve variants of uncertain significance and guide cardiovascular medicine.Nature reviews. Cardiology · 2026Review
- A scalable approach to resolving variants of uncertain significance.bioRxiv : the preprint server for biology · 2026Article
- Multiplexed assays of variant effect for clinical variant interpretation.Nature reviews. Genetics · 2026Review
- Mutational scanning reveals oncogenic CTNNB1 mutations have diverse effects on signaling.Nature genetics · 2026Article
- Supervised Learning of Protein Melting Temperature: Cross-Species vs. Species-Specific Prediction.Proteins · 2025Article
- LDBT instead of DBTL: combining machine learning and rapid cell-free testing.Nature communications · 2025Article
- Characterizing Variants of Uncertain Drug Resistance (VUDRs) Using Quantitative Measurements at Clinical Exposures.bioRxiv : the preprint server for biology · 2025Article
- Variant scoring tools for deep mutational scanning.Molecular systems biology · 2025Review
- Off-target effects in CRISPR-Cas genome editing for human therapeutics: Progress and challenges.Molecular therapy. Nucleic acids · 2025Review
- A side-by-side comparison of variant function measurements using deep mutational scanning and base editing.Nucleic acids research · 2025Article
- Variant effect predictor correlation with functional assays is reflective of clinical classification performance.Genome biology · 2025Article
- Deep learning tools predict variants in disordered regions with lower sensitivity.BMC genomics · 2025Article
- DTreePred: an online viewer based on machine learning for pathogenicity prediction of genomic variants.BMC bioinformatics · 2025Article
- Language Modelling Techniques for Analysing the Impact of Human Genetic Variation.Bioinformatics and biology insights · 2025Review
45 more citing papers are in PubMed but not listed here.
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Authors and funding
2 authors.
Funding
Abstract
To deal with the huge number of novel protein-coding variants identified by genome and exome sequencing studies, many computational variant effect predictors (VEPs) have been developed. Such predictors are often trained and evaluated using different variant data sets, making a direct comparison between VEPs difficult. In this study, we use 31 previously published deep mutational scanning (DMS) experiments, which provide quantitative, independent phenotypic measurements for large numbers of single amino acid substitutions, in order to benchmark and compare 46 different VEPs. We also evaluate the ability of DMS measurements and VEPs to discriminate between pathogenic and benign missense variants. We find that DMS experiments tend to be superior to the top-ranking predictors, demonstrating the tremendous potential of DMS for identifying novel human disease mutations. Among the VEPs, DeepSequence clearly stood out, showing both the strongest correlations with DMS data and having the best ability to predict pathogenic mutations, which is especially remarkable given that it is an unsupervised method. We further recommend SNAP2, DEOGEN2, SNPs&GO, SuSPect and REVEL based upon their performance in these analyses.
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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.