REVEL: An Ensemble Method for Predicting the Pathogenicity of Rare Missense Variants.

By Nilah M. Ioannidis, Joseph Rothstein, V. Pejaver, Sumit Middha, S. McDonnell, Saurabh Baheti, Anthony M. Musolf, Qing Li, Emily Rose Holzinger, D. Karyadi and 31 more. 2016

2,471 citations recorded by the source corpus.

Subfields: Epidemiology, Genetics, Molecular Biology

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Citations in the corpus

Language models enable zero-shot prediction of the effects of mutations on protein function (2021, methodology): “Models have been designed specifically for proteins, using feature engineering such as Envision [36] and PolyPhen-2 [37], ensemble methods such as Revel [38], MPC [39], CADD [40], and M-CAP [41], language models such as UniRep [23, 16] and ESM [12], and other representation learning approaches [42, ”
Ensembl 2019 (2018, methodology): “This year we developed new VEP ‘plugins’ to expand this functionality including Missense Tolerance Ratio (MTR) (28) and the Rare Exome Variant Ensemble Learner (REVEL) (29).”
Evaluation of in silico algorithms for use with ACMG/AMP clinical variant interpretation guidelines (2017, methodology): “REVEL testset: this is the test dataset that contained ClinVar variants (Test Data 2) as described in [19].”
ClinPred: Prediction Tool to Identify Disease-Relevant Nonsynonymous Single-Nucleotide Variants. (2018, methodology): “This contrasts with other methods, where the benign variants are often selected based on certain AFs.(3,26) As examples, M-CAP considers any variant with a mean allele frequency % 1% in ExAC and 1000 Genomes as benign while REVEL selects variants with AFs between 0.”
Estimated birth prevalence of Menkes disease and ATP7A-related disorders based on the Genome Aggregation Database (gnomAD) (2020, methodology): “In contrast, the REVEL and CADD ensemble approaches to assessing pathogenicity are considered far more reliable [23,24].”
dbNSFP v4: a comprehensive database of transcript-specific functional predictions and annotations for human nonsynonymous and splice-site SNVs (2020, background): “…Eigen PC [19], FATHMM-XF [20], GenoCanyon [21], LINSIGHT [22], LIST-S2 [23], M-CAP [24], MPC [25], MutPred [26], MVP [27], PrimateAI [28], REVEL [29], SIFT4G [30]), one score for loss of function prediction (ALoFT [31]), and three conservation scores (phyloP17-way_primate [32],…”
Mutation severity spectrum of rare alleles in the human genome is predictive of disease type (2019, result): “DeepSAV trails the two best methods REVEL [46] (a meta-predictor) and VEST4 by about 0.01 in AUC.”
Variable population prevalence estimates of germline TP53 variants: a gnomAD-based analysis (2018, background): “, 2003), bioinformatics pathogenicity predictions provided by the Rare ExomeVariant Ensemble Learner (REVEL) (Ioannidis et al., 2016), and clinical classifications provided by both ClinVar (Landrum et al.”