M-CAP eliminates a majority of variants of uncertain significance in clinical exomes at high sensitivity

By Karthik Anand Jagadeesh, Aaron M. Wenger, Mark J. Berger, Harendra Guturu, Peter D. Stenson, David N. Cooper, J. Bernstein, Gill Bejerano. 2016

797 citations recorded by the source corpus.

Subfields: Genetics

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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, ”
Evaluation of in silico algorithms for use with ACMG/AMP clinical variant interpretation guidelines (2017, neutral): “For pathogenic variants, the best performing trio combination consisted of MutationTaster, Mcap [21], and CADD (Additional file 1: Table S4), with a fairly high false concordance for benign variants (18.2% and 25.8% false concordance, Additional data 3, see Availability of data and materials section”
The Human Gene Mutation Database: towards a comprehensive repository of inherited mutation data for medical research, genetic diagnosis and next-generation sequencing studies (2017, positive): “HGMD has also been used by a number of different groups to aid the development of a wide variety of post-NGS variant interpretation and exome prioritisation algorithms including MutPred (Li et al. 2009), MutPred Splice (Mort et al. 2014), PROVEAN (Choi et al. 2012), CAROL (Lopes et al. 2012), regSNP”
dbNSFP v4: a comprehensive database of transcript-specific functional predictions and annotations for human nonsynonymous and splice-site SNVs (2020, background): “…[15], CADD_hg19 [16], ClinPred [17], DEOGEN2 [18], Eigen and 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…”
Large-scale clinical interpretation of genetic variants using evolutionary data and deep learning (2020, methodology): “We compare the performance of EVE models with that of the top performing supervised variant effect predictors (VEPs) published to date: M-CAP [25], MISTIC [26], and Polyphen [27] (Extended data Fig.”
ClinPred: Prediction Tool to Identify Disease-Relevant Nonsynonymous Single-Nucleotide Variants. (2018, background): “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 0.1% and 1% across the seven study populations for their benign label.”
Comprehensive Characterization of Cancer Driver Genes and Mutations (2018, methodology): “For all mutations within a gene’s protein coding region, a composite score was calculated through summation of mutation recurrence multiplied by the functional impact score (Jagadeesh et al., 2016).”