MutationTaster2: mutation prediction for the deep-sequencing age

By J. M. Schwarz, David N. Cooper, M. Schuelke, D. Seelow. 2014

3,513 citations recorded by the source corpus.

Subfields: Cancer Research, Genetics, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cancer Research

In Genetics

In Molecular Biology

Citations in the corpus

CADD: predicting the deleteriousness of variants throughout the human genome (2018, methodology): “All other scores (i.e. FATHMM (4), LRT(5), MutationAssessor (6), MutationTaster (7), Polyphen2, PROVEAN (8), REVEL, SIFT, SiPhy(9), VEST (10)) were annotated via dbNSFP.”
Drug repositioning for non-small cell lung cancer by using machine learning algorithms and topological graph theory (2016, methodology): “Mutation-Taster [60] is another tool that uses NGS data to elucidate the effect of missense mutations on the expression and function of proteins.”
dbNSFP v3.0: A One-Stop Database of Functional Predictions and Annotations for Human Non-synonymous and Splice Site SNVs (2016, methodology): “0: SIFT to the version based on Ensembl 66; MutationTaster to MutationTaster2 [Schwarz et al., 2014]; FATHMM to v2.”
The Evaluation of Tools Used to Predict the Impact of Missense Variants Is Hindered by Two Types of Circularity (2015, methodology): “A number of such tools are already available, such as MutationTaster-2 (MT2) [Schwarz et al., 2014], LRT [Chun and Fay, 2009], PolyPhen-2 (PP2) [Adzhubei et al.”
The Ensembl Variant Effect Predictor (2016, methodology): “Other pathogenicity predictor scores such as Condel (Gonzalez-Perez et al. 2012), FATHMM (Shihab et al. 2013) and MutationTaster (Schwarz et al. 2014) are available for human data via VEP plugins (see Table 4).”
GAVIN: Gene-Aware Variant INterpretation for medical sequencing (2017, methodology): “The MutationTaster2 [13] test set contains known disease mutations from HGMD [24] Professional and putatively harmless polymorphisms from 1000 Genomes.”
Variation Interpretation Predictors: Principles, Types, Performance, and Choice (2016, background): “CADD [Kircher et al., 2014], MutationTaster2 [Schwarz et al., 2014], PROVEAN [Choi et al., 2012], and VEST [Carter et al., 2013; Douville et al., 2016] can predict impacts of insertions, deletions, as well as amino acid substitutions.”
Next-generation diagnostics and disease-gene discovery with the Exomiser (2015, methodology): “Even after filtering out common variants, additional methods are needed to predict the variants that may have serious functional consequences and prioritize them for validation 10–12 .”
dbNSFP v4: a comprehensive database of transcript-specific functional predictions and annotations for human nonsynonymous and splice-site SNVs (2020, background): “While most scores are in general evenly distributed, some scores are notably spiky and sparsely distributed, such as LRT [50], MutationTaster [51], GenoCanyon, phastCons100way_ vertebrate, and phastCons30way_mammalian, among others.”
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”
Calibration of computational tools for missense variant pathogenicity classification and ClinGen recommendations for PP3/BP4 criteria (2022, neutral): “30 The resulting data set consisted of 11,834 variants from 1,914 genes (Figure 1A).”