ArticleNucleic acids research2018
Performance evaluation of pathogenicity-computation methods for missense variants.
Article in Nucleic acids research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 124 papers.
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124 citing papers in PubMed.
- DeNovoSeer:a deep learning framework for pathogenicity prediction of De novo mutations.Briefings in bioinformatics · 2026Article
- Gene4Denovo2: an updated platform for human de novo mutations discovery and interpretation.Nucleic acids research · 2026Article
- CTpredX: Enhancing missense variant pathogenicity prediction in childhood cancer predisposition genes.Genes & diseases · 2026Article
- Exploration of the Pathogenic Mechanism of the Factor XIII A Subunit in a Patient With Congenital Factor XIII Deficiency.Molecular genetics & genomic medicine · 2026Article
- Comparison of missing data handling methods for variant pathogenicity predictors.NAR genomics and bioinformatics · 2025Article
- MutAnt: mutation annotation tool predicts deleteriousness of missense mutations and improves mutation calling from transcriptomics.Human genetics · 2025Article
- Characterization of the genetic and clinical landscapes of DCTN1 gene in neurodegenerative diseases: a series of large case-control study.NPJ genomic medicine · 2025Article
- Functional synonymous mutations and their evolutionary consequences.Nature reviews. Genetics · 2025Review
- Assessing the performance of 28 pathogenicity prediction methods on rare single nucleotide variants in coding regions.BMC genomics · 2025Article
- PRP: pathogenic risk prediction for rare nonsynonymous single nucleotide variants.Human genetics · 2025Article
- Genetic risk and plasma biomarkers of dementia with Lewy bodies in a Chinese population.NPJ Parkinson's disease · 2025Article
- Evaluating variant pathogenicity prediction tools to establish African inclusive guidelines for germline genetic testing.Communications medicine · 2025Article
- LMTK2 and CRB1 are two novel risk genes for Alzheimer's disease in Han Chinese.The journal of prevention of Alzheimer's disease · 2025Article
- Article
- iMFP-LG: Identify Novel Multi-functional Peptides Using Protein Language Models and Graph-based Deep Learning.Genomics, proteomics & bioinformatics · 2025Article
- PSAP-Genomic-Regions: A Method Leveraging Population Data to Prioritize Coding and Non-Coding Variants in Whole Genome Sequencing for Rare Disease Diagnosis.Genetic epidemiology · 2025Article
- De NovoHuman mutation · 2025Article
- Epilepsy-associatedFrontiers in neurology · 2025Article
- Ensemble and consensus approaches to prediction of recessive inheritance for missense variants in human disease.Cell reports methods · 2024Article
- Autosomal dominant stromal corneal dystrophy associated with a SPARCL1 missense variant.European journal of human genetics : EJHG · 2024Article
64 more citing papers are in PubMed but not listed here.
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8 authors.
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Abstract
With expanding applications of next-generation sequencing in medical genetics, increasing computational methods are being developed to predict the pathogenicity of missense variants. Selecting optimal methods can accelerate the identification of candidate genes. However, the performances of different computational methods under various conditions have not been completely evaluated. Here, we compared 12 performance measures of 23 methods based on three independent benchmark datasets: (i) clinical variants from the ClinVar database related to genetic diseases, (ii) somatic variants from the IARC TP53 and ICGC databases related to human cancers and (iii) experimentally evaluated PPARG variants. Some methods showed different performances under different conditions, suggesting that they were not always applicable for different conditions. Furthermore, the specificities were lower than the sensitivities for most methods (especially, for the experimentally evaluated benchmark datasets), suggesting that more rigorous cutoff values are necessary to distinguish pathogenic variants. Furthermore, REVEL, VEST3 and the combination of both methods (i.e. ReVe) showed the best overall performances with all the benchmark data. Finally, we evaluated the performances of these methods with de novo mutations, finding that ReVe consistently showed the best performance. We have summarized the performances of different methods under various conditions, providing tentative guidance for optimal tool selection.
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