ArticlePloS one2014
Gene-specific function prediction for non-synonymous mutations in monogenic diabetes genes.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Clinical, biochemical, and molecular characterization of a cohort of Egyptian patients with Sanfilippo B syndrome (MPS IIIB): Bayesian Gaussian mixture model.Molecular biology reports · 2025Article
- PRP: pathogenic risk prediction for rare nonsynonymous single nucleotide variants.Human genetics · 2025Article
- Novel gene-specific Bayesian Gaussian mixture model to predict the missense variants pathogenicity of Sanfilippo syndrome.Scientific reports · 2024Article
- mvPPT: A Highly Efficient and Sensitive Pathogenicity Prediction Tool for Missense Variants.Genomics, proteomics & bioinformatics · 2023Article
- Desmoplakin and periplakin genetically and functionally contribute to eosinophilic esophagitis.Nature communications · 2021Observational
- Opportunities and challenges for the computational interpretation of rare variation in clinically important genes.American journal of human genetics · 2021Review
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- REVEL and BayesDel outperform other in silico meta-predictors for clinical variant classification.Scientific reports · 2019Article
- Role of deleterious single nucleotide variants in the coding regions of TNFAIP3 for Japanese autoimmune hepatitis with cirrhosis.Scientific reports · 2019Article
- ClinPred: Prediction Tool to Identify Disease-Relevant Nonsynonymous Single-Nucleotide Variants.American journal of human genetics · 2018Article
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- Article
- The role of functional data in interpreting the effects of genetic variation.Molecular biology of the cell · 2015Article
- Secondary findings and carrier test frequencies in a large multiethnic sample.Genome medicine · 2015Article
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Authors and funding
6 authors.
Funding
Abstract
The rapid progress of genomic technologies has been providing new opportunities to address the need of maturity-onset diabetes of the young (MODY) molecular diagnosis. However, whether a new mutation causes MODY can be questionable. A number of in silico methods have been developed to predict functional effects of rare human mutations. The purpose of this study is to compare the performance of different bioinformatics methods in the functional prediction of nonsynonymous mutations in each MODY gene, and provides reference matrices to assist the molecular diagnosis of MODY. Our study showed that the prediction scores by different methods of the diabetes mutations were highly correlated, but were more complimentary than replacement to each other. The available in silico methods for the prediction of diabetes mutations had varied performances across different genes. Applying gene-specific thresholds defined by this study may be able to increase the performance of in silico prediction of disease-causing mutations.
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