ArticleBMC medical genetics2015
Assessment of the predictive accuracy of five in silico prediction tools, alone or in combination, and two metaservers to classify long QT syndrome gene mutations.
Article in BMC medical genetics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers, 1 of them a synthesis that pooled it.
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Who cites it
51 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Machine learning optimized DriverDetect software for high precision prediction of deleterious mutations in human cancers.Scientific reports · 2024Article
- The Competitive Counterflow Assay for Identifying Drugs Transported by Solute Carriers: Principle, Applications, Challenges/Limits, and Perspectives.European journal of drug metabolism and pharmacokinetics · 2024Review
- Using computational approaches to enhance the interpretation of missense variants in the PAX6 gene.European journal of human genetics : EJHG · 2024Article
- Untapped Potential of Poly(ADP-Ribose) Polymerase Inhibitors: Lessons Learned From the Real-World Clinical Homologous Recombination Repair Mutation Testing.World journal of oncology · 2024Article
- MLe-KCNQ2: An Artificial Intelligence Model for the Prognosis of MissenseInternational journal of molecular sciences · 2024Article
- A comprehensive in silico investigation into the pathogenic SNPs in the RTEL1 gene and their biological consequences.PloS one · 2024Article
- Development and validation of animal variant classification guidelines to objectively evaluate genetic variant pathogenicity in domestic animals.Frontiers in veterinary science · 2024Article
- Identification of High-Risk Single Nucleotide Polymorphisms in the Human CYB5R3 Gene Responsible for Recessive Congenital Methemoglobinemia: A Computational Approach.Molecular syndromology · 2023Article
- In Silico-Based Structural Evaluation to Categorize the Pathogenicity of Mutations Identified in the RAD Class of Proteins.ACS omega · 2023Article
- The Spectra of Disease-Causing Mutations in the Ferroportin 1 (Human mutation · 2023Review
- Article
- DNA Repair Mechanisms, Protein Interactions and Therapeutic Targeting of the MRN Complex.Cancers · 2022Review
- Comprehensive In Silico Functional Prediction Analysis of CDKL5 by Single Amino Acid Substitution in the Catalytic Domain.International journal of molecular sciences · 2022Article
- Genome interpretation using in silico predictors of variant impact.Human genetics · 2022Review
- Recurrent Pulmonary Embolism Associated with a New Mutation Site of SLC44A2: A Case Report.Anatolian journal of cardiology · 2022Article
- Genetic Analysis ofGenes · 2022Review
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLong QT syndrome (LQTS) is an autosomal dominant condition predisposing to sudden death from malignant arrhythmia. Genetic testing identifies many missense single nucleotide variants of uncertain pathogenicity. Establishing genetic pathogenicity is an essential prerequisite to family cascade screening. Many laboratories use in silico prediction tools, either alone or in combination, or metaservers, in order to predict pathogenicity; however, their accuracy in the context of LQTS is unknown. We evaluated the accuracy of five in silico programs and two metaservers in the analysis of LQTS 1-3 gene variants.
methodsThe in silico tools SIFT, PolyPhen-2, PROVEAN, SNPs&GO and SNAP, either alone or in all possible combinations, and the metaservers Meta-SNP and PredictSNP, were tested on 312 KCNQ1, KCNH2 and SCN5A gene variants that have previously been characterised by either in vitro or co-segregation studies as either "pathogenic" (283) or "benign" (29). The accuracy, sensitivity, specificity and Matthews Correlation Coefficient (MCC) were calculated to determine the best combination of in silico tools for each LQTS gene, and when all genes are combined.
resultsThe best combination of in silico tools for KCNQ1 is PROVEAN, SNPs&GO and SIFT (accuracy 92.7%, sensitivity 93.1%, specificity 100% and MCC 0.70). The best combination of in silico tools for KCNH2 is SIFT and PROVEAN or PROVEAN, SNPs&GO and SIFT. Both combinations have the same scores for accuracy (91.1%), sensitivity (91.5%), specificity (87.5%) and MCC (0.62). In the case of SCN5A, SNAP and PROVEAN provided the best combination (accuracy 81.4%, sensitivity 86.9%, specificity 50.0%, and MCC 0.32). When all three LQT genes are combined, SIFT, PROVEAN and SNAP is the combination with the best performance (accuracy 82.7%, sensitivity 83.0%, specificity 80.0%, and MCC 0.44). Both metaservers performed better than the single in silico tools; however, they did not perform better than the best performing combination of in silico tools.
conclusionsThe combination of in silico tools with the best performance is gene-dependent. The in silico tools reported here may have some value in assessing variants in the KCNQ1 and KCNH2 genes, but caution should be taken when the analysis is applied to SCN5A gene variants.
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