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Published on: December 9, 2015
Evaluating the Performance of In Silico Tools for PRRT2 Missense Variants
1Department of Neurology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510120, China.
Background:
Variants in the PRRT2 gene are associated with paroxysmal kinesigenic dyskinesia and other episodic disorders. With the employment of variant screening in patients with episodic dyskinesia, many PRRT2 variants have been discovered. Bioinformatics tools are becoming increasingly important for predicting the functional significance of variants. This study aimed to evaluate the performance of six in silico tools for PRRT2 missense variants.
Methods:
Pathogenic PRRT2 variants were retrieved from the Human Gene Mutation Database (HGMD) and literature from the PubMed database. The benign set of non-deleterious variants was retrieved from the Genome Aggregation Database (gnomAD). The overall accuracy, sensitivity, specificity, positive predictive values, and negative predictive values of SIFT, PolyPhen2, MutationTaster, CADD, Fathmm, and Provean were analyzed. The MCC score and ROC curve were calculated. The GraphPad Prism 8.0 software was used to plot ROC curves for the six bioinformatics software.
Results:
A total of 45 missense variants with confirmed pathogenicity were used as a positive set, and 222 missense variants were used as a negative set. The top three tools in accuracy are Fathmm, Provean, and MutationTaster. The top three predictors in sensitivity are SIFT, Poly- Phen2, and CADD. Regarding specificity, the top three tools were Provean, Fathmm, and MutationTaster. In terms of the MCC and F-score, the highest degree was observed in Fathmm. Fathmm also had the highest AUC score. The cutoff values of Fathmm, CADD, PolyPhen2, and Provean were between the median prediction scores of the positive and negative sets. In contrast, the cutoff value of SIFT was below the median prediction score of the positive and negative sets. Fathmm had the highest accuracy.
Conclusion:
The prediction performance of six in silico tools differed among the parameters. Fathmm had the best prediction performance, with the highest accuracy and MCC/F-score for PRRT2 missense variants.
