Evaluating the Performance of In silico Tools for PRRT2 Missense Variants.

IF 1.6 4区医学 Q4 BIOCHEMICAL RESEARCH METHODS

Combinatorial chemistry & high throughput screening Pub Date : 2024-06-20 DOI:10.2174/0113862073308898240607090256

Hui Sun, Wang Song, Bin Li

{"title":"Evaluating the Performance of In silico Tools for PRRT2 Missense Variants.","authors":"Hui Sun, Wang Song, Bin Li","doi":"10.2174/0113862073308898240607090256","DOIUrl":null,"url":null,"abstract":"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, PolyPhen2, 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.","PeriodicalId":10491,"journal":{"name":"Combinatorial chemistry & high throughput screening","volume":" ","pages":""},"PeriodicalIF":1.6000,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Combinatorial chemistry & high throughput screening","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.2174/0113862073308898240607090256","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}

引用次数: 0

Abstract

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, PolyPhen2, 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.

查看原文本刊更多论文

评估针对 PRRT2 缺义变异的硅学工具的性能。

背景：PRRT2 基因变异与阵发性运动障碍和其他发作性疾病有关。随着对阵发性运动障碍患者进行变异筛选，发现了许多 PRRT2 变异。生物信息学工具在预测变异的功能意义方面正变得越来越重要。本研究旨在评估六种针对 PRRT2 错义变异的硅学工具的性能：方法：从人类基因突变数据库（HGMD）中检索致病性 PRRT2 变异，并从 PubMed 数据库中检索文献。从基因组聚合数据库（gnomAD）中提取了非致畸变异的良性变异集。分析了 SIFT、PolyPhen2、MutationTaster、CADD、Fathmm 和 Provean 的总体准确性、灵敏度、特异性、阳性预测值和阴性预测值。计算了 MCC 评分和 ROC 曲线。使用 GraphPad Prism 8.0 软件绘制了六种生物信息学软件的 ROC 曲线：结果：共有 45 个已证实具有致病性的错义变异被用作阳性集，222 个错义变异被用作阴性集。准确性排名前三的工具是 Fathmm、Provean 和 MutationTaster。灵敏度排名前三的预测工具是 SIFT、PolyPhen2 和 CADD。在特异性方面，排名前三的工具是 Provean、Fathmm 和 MutationTaster。就 MCC 和 F 分数而言，Fathmm 的程度最高。Fathmm 的 AUC 分数也最高。Fathmm、CADD、PolyPhen2 和 Provean 的临界值介于阳性集和阴性集预测得分的中位数之间。相比之下，SIFT 的临界值低于阳性和阴性数据集的预测得分中值。Fathmm 的准确率最高：结论：六种硅学工具的预测性能因参数而异。Fathmm的预测性能最好，对PRRT2错义变异的预测准确率和MCC/F-score最高。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Combinatorial chemistry & high throughput screening 化学-生化研究方法

CiteScore

3.10

自引率

5.60%

发文量

327

审稿时长

7.5 months

期刊介绍： Combinatorial Chemistry & High Throughput Screening (CCHTS) publishes full length original research articles and reviews/mini-reviews dealing with various topics related to chemical biology (High Throughput Screening, Combinatorial Chemistry, Chemoinformatics, Laboratory Automation and Compound management) in advancing drug discovery research. Original research articles and reviews in the following areas are of special interest to the readers of this journal: Target identification and validation Assay design, development, miniaturization and comparison High throughput/high content/in silico screening and associated technologies Label-free detection technologies and applications Stem cell technologies Biomarkers ADMET/PK/PD methodologies and screening Probe discovery and development, hit to lead optimization Combinatorial chemistry (e.g. small molecules, peptide, nucleic acid or phage display libraries) Chemical library design and chemical diversity Chemo/bio-informatics, data mining Compound management Pharmacognosy Natural Products Research (Chemistry, Biology and Pharmacology of Natural Products) Natural Product Analytical Studies Bipharmaceutical studies of Natural products Drug repurposing Data management and statistical analysis Laboratory automation, robotics, microfluidics, signal detection technologies Current & Future Institutional Research Profile Technology transfer, legal and licensing issues Patents.