Literature DB >> 27577208

[Evaluation of performance of five bioinformatics software for the prediction of missense mutations].

Qianting Chen1, Congling Dai, Qianjun Zhang, Juan Du, Wen Li.   

Abstract

OBJECTIVE: To study the prediction performance evaluation with five kinds of bioinformatics software (SIFT, PolyPhen2, MutationTaster, Provean, MutationAssessor).
METHODS: From own database for genetic mutations collected over the past five years, Chinese literature database, Human Gene Mutation Database, and dbSNP, 121 missense mutations confirmed by functional studies, and 121 missense mutations suspected to be pathogenic by pedigree analysis were used as positive gold standard, while 242 missense mutations with minor allele frequency (MAF)>5% in dominant hereditary diseases were used as negative gold standard. The selected mutations were predicted with the five software. Based on the results, the performance of the five software was evaluated for their sensitivity, specificity, positive predict value, false positive rate, negative predict value, false negative rate, false discovery rate, accuracy, and receiver operating characteristic curve (ROC).
RESULTS: In terms of sensitivity, negative predictive value and false negative rate, the rank was MutationTaster, PolyPhen2, Provean, SIFT, and MutationAssessor. For specificity and false positive rate, the rank was MutationTaster, Provean, MutationAssessor, SIFT, and PolyPhen2. For positive predict value and false discovery rate, the rank was MutationTaster, Provean, MutationAssessor, PolyPhen2, and SIFT. For area under the ROC curve (AUC) and accuracy, the rank was MutationTaster, Provean, PolyPhen2, MutationAssessor, and SIFT.
CONCLUSION: The prediction performance of software may be different when using different parameters. Among the five software, MutationTaster has the best prediction performance.

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Year:  2016        PMID: 27577208     DOI: 10.3760/cma.j.issn.1003-9406.2016.05.009

Source DB:  PubMed          Journal:  Zhonghua Yi Xue Yi Chuan Xue Za Zhi        ISSN: 1003-9406


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