Comparison and integration of computational methods for deleterious synonymous mutation prediction

• Published in: Briefings in bioinformatics Vol. 21; no. 3; pp. 970 - 981
• Main Authors: Cheng, Na, Li, Menglu, Zhao, Le, Zhang, Bo, Yang, Yuhua, Zheng, Chun-Hou, Xia, Junfeng
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 21.05.2020; Oxford Publishing Limited (England)
• Subjects: Algorithms; Amino acids; Computer applications; Genomes; Identification methods; Mathematical models; mRNA; Mutation; Next-generation sequencing; Nucleotides; Performance enhancement; Performance evaluation; Performance prediction; Predictions; Robustness (mathematics); Structure-function relationships; ensemble learning; deleterious synonymous mutation; prediction model; machine learning
• ISSN: 1467-5463; 1477-4054
• DOI: 10.1093/bib/bbz047

Abstract Synonymous mutations do not change the encoded amino acids but may alter the structure or function of an mRNA in ways that impact gene function. Advances in next generation sequencing technologies have detected numerous synonymous mutations in the human genome. Several computational models have been proposed to predict deleterious synonymous mutations, which have greatly facilitated the development of this important field. Consequently, there is an urgent need to assess the state-of-the-art computational methods for deleterious synonymous mutation prediction to further advance the existing methodologies and to improve performance. In this regard, we systematically compared a total of 10 computational methods (including specific method for deleterious synonymous mutation and general method for single nucleotide mutation) in terms of the algorithms used, calculated features, performance evaluation and software usability. In addition, we constructed two carefully curated independent test datasets and accordingly assessed the robustness and scalability of these different computational methods for the identification of deleterious synonymous mutations. In an effort to improve predictive performance, we established an ensemble model, named Prediction of Deleterious Synonymous Mutation (PrDSM), which averages the ratings generated by the three most accurate predictors. Our benchmark tests demonstrated that the ensemble model PrDSM outperformed the reviewed tools for the prediction of deleterious synonymous mutations. Using the ensemble model, we developed an accessible online predictor, PrDSM, available at http://bioinfo.ahu.edu.cn:8080/PrDSM/. We hope that this comprehensive survey and the proposed strategy for building more accurate models can serve as a useful guide for inspiring future developments of computational methods for deleterious synonymous mutation prediction.