60960 20191105115800.0 doi 10.1186/s12859-017-1562-7 sideral 98400 ART-2017-98400 eng Martín Navarro, Antonio Machine learning classifier for identification of damaging missense mutations exclusive to human mitochondrial DNA-encoded polypeptides 2017 Access copy available to the general public Unrestricted Background: Several methods have been developed to predict the pathogenicity of missense mutations but none has been specifically designed for classification of variants in mtDNA-encoded polypeptides. Moreover, there is not available curated dataset of neutral and damaging mtDNA missense variants to test the accuracy of predictors. Because mtDNA sequencing of patients suffering mitochondrial diseases is revealing many missense mutations, it is needed to prioritize candidate substitutions for further confirmation. Predictors can be useful as screening tools but their performance must be improved. Results: We have developed a SVM classifier (Mitoclass.1) specific for mtDNA missense variants. Training and validation of the model was executed with 2,835 mtDNA damaging and neutral amino acid substitutions, previously curated by a set of rigorous pathogenicity criteria with high specificity. Each instance is described by a set of three attributes based on evolutionary conservation in Eukaryota of wildtype and mutant amino acids as well as coevolution and a novel evolutionary analysis of specific substitutions belonging to the same domain of mitochondrial polypeptides. Our classifier has performed better than other web-available tested predictors. We checked performance of three broadly used predictors with the total mutations of our curated dataset. PolyPhen-2 showed the best results for a screening proposal with a good sensitivity. Nevertheless, the number of false positive predictions was too high. Our method has an improved sensitivity and better specificity in relation to PolyPhen-2. We also publish predictions for the complete set of 24,201 possible missense variants in the 13 human mtDNA-encoded polypeptides. Conclusions: Mitoclass.1 allows a better selection of candidate damaging missense variants from mtDNA. A careful search of discriminatory attributes and a training step based on a curated dataset of amino acid substitutions belonging exclusively to human mtDNA genes allows an improved performance. Mitoclass.1 accuracy could be improved in the future when more mtDNA missense substitutions will be available for updating the attributes and retraining the model. info:eu-repo/grantAgreement/ES/MINECO/TIN2016-80347-R info:eu-repo/grantAgreement/ES/MINECO/TIN2011-27479-C04-01 info:eu-repo/grantAgreement/ES/MEC/FPU-AP2010-1058 info:eu-repo/grantAgreement/ES/FIS/PI14-00070 info:eu-repo/grantAgreement/ES/FIS/PI14-00005 info:eu-repo/grantAgreement/ES/DGA/B33 info:eu-repo/semantics/openAccess by http://creativecommons.org/licenses/by/3.0/es/ 2.213 2017 MATHEMATICAL & COMPUTATIONAL BIOLOGY 14 / 59 = 0.237 2017 Q1 T1 BIOCHEMICAL RESEARCH METHODS 43 / 79 = 0.544 2017 Q3 T2 BIOTECHNOLOGY & APPLIED MICROBIOLOGY 80 / 160 = 0.5 2017 Q2 T2 1.479 2017 Applied Mathematics 2017 Q1 Biochemistry 2017 Q1 Computer Science Applications 2017 Q1 Molecular Biology 2017 Q2 Structural Biology 2017 Q2 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Gaudioso Simón, Andrés (orcid)0000-0002-0946-0957 Álvarez Jarreta, Jorge (orcid)0000-0003-1770-6299 Montoya, Julio Universidad de Zaragoza (orcid)0000-0002-9109-5337 Mayordomo, Elvira Universidad de Zaragoza (orcid)0000-0002-0269-7337 Ruiz Pesini, Eduardo Universidad de Zaragoza 1002 060 Universidad de Zaragoza Dpto. Bioq.Biolog.Mol. Celular Área Bioquímica y Biolog.Mole. 5007 570 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Lenguajes y Sistemas Inf. 18, 158 (2017), [11 pp.] BMC bioinformatics BMC BIOINFORMATICS 1471-2105 458553 http://zaguan.unizar.es/record/60960/files/texto_completo.pdf Versión publicada 86976 http://zaguan.unizar.es/record/60960/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:60960 articulos driver 2019-11-05-11:50:18 ARTICLE