000060960 001__ 60960
000060960 005__ 20191105115800.0
000060960 0247_ $$2doi$$a10.1186/s12859-017-1562-7
000060960 0248_ $$2sideral$$a98400
000060960 037__ $$aART-2017-98400
000060960 041__ $$aeng
000060960 100__ $$aMartín Navarro, Antonio
000060960 245__ $$aMachine learning classifier for identification of damaging missense mutations exclusive to human mitochondrial DNA-encoded polypeptides
000060960 260__ $$c2017
000060960 5060_ $$aAccess copy available to the general public$$fUnrestricted
000060960 5203_ $$aBackground: 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.
000060960 536__ $$9info:eu-repo/grantAgreement/ES/MINECO/TIN2016-80347-R$$9info:eu-repo/grantAgreement/ES/MINECO/TIN2011-27479-C04-01$$9info:eu-repo/grantAgreement/ES/MEC/FPU-AP2010-1058$$9info:eu-repo/grantAgreement/ES/FIS/PI14-00070$$9info:eu-repo/grantAgreement/ES/FIS/PI14-00005$$9info:eu-repo/grantAgreement/ES/DGA/B33
000060960 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000060960 590__ $$a2.213$$b2017
000060960 591__ $$aMATHEMATICAL & COMPUTATIONAL BIOLOGY$$b14 / 59 = 0.237$$c2017$$dQ1$$eT1
000060960 591__ $$aBIOCHEMICAL RESEARCH METHODS$$b43 / 79 = 0.544$$c2017$$dQ3$$eT2
000060960 591__ $$aBIOTECHNOLOGY & APPLIED MICROBIOLOGY$$b80 / 160 = 0.5$$c2017$$dQ2$$eT2
000060960 592__ $$a1.479$$b2017
000060960 593__ $$aApplied Mathematics$$c2017$$dQ1
000060960 593__ $$aBiochemistry$$c2017$$dQ1
000060960 593__ $$aComputer Science Applications$$c2017$$dQ1
000060960 593__ $$aMolecular Biology$$c2017$$dQ2
000060960 593__ $$aStructural Biology$$c2017$$dQ2
000060960 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000060960 700__ $$aGaudioso Simón, Andrés
000060960 700__ $$0(orcid)0000-0002-0946-0957$$aÁlvarez Jarreta, Jorge
000060960 700__ $$0(orcid)0000-0003-1770-6299$$aMontoya, Julio$$uUniversidad de Zaragoza
000060960 700__ $$0(orcid)0000-0002-9109-5337$$aMayordomo, Elvira$$uUniversidad de Zaragoza
000060960 700__ $$0(orcid)0000-0002-0269-7337$$aRuiz Pesini, Eduardo$$uUniversidad de Zaragoza
000060960 7102_ $$11002$$2060$$aUniversidad de Zaragoza$$bDpto. Bioq.Biolog.Mol. Celular$$cÁrea Bioquímica y Biolog.Mole.
000060960 7102_ $$15007$$2570$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Lenguajes y Sistemas Inf.
000060960 773__ $$g18, 158 (2017), [11 pp.]$$pBMC bioinformatics$$tBMC BIOINFORMATICS$$x1471-2105
000060960 8564_ $$s458553$$uhttps://zaguan.unizar.es/record/60960/files/texto_completo.pdf$$yVersión publicada
000060960 8564_ $$s86976$$uhttps://zaguan.unizar.es/record/60960/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000060960 909CO $$ooai:zaguan.unizar.es:60960$$particulos$$pdriver
000060960 951__ $$a2019-11-05-11:50:18
000060960 980__ $$aARTICLE