ArticleBMC bioinformatics2017
Machine learning classifier for identification of damaging missense mutations exclusive to human mitochondrial DNA-encoded polypeptides.
Article in BMC bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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22 citing papers in PubMed, 48 citations in OpenAlex.
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- Single-molecule DNA analysis implicates brain mitochondria pathology in bipolar disorder.Molecular psychiatry · 2025Article
- Risk Factors Associated With Leber Hereditary Optic Neuropathy due to Rare Mutations in Mitochondrial DNA-Encoded Respiratory Complex I Subunits.Clinical genetics · 2025Review
- Novel pathogenic mtDNA variants in Chinese children with neurological mitochondrial disorders.Annals of clinical and translational neurology · 2025Article
- Identification and characterization of a new pathologic mutation in a large Leber hereditary optic neuropathy pedigree.Orphanet journal of rare diseases · 2024Article
- MmisAT and MmisP: an efficient and accurate suite of variant analysis toolkit for primary mitochondrial diseases.Human genomics · 2023Article
- Toxic and nutritional factors trigger Leber hereditary optic neuropathy due to a mitochondrial tRNA mutation.Clinical genetics · 2022Article
- Is population frequency a useful criterion to assign pathogenicity to newly described mitochondrial DNA variants?Orphanet journal of rare diseases · 2022Article
- Genetic Alterations in Mitochondrial DNA Are Complementary to Nuclear DNA Mutations in Pheochromocytomas.Cancers · 2022Article
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- Changes in Nuclear Gene Expression Related to Mitochondrial Function Affect Extracellular Matrix, Collagens, and Focal Adhesion in Keratoconus.Translational vision science & technology · 2021Article
- MitImpact 3: modeling the residue interaction network of the Respiratory Chain subunits.Nucleic acids research · 2021Article
- Leber's Hereditary Optic Neuropathy: A Report on Novel mtDNA Pathogenic Variants.Frontiers in neurology · 2021Article
- Whole mitochondrial genome analysis in Chinese patients with keratoconus.Molecular vision · 2021Article
- Possible A2E Mutagenic Effects on RPE Mitochondrial DNA from Innovative RNA-Seq Bioinformatics Pipeline.Antioxidants (Basel, Switzerland) · 2020Article
- Leigh Syndrome in a Pedigree Harboring the m.1555A>G Mutation in the Mitochondrial 12S rRNA.Genes · 2020Article
- Clinical Bioinformatics in Precise Diagnosis of Mitochondrial Disease.Clinics in laboratory medicine · 2020Review
- Deleterious mitochondrial DNA point mutations are overrepresented in Drosophila expressing a proofreading-defective DNA polymerase γ.PLoS genetics · 2018Article
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6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSeveral 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.
resultsWe 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.
conclusionsMitoclass.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.
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