High-confidence assessment of functional impact of human mitochondrial non-synonymous genome variations by APOGEE

PLoS Comput Biol. 2017 Jun 22;13(6):e1005628. doi: 10.1371/journal.pcbi.1005628. eCollection 2017 Jun.


24,189 are all the possible non-synonymous amino acid changes potentially affecting the human mitochondrial DNA. Only a tiny subset was functionally evaluated with certainty so far, while the pathogenicity of the vast majority was only assessed in-silico by software predictors. Since these tools proved to be rather incongruent, we have designed and implemented APOGEE, a machine-learning algorithm that outperforms all existing prediction methods in estimating the harmfulness of mitochondrial non-synonymous genome variations. We provide a detailed description of the underlying algorithm, of the selected and manually curated training and test sets of variants, as well as of its classification ability.

MeSH terms

  • Algorithms*
  • Chromosome Mapping / methods*
  • DNA Mutational Analysis / methods*
  • Genetic Variation / genetics*
  • Genome, Human / genetics
  • Genome, Mitochondrial / genetics*
  • Humans
  • Machine Learning
  • Pattern Recognition, Automated
  • Reproducibility of Results
  • Sensitivity and Specificity
  • Software

Grant support

The authors received no specific funding for this work.