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

Stefano Castellana, Caterina Fusilli, Gianluigi Mazzoccoli, Tommaso Biagini, Daniele Capocefalo, Massimo Carella, Angelo Luigi Vescovi, Tommaso Mazza

Research output: Contribution to journalArticle

18 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article numbere1005628
JournalPLoS Computational Biology
Volume13
Issue number6
DOIs
StatePublished - Jun 2017

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Modeling and Simulation
  • Ecology
  • Molecular Biology
  • Genetics
  • Cellular and Molecular Neuroscience
  • Computational Theory and Mathematics

Fingerprint Dive into the research topics of 'High-confidence assessment of functional impact of human mitochondrial non-synonymous genome variations by APOGEE'. Together they form a unique fingerprint.

Cite this