APOGEE 2: multi-layer machine-learning model for the interpretable prediction of mitochondrial missense variants

Salvatore Daniele Bianco; Luca Parca; Francesco Petrizzelli; Tommaso Biagini; Agnese Giovannetti; Niccolò Liorni; Alessandro Napoli; Massimo Carella; Vincent Procaccio; Marie T. Lott; Shiping Zhang; Angelo Luigi Vescovi; Douglas C. Wallace; Viviana Caputo; Tommaso Mazza

doi:10.1038/s41467-023-40797-7

BioinformaticsNature CommunicationsAug 19, 2023

Peer-reviewed journal article

APOGEE 2: multi-layer machine-learning model for the interpretable prediction of mitochondrial missense variants

Salvatore Daniele Bianco iD, Luca Parca iD, Francesco Petrizzelli iD, Tommaso Biagini iD, Agnese Giovannetti iD, Niccolò Liorni iD, Alessandro Napoli iD, Massimo Carella iD, Vincent Procaccio iD, Marie T. Lott iD, Shiping Zhang iD, Angelo Luigi Vescovi iD, Douglas C. Wallace iD, Viviana Caputo iD, Tommaso Mazza iD

Abstract

APOGEE 2 is a mitochondrially-centered ensemble method designed to improve the accuracy of pathogenicity predictions for interpreting missense mitochondrial variants. Built on the joint consensus recommendations by the American College of Medical Genetics and Genomics/Association for Molecular Pathology (ACMG/AMP), APOGEE 2 features an improved machine learning method and a curated training set for enhanced performance metrics. It offers region-wise assessments of genome fragility and mechanistic analyses of specific amino acids that cause perceptible long-range effects on protein structure. With clinical and research use in mind, APOGEE 2 scores and pathogenicity probabilities are precompiled and available in MitImpact. APOGEE 2s ability to address challenges in interpreting mitochondrial missense variants makes it an essential tool in the field of mitochondrial genetics.

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