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 language | English |
|---|---|
| Article number | e1005628 |
| Journal | PLoS Computational Biology |
| Volume | 13 |
| Issue number | 6 |
| DOIs | |
| Publication status | Published - Jun 1 2017 |
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ASJC Scopus subject areas
- Ecology, Evolution, Behavior and Systematics
- Modelling and Simulation
- Ecology
- Molecular Biology
- Genetics
- Cellular and Molecular Neuroscience
- Computational Theory and Mathematics
Cite this
High-confidence assessment of functional impact of human mitochondrial non-synonymous genome variations by APOGEE. / Castellana, Stefano; Fusilli, Caterina; Mazzoccoli, Gianluigi; Biagini, Tommaso; Capocefalo, Daniele; Carella, Massimo; Vescovi, Angelo Luigi; Mazza, Tommaso.
In: PLoS Computational Biology, Vol. 13, No. 6, e1005628, 01.06.2017.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - High-confidence assessment of functional impact of human mitochondrial non-synonymous genome variations by APOGEE
AU - Castellana, Stefano
AU - Fusilli, Caterina
AU - Mazzoccoli, Gianluigi
AU - Biagini, Tommaso
AU - Capocefalo, Daniele
AU - Carella, Massimo
AU - Vescovi, Angelo Luigi
AU - Mazza, Tommaso
PY - 2017/6/1
Y1 - 2017/6/1
N2 - 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.
AB - 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.
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U2 - 10.1371/journal.pcbi.1005628
DO - 10.1371/journal.pcbi.1005628
M3 - Article
C2 - 28640805
AN - SCOPUS:85021737153
VL - 13
JO - PLoS Computational Biology
JF - PLoS Computational Biology
SN - 1553-734X
IS - 6
M1 - e1005628
ER -