Rapid discrimination between deleterious and benign missense mutations in the CAGI 6 experiment.
Journal
Human genomics
ISSN: 1479-7364
Titre abrégé: Hum Genomics
Pays: England
ID NLM: 101202210
Informations de publication
Date de publication:
27 Aug 2024
27 Aug 2024
Historique:
received:
15
06
2023
accepted:
08
08
2024
medline:
28
8
2024
pubmed:
28
8
2024
entrez:
27
8
2024
Statut:
epublish
Résumé
We describe the machine learning tool that we applied in the CAGI 6 experiment to predict whether single residue mutations in proteins are deleterious or benign. This tool was trained using only single sequences, i.e., without multiple sequence alignments or structural information. Instead, we used global characterizations of the protein sequence. Training and testing data for human gene mutations was obtained from ClinVar (ncbi.nlm.nih.gov/pub/ClinVar/), and for non-human gene mutations from Uniprot (www.uniprot.org). Testing was done on post-training data from ClinVar. This testing yielded high AUC and Matthews correlation coefficient (MCC) for well trained examples but low generalizability. For genes with either sparse or unbalanced training data, the prediction accuracy is poor. The resulting prediction server is available online at http://www.mamiris.com/Shoni.cagi6.
Identifiants
pubmed: 39192324
doi: 10.1186/s40246-024-00655-z
pii: 10.1186/s40246-024-00655-z
doi:
Substances chimiques
Proteins
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
89Subventions
Organisme : NIH HHS
ID : R01HG012117
Pays : United States
Informations de copyright
© 2024. The Author(s).
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