Deep learning based local feature classification to automatically identify single molecule fluorescence events.
Journal
Communications biology
ISSN: 2399-3642
Titre abrégé: Commun Biol
Pays: England
ID NLM: 101719179
Informations de publication
Date de publication:
28 Oct 2024
28 Oct 2024
Historique:
received:
20
06
2024
accepted:
22
10
2024
medline:
29
10
2024
pubmed:
29
10
2024
entrez:
29
10
2024
Statut:
epublish
Résumé
Long-term single-molecule fluorescence measurements are widely used powerful tools to study the conformational dynamics of biomolecules in real time to further elucidate their conformational dynamics. Typically, thousands or even more single-molecule traces are analyzed to provide statistically meaningful information, which is labor-intensive and can introduce user bias. Recently, several deep-learning models have been developed to automatically classify single-molecule traces. In this study, we introduce DEBRIS (Deep lEarning Based fRagmentatIon approach for Single-molecule fluorescence event identification), a deep-learning model focusing on classifying local features and capable of automatically identifying steady fluorescence signals and dynamically emerging signals of different patterns. DEBRIS efficiently and accurately identifies both one-color and two-color single-molecule events, including their start and end points. By adjusting user-defined criteria, DEBRIS becomes the pioneer in using a deep learning model to accurately classify four different types of single-molecule fluorescence events using the same trained model, demonstrating its universality and ability to enrich the current toolbox.
Identifiants
pubmed: 39468368
doi: 10.1038/s42003-024-07122-4
pii: 10.1038/s42003-024-07122-4
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1404Subventions
Organisme : National Natural Science Foundation of China (National Science Foundation of China)
ID : 21922704
Organisme : National Natural Science Foundation of China (National Science Foundation of China)
ID : 22061160466
Organisme : National Natural Science Foundation of China (National Science Foundation of China)
ID : 22277063
Informations de copyright
© 2024. The Author(s).
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