Deep learning based local feature classification to automatically identify single molecule fluorescence events
Abstract 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 statistic...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-10-01
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| Series: | Communications Biology |
| Online Access: | https://doi.org/10.1038/s42003-024-07122-4 |
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| Summary: | Abstract 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. |
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| ISSN: | 2399-3642 |