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...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-10-01
|
| Series: | Communications Biology |
| Online Access: | https://doi.org/10.1038/s42003-024-07122-4 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850179075571712000 |
|---|---|
| author | Shuqi Zhou Yu Miao Haoren Qiu Yuan Yao Wenjuan Wang Chunlai Chen |
| author_facet | Shuqi Zhou Yu Miao Haoren Qiu Yuan Yao Wenjuan Wang Chunlai Chen |
| author_sort | Shuqi Zhou |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-8c656b99862742c8b42dfa324e1108a1 |
| institution | OA Journals |
| issn | 2399-3642 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Biology |
| spelling | doaj-art-8c656b99862742c8b42dfa324e1108a12025-08-20T02:18:35ZengNature PortfolioCommunications Biology2399-36422024-10-017111110.1038/s42003-024-07122-4Deep learning based local feature classification to automatically identify single molecule fluorescence eventsShuqi Zhou0Yu Miao1Haoren Qiu2Yuan Yao3Wenjuan Wang4Chunlai Chen5State Key Laboratory of Membrane Biology, Beijing Frontier Research Center for Biological Structure, School of Life Sciences, Tsinghua UniversityState Key Laboratory of Membrane Biology, Beijing Frontier Research Center for Biological Structure, School of Life Sciences, Tsinghua UniversityState Key Laboratory of Membrane Biology, Beijing Frontier Research Center for Biological Structure, School of Life Sciences, Tsinghua UniversityDepartment of Mathematics, The Hong Kong University of Science and TechnologyTechnology Center for Protein Sciences, School of Life Sciences, Tsinghua UniversityState Key Laboratory of Membrane Biology, Beijing Frontier Research Center for Biological Structure, School of Life Sciences, Tsinghua UniversityAbstract 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.https://doi.org/10.1038/s42003-024-07122-4 |
| spellingShingle | Shuqi Zhou Yu Miao Haoren Qiu Yuan Yao Wenjuan Wang Chunlai Chen Deep learning based local feature classification to automatically identify single molecule fluorescence events Communications Biology |
| title | Deep learning based local feature classification to automatically identify single molecule fluorescence events |
| title_full | Deep learning based local feature classification to automatically identify single molecule fluorescence events |
| title_fullStr | Deep learning based local feature classification to automatically identify single molecule fluorescence events |
| title_full_unstemmed | Deep learning based local feature classification to automatically identify single molecule fluorescence events |
| title_short | Deep learning based local feature classification to automatically identify single molecule fluorescence events |
| title_sort | deep learning based local feature classification to automatically identify single molecule fluorescence events |
| url | https://doi.org/10.1038/s42003-024-07122-4 |
| work_keys_str_mv | AT shuqizhou deeplearningbasedlocalfeatureclassificationtoautomaticallyidentifysinglemoleculefluorescenceevents AT yumiao deeplearningbasedlocalfeatureclassificationtoautomaticallyidentifysinglemoleculefluorescenceevents AT haorenqiu deeplearningbasedlocalfeatureclassificationtoautomaticallyidentifysinglemoleculefluorescenceevents AT yuanyao deeplearningbasedlocalfeatureclassificationtoautomaticallyidentifysinglemoleculefluorescenceevents AT wenjuanwang deeplearningbasedlocalfeatureclassificationtoautomaticallyidentifysinglemoleculefluorescenceevents AT chunlaichen deeplearningbasedlocalfeatureclassificationtoautomaticallyidentifysinglemoleculefluorescenceevents |