DeepFRET, a software for rapid and automated single-molecule FRET data classification using deep learning

Research output: Contribution to journal › Journal article › Research › peer-review

Standard

DeepFRET, a software for rapid and automated single-molecule FRET data classification using deep learning. / Thomsen, Johannes; Sletfjerding, Magnus Berg; Jensen, Simon Bo; Stella, Stefano; Paul, Bijoya; Malle, Mette Galsgaard; Montoya, Guillermo; Petersen, Troels Christian; Hatzakis, Nikos S.

In: eLife, Vol. 9, e60404, 11.2020.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Thomsen, J, Sletfjerding, MB, Jensen, SB, Stella, S, Paul, B, Malle, MG, Montoya, G, Petersen, TC & Hatzakis, NS 2020, 'DeepFRET, a software for rapid and automated single-molecule FRET data classification using deep learning', eLife, vol. 9, e60404. https://doi.org/10.7554/eLife.60404

APA

Thomsen, J., Sletfjerding, M. B., Jensen, S. B., Stella, S., Paul, B., Malle, M. G., Montoya, G., Petersen, T. C., & Hatzakis, N. S. (2020). DeepFRET, a software for rapid and automated single-molecule FRET data classification using deep learning. eLife, 9, [e60404]. https://doi.org/10.7554/eLife.60404

Vancouver

Thomsen J, Sletfjerding MB, Jensen SB, Stella S, Paul B, Malle MG et al. DeepFRET, a software for rapid and automated single-molecule FRET data classification using deep learning. eLife. 2020 Nov;9. e60404. https://doi.org/10.7554/eLife.60404

Author

Thomsen, Johannes ; Sletfjerding, Magnus Berg ; Jensen, Simon Bo ; Stella, Stefano ; Paul, Bijoya ; Malle, Mette Galsgaard ; Montoya, Guillermo ; Petersen, Troels Christian ; Hatzakis, Nikos S. / DeepFRET, a software for rapid and automated single-molecule FRET data classification using deep learning. In: eLife. 2020 ; Vol. 9.

Bibtex

@article{8063611027a34088b16e55d0b37bee9a,
title = "DeepFRET, a software for rapid and automated single-molecule FRET data classification using deep learning",
abstract = "Single-molecule Forster Resonance energy transfer (smFRET) is an adaptable method for studying the structure and dynamics of biomolecules. The development of high throughput methodologies and the growth of commercial instrumentation have outpaced the development of rapid, standardized, and automated methodologies to objectively analyze the wealth of produced data. Here we present DeepFRET, an automated, open-source standalone solution based on deep learning, where the only crucial human intervention in transiting from raw microscope images to histograms of biomolecule behavior, is a user-adjustable quality threshold. Integrating standard features of smFRET analysis, DeepFRET consequently outputs the common kinetic information metrics. Its classification accuracy on ground truth data reached >95% outperforming human operators and commonly used threshold, only requiring similar to 1% of the time. Its precise and rapid operation on real data demonstrates DeepFRET's capacity to objectively quantify biomolecular dynamics and the potential to contribute to benchmarking smFRET for dynamic structural biology.",
author = "Johannes Thomsen and Sletfjerding, {Magnus Berg} and Jensen, {Simon Bo} and Stefano Stella and Bijoya Paul and Malle, {Mette Galsgaard} and Guillermo Montoya and Petersen, {Troels Christian} and Hatzakis, {Nikos S.}",
year = "2020",
month = nov,
doi = "10.7554/eLife.60404",
language = "English",
volume = "9",
journal = "eLife",
issn = "2050-084X",
publisher = "eLife Sciences Publications Ltd.",

}

RIS

TY - JOUR

T1 - DeepFRET, a software for rapid and automated single-molecule FRET data classification using deep learning

AU - Thomsen, Johannes

AU - Sletfjerding, Magnus Berg

AU - Jensen, Simon Bo

AU - Stella, Stefano

AU - Paul, Bijoya

AU - Malle, Mette Galsgaard

AU - Montoya, Guillermo

AU - Petersen, Troels Christian

AU - Hatzakis, Nikos S.

PY - 2020/11

Y1 - 2020/11

N2 - Single-molecule Forster Resonance energy transfer (smFRET) is an adaptable method for studying the structure and dynamics of biomolecules. The development of high throughput methodologies and the growth of commercial instrumentation have outpaced the development of rapid, standardized, and automated methodologies to objectively analyze the wealth of produced data. Here we present DeepFRET, an automated, open-source standalone solution based on deep learning, where the only crucial human intervention in transiting from raw microscope images to histograms of biomolecule behavior, is a user-adjustable quality threshold. Integrating standard features of smFRET analysis, DeepFRET consequently outputs the common kinetic information metrics. Its classification accuracy on ground truth data reached >95% outperforming human operators and commonly used threshold, only requiring similar to 1% of the time. Its precise and rapid operation on real data demonstrates DeepFRET's capacity to objectively quantify biomolecular dynamics and the potential to contribute to benchmarking smFRET for dynamic structural biology.

AB - Single-molecule Forster Resonance energy transfer (smFRET) is an adaptable method for studying the structure and dynamics of biomolecules. The development of high throughput methodologies and the growth of commercial instrumentation have outpaced the development of rapid, standardized, and automated methodologies to objectively analyze the wealth of produced data. Here we present DeepFRET, an automated, open-source standalone solution based on deep learning, where the only crucial human intervention in transiting from raw microscope images to histograms of biomolecule behavior, is a user-adjustable quality threshold. Integrating standard features of smFRET analysis, DeepFRET consequently outputs the common kinetic information metrics. Its classification accuracy on ground truth data reached >95% outperforming human operators and commonly used threshold, only requiring similar to 1% of the time. Its precise and rapid operation on real data demonstrates DeepFRET's capacity to objectively quantify biomolecular dynamics and the potential to contribute to benchmarking smFRET for dynamic structural biology.

U2 - 10.7554/eLife.60404

DO - 10.7554/eLife.60404

M3 - Journal article

C2 - 33138911

VL - 9

JO - eLife

JF - eLife

SN - 2050-084X

M1 - e60404

ER -

ID: 252105188