FRETboard: Semisupervised classification of FRET traces.

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Tytuł:: FRETboard: Semisupervised classification of FRET traces.
Autorzy:: de Lannoy CV; Bioinformatics Group, Wageningen University, Wageningen. Electronic address: .
Filius M; Department of Bionanoscience, Delft University of Technology, Delft, The Netherlands.
Kim SH; Department of Bionanoscience, Delft University of Technology, Delft, The Netherlands.
Joo C; Department of Bionanoscience, Delft University of Technology, Delft, The Netherlands.
de Ridder D; Bioinformatics Group, Wageningen University, Wageningen.
Źródło:: Biophysical journal [Biophys J] 2021 Aug 17; Vol. 120 (16), pp. 3253-3260. Date of Electronic Publication: 2021 Jul 06.
Typ publikacji:: Journal Article; Research Support, Non-U.S. Gov't
Język:: English
Imprint Name(s):: Publication: Cambridge, MA : Cell Press
Original Publication: New York, Published by Rockefeller University Press [etc.] for the Biophysical Society.
MeSH Terms:: Fluorescence Resonance Energy Transfer*
Software*
Nanotechnology
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Entry Date(s):: Date Created: 20210708 Date Completed: 20210910 Latest Revision: 20220818
Update Code:: 20240105
PubMed Central ID:: PMC8391082
DOI:: 10.1016/j.bpj.2021.06.030
PMID:: 34237288
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Förster resonance energy transfer (FRET) is a useful phenomenon in biomolecular investigations, as it can be leveraged for nanoscale measurements. The optical signals produced by such experiments can be analyzed by fitting a statistical model. Several software tools exist to fit such models in an unsupervised manner but lack the flexibility to adapt to different experimental setups and require local installations. Here, we propose to fit models to optical signals more intuitively by adopting a semisupervised approach, in which the user interactively guides the model to fit a given data set, and introduce FRETboard, a web tool that allows users to provide such guidance. We show that our approach is able to closely reproduce ground truth FRET statistics in a wide range of simulated single-molecule scenarios and correctly estimate parameters for up to 11 states. On in vitro data, we retrieve parameters identical to those obtained by laborious manual classification in a fraction of the required time. Moreover, we designed FRETboard to be easily extendable to other models, allowing it to adapt to future developments in FRET measurement and analysis.
(Copyright © 2021 Biophysical Society. Published by Elsevier Inc. All rights reserved.)

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