FRETboard: semi-supervised classification of fret traces

Abstract

Förster resonance energy transfer (FRET) is a useful phenomenon in biomolecular investigations, as it can be leveraged for nano-scale 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 their operating system-dependent installation requirements and lack of flexibility impede wide-spread adoption. Here we propose to fit such models more efficiently and intuitively by adopting a semi-supervised approach, in which the user interactively guides the model to fit a given dataset, 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 eleven 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.Availabilitysource code is available at https://github.com/cvdelannoy/FRETboard. The FRETboard classification tool is also available as a browser application at https://www.bioinformatics.nl/FRETboard.