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

ABSTRACT: Single-molecule Förster 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 reache

SUBMITTER: Thomsen J 

PROVIDER: S-EPMC7609065 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature