SMAUG: Analyzing single-molecule tracks with nonparametric Bayesian statistics.

ABSTRACT: Single-molecule fluorescence microscopy probes nanoscale, subcellular biology in real time. Existing methods for analyzing single-particle tracking data provide dynamical information, but can suffer from supervisory biases and high uncertainties. Here, we develop a method for the case of multiple interconverting species undergoing free diffusion and introduce a new approach to analyzing single-molecule trajectories: the Single-Molecule Analysis by Unsupervised Gibbs sampling (SMAUG) algorithm, which uses nonparametric Bayesian statistics to uncover the whole range of information contained within a single-particle trajectory dataset. Even in complex systems where multiple biological states lead to a number of observed mobility states, SMAUG provides the number of mobility states, the averag

SUBMITTER: Karslake JD 

PROVIDER: S-EPMC7529709 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature