Journal
FRONTIERS IN BIOINFORMATICS
Volume 1, Issue -, Pages -Publisher
FRONTIERS MEDIA SA
DOI: 10.3389/fbinf.2021.742073
Keywords
single-molecule tracking (SPT); nonparametric Bayesian statistics; hierarchical Dirichlet process (HDP); hidden Markov model (HMM); recurrent neural network (RNN); anomalous diffusion
Categories
Funding
- This work was supported by National Institutes of Health grant R21-GM128022 and National Science Foundation grant EF-1921677 to JB. [R21-GM128022]
- National Institutes of Health [EF-1921677]
- National Science Foundation
Ask authors/readers for more resources
The NOBIAS framework is developed for thorough analysis of complicated SPT data in living cells, providing automatic determination of diffusive states and assessment of dynamics and anomalous diffusion behavior for each state. It demonstrates robustness, high computational efficiency, and advantage in handling experimental trajectories with asymmetry and anomalous diffusion compared to other SPT analysis methods.
Single particle tracking (SPT) enables the investigation of biomolecular dynamics at a high temporal and spatial resolution in living cells, and the analysis of these SPT datasets can reveal biochemical interactions and mechanisms. Still, how to make the best use of these tracking data for a broad set of experimental conditions remains an analysis challenge in the field. Here, we develop a new SPT analysis framework: NOBIAS (NOnparametric Bayesian Inference for Anomalous Diffusion in Single-Molecule Tracking), which applies nonparametric Bayesian statistics and deep learning approaches to thoroughly analyze SPT datasets. In particular, NOBIAS handles complicated live-cell SPT data for which: the number of diffusive states is unknown, mixtures of different diffusive populations may exist within single trajectories, symmetry cannot be assumed between the x and y directions, and anomalous diffusion is possible. NOBIAS provides the number of diffusive states without manual supervision, it quantifies the dynamics and relative populations of each diffusive state, it provides the transition probabilities between states, and it assesses the anomalous diffusion behavior for each state. We validate the performance of NOBIAS with simulated datasets and apply it to the diffusion of single outer-membrane proteins in Bacteroides thetaiotaomicron. Furthermore, we compare NOBIAS with other SPT analysis methods and find that, in addition to these advantages, NOBIAS is robust and has high computational efficiency and is particularly advantageous due to its ability to treat experimental trajectories with asymmetry and anomalous diffusion.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available