NOBIAS: Analyzing anomalous diffusion in single-molecule tracks with nonparametric Bayesian inference.
Anomalous Diffusion
Hidden Markov Model
Hierarchical Dirichlet Process
Nonparametric Bayesian Statistics
Recurrent Neural Network
Single-Molecule Tracking
Journal
Frontiers in bioinformatics
ISSN: 2673-7647
Titre abrégé: Front Bioinform
Pays: Switzerland
ID NLM: 9918227263306676
Informations de publication
Date de publication:
2021
2021
Historique:
entrez:
2
5
2022
pubmed:
1
1
2021
medline:
1
1
2021
Statut:
ppublish
Résumé
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
Identifiants
pubmed: 35498544
doi: 10.3389/fbinf.2021.742073
pmc: PMC9053523
mid: NIHMS1750087
pii:
doi:
Types de publication
Journal Article
Langues
eng
Subventions
Organisme : NIGMS NIH HHS
ID : R21 GM128022
Pays : United States
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