ArticleNucleic acids research2021
Bound2Learn: a machine learning approach for classification of DNA-bound proteins from single-molecule tracking experiments.
Article in Nucleic acids research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Review of Machine Learning for Single-Particle Tracking: Methods, Challenges, and Biophysical Insights.Chemical & biomedical imaging · 2026Review
- CoPixie, a novel algorithm for single-particle track colocalization, enables efficient quantification of telomerase dynamics at telomeres.Nucleic acids research · 2024Article
- Disordered C-terminal domain drives spatiotemporal confinement of RNAPII to enhance search for chromatin targets.Nature cell biology · 2024Article
- Single-molecule tracking for studying protein dynamics and target-search mechanism in live cells of S. cerevisiae.STAR protocols · 2022Article
- Impact ofInternational journal of molecular sciences · 2022Review
- Article
- Following the tracks: How transcription factor binding dynamics control transcription.Biophysical journal · 2022Review
- Editorial: Single-molecule studies of DNA-protein interactions collection 2021.Nucleic acids research · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
DNA-bound proteins are essential elements for the maintenance, regulation, and use of the genome. The time they spend bound to DNA provides useful information on their stability within protein complexes and insight into the understanding of biological processes. Single-particle tracking allows for direct visualization of protein-DNA kinetics, however, identifying whether a molecule is bound to DNA can be non-trivial. Further complications arise when tracking molecules for extended durations in processes with slow kinetics. We developed a machine learning approach, termed Bound2Learn, using output from a widely used tracking software, to robustly classify tracks in order to accurately estimate residence times. We validated our approach in silico, and in live-cell data from Escherichia coli and Saccharomyces cerevisiae. Our method has the potential for broad utility and is applicable to other organisms.
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Registered trials
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