Top-down machine learning approach for high-throughput single-molecule analysis

Single-molecule approaches provide enormous insight into the dynamics of biomolecules, but adequately sampling distributions of states and events often requires extensive sampling. Although emerging experimental techniques can generate such large datasets, existing analysis tools are not suitable to...

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Bibliographic Details
Published in:eLife 2020-04, Vol.9
Main Authors: White, David S, Goldschen-Ohm, Marcel P, Goldsmith, Randall H, Chanda, Baron
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Clustering
cooperativity
Datasets
divisive segmentation
Fluorescent Dyes
HCN channels
High-Throughput Screening Assays - methods
Learning algorithms
Machine learning
Mathematical models
Methods
Noise
Pacemakers
Parameter estimation
Sampling
Single Molecule Imaging - methods
Software
Spectrum analysis
Statistical analysis
Structural Biology and Molecular Biophysics
Tools and Resources
unsupervised analysis
Unsupervised Machine Learning
zero mode waveguides
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Summary:Single-molecule approaches provide enormous insight into the dynamics of biomolecules, but adequately sampling distributions of states and events often requires extensive sampling. Although emerging experimental techniques can generate such large datasets, existing analysis tools are not suitable to process the large volume of data obtained in high-throughput paradigms. Here, we present a new analysis platform (DISC) that accelerates unsupervised analysis of single-molecule trajectories. By merging model-free statistical learning with the Viterbi algorithm, DISC idealizes single-molecule trajectories up to three orders of magnitude faster with improved accuracy compared to other commonly used algorithms. Further, we demonstrate the utility of DISC algorithm to probe cooperativity between multiple binding events in the cyclic nucleotide binding domains of HCN pacemaker channel. Given the flexible and efficient nature of DISC, we anticipate it will be a powerful tool for unsupervised processing of high-throughput data across a range of single-molecule experiments.
ISSN:2050-084X
2050-084X
DOI:10.7554/eLife.53357