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

Abstract
Data availability
Article and author information
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Abstract

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.

Data availability

Simulated and raw data in addition to analysis scripts are available at https://zenodo.org/record/3727917#.Xn0Fw9NKjq0DOI: 10.5281/zenodo.3727917

The following data sets were generated

(2020) Data Associated with Top-Down Machine Learning for High-Throughput Single-Molecule Analysis
Zenodo, doi:10.5281/zenodo.3727917.

http://dx.doi.org/10.5281/zenodo.3727917

Article and author information

Author details

David S White

Neuroscience, University of Wisconsin-Madison, Madison, United States

Competing interests
No competing interests declared.
Marcel P Goldschen-Ohm

Neuroscience, University of Texas at Austin, Austin, United States

Competing interests
No competing interests declared.

"This ORCID iD identifies the author of this article:" 0000-0003-1466-9808
Randall H Goldsmith

Chemistry, University of Wisconsin-Madison, Madison, United States

For correspondence
rhg@chem.wisc.edu

Competing interests
No competing interests declared.
Baron Chanda

Department of Neuroscience, University of Wisconsin-Madison, Madison, United States

For correspondence
chanda@wisc.edu

Competing interests
Baron Chanda, Reviewing editor, eLife.

"This ORCID iD identifies the author of this article:" 0000-0003-4954-7034

Funding

National Institute of Neurological Disorders and Stroke (NS-101723)

Baron Chanda

National Institute of Neurological Disorders and Stroke (NS-081320)

Baron Chanda

National Institute of Neurological Disorders and Stroke (NS-081293)

Baron Chanda

National Institute of General Medical Sciences (GM007507)

David S White

National Institute of General Medical Sciences (GM127957)

Randall H Goldsmith

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.