Work

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

Year: 2020

Type: article

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.

Source: eLife

DOI: 10.7554/elife.53357

Authors: David S. White, Marcel P. Goldschen-Ohm, Randall H. Goldsmith, Baron Chanda

Institutions: University of Wisconsin–Madison, The University of Texas at Austin

Language: English

Topic: Advanced Fluorescence Microscopy Techniques

Subfield: Biophysics

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: gold

Estimated APC paid: $2,500

Funders: National Science Foundation, National Institutes of Health, National Institute of General Medical Sciences, National Institute of Neurological Disorders and Stroke