TY - JOUR
T1 - Top-down machine learning approach for high-throughput single-molecule analysis
AU - White, David S.
AU - Goldschen-Ohm, Marcel P.
AU - Goldsmith, Randall H.
AU - Chanda, Baron
N1 - Publisher Copyright:
© 2020, eLife Sciences Publications Ltd. All rights reserved.
PY - 2020/4
Y1 - 2020/4
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85084379549&partnerID=8YFLogxK
U2 - 10.7554/eLife.53357
DO - 10.7554/eLife.53357
M3 - Article
C2 - 32267232
AN - SCOPUS:85084379549
VL - 9
JO - eLife
JF - eLife
SN - 2050-084X
M1 - e53357
ER -