AutoStepfinder: A fast and automated step detection method for single-molecule analysis

Luuk Loeff; Jacob W.J. Kerssemakers; Chirlmin Joo; Cees Dekker

doi:10.1016/j.patter.2021.100256

AutoStepfinder: A fast and automated step detection method for single-molecule analysis

Luuk Loeff, Jacob W.J. Kerssemakers, Chirlmin Joo, Cees Dekker

Department of Physics

Research output: Contribution to journal › Article › peer-review

21 Scopus citations

Abstract

Single-molecule techniques allow the visualization of the molecular dynamics of nucleic acids and proteins with high spatiotemporal resolution. Valuable kinetic information of biomolecules can be obtained when the discrete states within single-molecule time trajectories are determined. Here, we present a fast, automated, and bias-free step detection method, AutoStepfinder, that determines steps in large datasets without requiring prior knowledge on the noise contributions and location of steps. The analysis is based on a series of partition events that minimize the difference between the data and the fit. A dual-pass strategy determines the optimal fit and allows AutoStepfinder to detect steps of a wide variety of sizes. We demonstrate step detection for a broad variety of experimental traces. The user-friendly interface and the automated detection of AutoStepfinder provides a robust analysis procedure that enables anyone without programming knowledge to generate step fits and informative plots in less than an hour.

Original language	English
Article number	100256
Journal	Patterns
Volume	2
Issue number	5
DOIs	https://doi.org/10.1016/j.patter.2021.100256
State	Published - 14 May 2021

Bibliographical note

Funding Information:
We would like to thank Jorine Eeftens for providing magnetic tweezer data, Laura Restrepo Perez for providing nanopore data, and Marileen Dogterom for contributions to the earlier version of Stepfinder and for critically reading this manuscript. C.D. was funded by the ERC Advanced Grant LoopingDNA (no. 883684) and The Netherlands Organization of Scientific Research (NWO/OCW) as part of the Frontiers of Nanoscience Program. C.J. was funded by Vidi (864.14.002) of the Netherlands Organisation for Scientific Research. L.L. J.K. C.J. and C.D. conceived the study. L.L. and J.K. developed the code. L.L. and J.K. analyzed the data. L.L. J.K. C.J. and C.D. discussed the data and wrote the manuscript. The authors declare no competing financial interests.

Funding Information:
We would like to thank Jorine Eeftens for providing magnetic tweezer data, Laura Restrepo Perez for providing nanopore data, and Marileen Dogterom for contributions to the earlier version of Stepfinder and for critically reading this manuscript. C.D. was funded by the ERC Advanced Grant LoopingDNA (no. 883684) and The Netherlands Organization of Scientific Research (NWO/OCW) as part of the Frontiers of Nanoscience Program. C.J. was funded by Vidi ( 864.14.002 ) of the Netherlands Organisation for Scientific Research .

Publisher Copyright:
© 2021 The Authors

Keywords

AutoStepfinder
DSML 3: Development/Pre-production: Data science output has been rolled out/validated across multiple domains/problems
Stepfinder
biophysics
data analysis
fluorescence
magnetic tweezer
nanopore
optical tweezer
single molecule
step detection

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10.1016/j.patter.2021.100256

Cite this

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title = "AutoStepfinder: A fast and automated step detection method for single-molecule analysis",

abstract = "Single-molecule techniques allow the visualization of the molecular dynamics of nucleic acids and proteins with high spatiotemporal resolution. Valuable kinetic information of biomolecules can be obtained when the discrete states within single-molecule time trajectories are determined. Here, we present a fast, automated, and bias-free step detection method, AutoStepfinder, that determines steps in large datasets without requiring prior knowledge on the noise contributions and location of steps. The analysis is based on a series of partition events that minimize the difference between the data and the fit. A dual-pass strategy determines the optimal fit and allows AutoStepfinder to detect steps of a wide variety of sizes. We demonstrate step detection for a broad variety of experimental traces. The user-friendly interface and the automated detection of AutoStepfinder provides a robust analysis procedure that enables anyone without programming knowledge to generate step fits and informative plots in less than an hour.",

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author = "Luuk Loeff and Kerssemakers, {Jacob W.J.} and Chirlmin Joo and Cees Dekker",

note = "Funding Information: We would like to thank Jorine Eeftens for providing magnetic tweezer data, Laura Restrepo Perez for providing nanopore data, and Marileen Dogterom for contributions to the earlier version of Stepfinder and for critically reading this manuscript. C.D. was funded by the ERC Advanced Grant LoopingDNA (no. 883684) and The Netherlands Organization of Scientific Research (NWO/OCW) as part of the Frontiers of Nanoscience Program. C.J. was funded by Vidi (864.14.002) of the Netherlands Organisation for Scientific Research. L.L. J.K. C.J. and C.D. conceived the study. L.L. and J.K. developed the code. L.L. and J.K. analyzed the data. L.L. J.K. C.J. and C.D. discussed the data and wrote the manuscript. The authors declare no competing financial interests. Funding Information: We would like to thank Jorine Eeftens for providing magnetic tweezer data, Laura Restrepo Perez for providing nanopore data, and Marileen Dogterom for contributions to the earlier version of Stepfinder and for critically reading this manuscript. C.D. was funded by the ERC Advanced Grant LoopingDNA (no. 883684) and The Netherlands Organization of Scientific Research (NWO/OCW) as part of the Frontiers of Nanoscience Program. C.J. was funded by Vidi ( 864.14.002 ) of the Netherlands Organisation for Scientific Research . Publisher Copyright: {\textcopyright} 2021 The Authors",

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N2 - Single-molecule techniques allow the visualization of the molecular dynamics of nucleic acids and proteins with high spatiotemporal resolution. Valuable kinetic information of biomolecules can be obtained when the discrete states within single-molecule time trajectories are determined. Here, we present a fast, automated, and bias-free step detection method, AutoStepfinder, that determines steps in large datasets without requiring prior knowledge on the noise contributions and location of steps. The analysis is based on a series of partition events that minimize the difference between the data and the fit. A dual-pass strategy determines the optimal fit and allows AutoStepfinder to detect steps of a wide variety of sizes. We demonstrate step detection for a broad variety of experimental traces. The user-friendly interface and the automated detection of AutoStepfinder provides a robust analysis procedure that enables anyone without programming knowledge to generate step fits and informative plots in less than an hour.

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M1 - 100256

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AutoStepfinder: A fast and automated step detection method for single-molecule analysis

Abstract

Bibliographical note

Keywords

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