POTATO: Automated pipeline for batch analysis of optical tweezers data
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Abstract
Optical tweezers are a single-molecule technique that allows probing of intra- and intermolecular interactions that govern complex biological processes involving molecular motors, protein-nucleic acid interactions, and protein/RNA folding. Recent developments in instrumentation eased and accelerated optical tweezers data acquisition, but analysis of the data remains challenging. Here, to enable high-throughput data analysis, we developed an automated python-based analysis pipeline called POTATO (practical optical tweezers analysis tool). POTATO automatically processes the high-frequency raw data generated by force-ramp experiments and identifies (un)folding events using predefined parameters. After segmentation of the force-distance trajectories at the identified (un)folding events, sections of the curve can be fitted independently to a worm-like chain and freely jointed chain models, and the work applied on the molecule can be calculated by numerical integration. Furthermore, the tool allows plotting of constant force data and fitting of the Gaussian distance distribution over time. All these features are wrapped in a user-friendly graphical interface, which allows researchers without programming knowledge to perform sophisticated data analysis. -(c) 2022 Biophysical SocietyCitation
Stefan Buck, Lukas Pekarek, Neva Caliskan, POTATO: Automated pipeline for batch analysis of optical tweezers data, Biophysical Journal, Volume 121, Issue 15, 2022, Pages 2830-2839, ISSN 0006-3495, https://doi.org/10.1016/j.bpj.2022.06.030. (https://www.sciencedirect.com/science/article/pii/S0006349522005409) Abstract: Optical tweezers are a single-molecule technique that allows probing of intra- and intermolecular interactions that govern complex biological processes involving molecular motors, protein-nucleic acid interactions, and protein/RNA folding. Recent developments in instrumentation eased and accelerated optical tweezers data acquisition, but analysis of the data remains challenging. Here, to enable high-throughput data analysis, we developed an automated python-based analysis pipeline called POTATO (practical optical tweezers analysis tool). POTATO automatically processes the high-frequency raw data generated by force-ramp experiments and identifies (un)folding events using predefined parameters. After segmentation of the force-distance trajectories at the identified (un)folding events, sections of the curve can be fitted independently to a worm-like chain and freely jointed chain models, and the work applied on the molecule can be calculated by numerical integration. Furthermore, the tool allows plotting of constant force data and fitting of the Gaussian distance distribution over time. All these features are wrapped in a user-friendly graphical interface, which allows researchers without programming knowledge to perform sophisticated data analysis.Affiliation
Helmholtz Institute for RNA-based Infection Research (HIRI), Würzburg, GermanyMedical Faculty, Julius-Maximilians University Würzburg, Würzburg, GermanyPublisher
ElsevierJournal
Biophysical JournalAdditional Links
https://www.biorxiv.org/content/10.1101/2021.11.11.468103v1.fullType
ArticleSoftware
Other
Language
enDescription
Optical tweezers are a single-molecule technique that allows probing of intra- and intermolecular interactions that govern complex biological processes involving molecular motors, protein-nucleic acid interactions, and protein/RNA folding. Recent developments in instrumentation eased and accelerated optical tweezers data acquisition, but analysis of the data remains challenging. Here, to enable high-throughput data analysis, we developed an automated python-based analysis pipeline called POTATO (practical optical tweezers analysis tool). POTATO automatically processes the high-frequency raw data generated by force-ramp experiments and identifies (un)folding events using predefined parameters. After segmentation of the force-distance trajectories at the identified (un)folding events, sections of the curve can be fitted independently to a worm-like chain and freely jointed chain models, and the work applied on the molecule can be calculated by numerical integration. Furthermore, the tool allows plotting of constant force data and fitting of the Gaussian distance distribution over time. All these features are wrapped in a user-friendly graphical interface, which allows researchers without programming knowledge to perform sophisticated data analysis.Series/Report no.
Volume 121, Issue 15, 2 August 2022, Pages 2830-2839ISSN
00063495EISSN
15420086Sponsors
European Research Council: The work in our laboratory is supported by the Helmholtz Association and grants from the European Research Council (ERC) Grant Nr. 948636Scopus Count
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