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POTATO: Automated pipeline for batch analysis of optical tweezers data.
Buck, Stefan Pekarek, Lukas Caliskan, Neva
Buck, Stefan
Pekarek, Lukas
Caliskan, Neva
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Issue Date
2022-06-30
Submitted date
2021-11-23
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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.
Citation
Buck S, Pekarek L, Caliskan N. POTATO: Automated pipeline for batch analysis of optical tweezers data. Biophys J. 2022 Aug 2;121(15):2830-2839. doi: 10.1016/j.bpj.2022.06.030. Epub 2022 Jun 30. PMID: 35778838; PMCID: PMC9388390.
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Language
en
Description
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.
The algorithm is written in python 3. We designed a GUI and wrapped the code into a Windows standalone executable with pyinstaller to open this tool to a broader audience without a bioinformatics background. The code is freely available on GitHub (https://github.com/REMI-HIRI/POTATO), and the architecture of the python files and GUI is further explained in the supporting material.
Series/Report no.
Vol. 121, issue 15
ISSN
0006-3495
EISSN
1542-0086
ISBN
ISMN
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Sponsors
Funding details: European Research Council, ERC, 948636;
Funding details: Helmholtz AssociationFunding text
- 1:- We thank Vojtech Vrba for helpful python discussions. We thank Dr. Anke Sparmann for critically reviewing the manuscript. The work in our laboratory is supported by the Helmholtz Association and European Research Council (ERC) grant no. 948636