POTATO: Automated pipeline for batch analysis of optical tweezers data
| dc.contributor.author | Buck, Stefan | |
| dc.contributor.author | Pekarek, Lukas | |
| dc.contributor.author | Caliskan, Neva | |
| dc.date.accessioned | 2022-09-27T13:29:05Z | |
| dc.date.available | 2022-09-27T13:29:05Z | |
| dc.date.issued | 2022-08-02 | |
| dc.identifier.citation | 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. | en_US |
| dc.identifier.issn | 00063495 | |
| dc.identifier.doi | 10.1016/j.bpj.2022.06.030 | |
| dc.identifier.uri | http://hdl.handle.net/10033/623249 | |
| dc.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. | en_US |
| dc.description.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 Society | en_US |
| dc.description.sponsorship | European Research Council: The work in our laboratory is supported by the Helmholtz Association and grants from the European Research Council (ERC) Grant Nr. 948636 | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Elsevier | en_US |
| dc.relation | https://doi.org/10.3030/948636 | en_US |
| dc.relation.ispartofseries | Volume 121, Issue 15, 2 August 2022, Pages 2830-2839 | en_US |
| dc.relation.url | https://www.biorxiv.org/content/10.1101/2021.11.11.468103v1.full | en_US |
| dc.rights | embargoedAccess | en_US |
| dc.rights | Attribution 4.0 International | * |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
| dc.subject | Acids; algorithm; Algorithms; analysis; antiviral; Association; B; Base pairs; Berlin; Bias; Binding; Biomolecules; biophysics; cell; Cells; Chains; Combination; data analysis; Detection; Development; DNA; Dynamics; energy; Environment; environmental change; error; European; events; experiment; Export; Expression; Fluorescence; Fluorescence detection; fluorescence microscopy; Folding; force; frameshifting; funding; gene; Gene Expression; Germany; Heterogeneity; High resolution; High throughput; High-resolution; Hiv-1; Host; Human; hybridization; Infection; information; Inhibitor; instrumentation; integration; interaction; Interactions; Kinetics; Knowledge; Laboratories; ligand; literature; macromolecule; Macromolecules; measurement; Mechanism; methods; microfluidic; microfluidics; Microscopy; Model; molecule; Molecules; mRNA; nanotechnology; nucleic acid; Nucleic acids; Nucleic-Acids; nucleocapsid protein; open source; Optical tweezer; optical tweezers; Particles; pattern recognition; Performance; Pipeline; plasticity; potato; prediction; procedures; Protein; protein A; Protein Folding; protein nucleic acid interaction; protein-nucleic acid interaction; Proteins; Pseudoknot; recent advances; Recognition; Recovery; Regulation; research; Response; Rev; review; ribosomal frameshifting; ribosome; Rna; RNA structure; Safety; SARS; SARS-CoV-2; Single cells; Single molecule force spectroscopy; Software; Solanum tuberosum; spectroscopy; Statistical computing; stimulation; Structure; structures; System; technique; tension; Theory; time; Trajectories; Translation; translation regulation; Translocation; Universities; university; Viral; virus; Viruses; ZAP | en_US |
| dc.title | POTATO: Automated pipeline for batch analysis of optical tweezers data | en_US |
| dc.type | Article | en_US |
| dc.type | Software | en_US |
| dc.type | Other | en_US |
| dc.identifier.eissn | 15420086 | |
| dc.contributor.department | Helmholtz Institute for RNA-based Infection Research (HIRI), Würzburg, GermanyMedical Faculty, Julius-Maximilians University Würzburg, Würzburg, Germany | en_US |
| dc.identifier.journal | Biophysical Journal | en_US |
| dc.identifier.orcid | 0000-0003-0435-4757 | |
| dc.identifier.eid | 2-s2.0-85134187723 | |
| dc.identifier.scopusid | SCOPUS_ID:85134187723 | |
| dc.identifier.pii | S0006349522005409 | |
| dc.source.volume | 121 | |
| dc.source.issue | 15 | |
| dc.source.beginpage | 2830 | |
| dc.source.endpage | 2839 | |
| authorProfile.OrchidId | 0000-0003-0435-4757 | |
| dc.source.journaltitle | Biophysical Journal |
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