POTATO: An automated pipeline for batch analysis of optical tweezers data

Optical tweezers is 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 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 (https://github.com/REMI-HIRI/POTATO), which allows researchers without programming knowledge to perform sophisticated data analysis. SIGNIFICANCEStudying (un)folding of biopolymer structures with optical tweezers under different conditions generates very large datasets for statistical data analysis. Recent technical improvements accelerated data acquisition by coupling modern instruments with microfluidic systems, at the same time creating the need for a high-throughput, and unbiased data analysis. We developed Practical Optical Tweezers Analysis TOol (POTATO); an open-source python-based tool that can process data gathered by any OT force-ramp experiment in an automated fashion. POTATO is principally designed for data preprocessing, identification of (un)folding events and the fitting of the force-distance curves. In addition, all parameters for preprocessing, statistical analysis and fitting of the curves can be adapted to suit the dataset under analysis in an easy-to-use graphical user interface.