<i>AutoStepfinder</i>: A fast and automated step detection method for single-molecule analysis.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 34036291.
- Also identified by DOI 10.1016/j.patter.2021.100256 and PMC identifier 8134948.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
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, <i>AutoStepfinder</i>, 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 <i>AutoStepfinder</i> 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 <i>AutoStepfinder</i> provides a robust analysis procedure that enables anyone without programming knowledge to generate step fits and informative plots in less than an hour.