AutoStepfinder: A fast and automated step detection method for single-molecule analysis.

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, AutoStepfinder, 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 AutoStepfinder to detect steps of a wide variety of sizes. We demonstrate step detection for a broad variety of ex

SUBMITTER: Loeff L 

PROVIDER: S-EPMC8134948 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature