AutoStepfinder

This repository contains code associated with the paper:

AutoStepfinder: a fast and automated step detection method for single-molecule analysis*

Luuk Loeff[1,2,3], Jacob W. J. Kerssemakers[1,3], Chirlmin Joo1 [4], Cees Dekker [1,4]

1 Kavli Institute of Nanoscience and Department of Bionanoscience, Delft University of Technology, Delft, The Netherlands 2 Present address: Department of Biochemistry, University of Zurich, Zurich, Switzerland 3 Equal contribution 4 Correspondence: [email protected] (CJ), [email protected] (CD)

HIGHLIGHTS

• Fast, automated, and bias-free detection of steps within single-molecule trajectories.
• Robust step detection without any prior knowledge on the data.
• A dual-pass strategy for the detection of steps over a wide variety of scales.
• [Matlab:] a user-friendly interface for a simplified step fitting procedure
• [Python:] a command-line style development version in freely available Python (post-publication)

NEWS for release V2.1.0

• Matlab: a non-gui version together with a demo stub was added to Auxiliary Tools. This version follows the exact same analysis steps, but allows easy nested use in custom-written code

SUMMARY

Single-molecule techniques allow the visualization of the molecular dynamics of nucleic acids and proteins with high spatio-temporal resolution, for example a motor protein stepping along DNA. 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 we developed to determine steps in large datasets without requiring prior knowledge on the noise contributions, distribution, 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 successful step detection for a broad variety of experimental traces. The user-friendly interface and the automated detection of AutoStepfinder provides a robust analysis procedure that enables anyone without programming knowledge to generate step fits and informative plots in less than an hour. 

REPOSITORY CONTENTS [Matlab]

• code to analyze steps: 'AutoStepFinder' [both GUI and non-GUI based]
• code for cleaning data from corrupted points (such as Inf): 'DataDuster'
• code for generating test traces : 'StepMaker'
• test traces (multi and single column .txt)

REPOSITORY CONTENTS [Python]

• code to analyze steps: 'AutoSteppyfinder' [non-GUI based]
• test traces (single column .txt)

FOR USERS

• an elaborate manual is included with Supplemental information in the paper [written for Matlab version]
• for further details, see sub-repository README's
• disclaimer: Whileas code is continously tested and developed,small bugs may still occur. Feed back is appreciated via Github
• please cite the paper when using this code
• for questions regarding this code, please contact: Dr. Jacob Kerssemakers ([email protected]) or Dr. Luuk Loeff ([email protected])