We report the development of a simple-to-implement magnetic force transducer that can apply a wide range of piconewton (pN) scale forces on single DNA molecules and DNA–protein complexes in the horizontal plane. The resulting low-noise force-extension data enable very high-resolution detection of changes in the DNA tether’s extension: ~0.05 pN in force and <10 nm change in extension. We have also verified that we can manipulate DNA in near equilibrium conditions through the wide range of forces by ramping the force from low to high and back again, and observing minimal hysteresis in the molecule’s force response. Using a calibration technique based on Stokes’ drag law, we have confirmed our force measurements from DNA force-extension experiments obtained using the fluctuation-dissipation theorem applied to transverse fluctuations of the magnetic microsphere. We present data on the force-distance characteristics of a DNA molecule complexed with histones. The results illustrate how the tweezers can be used to study DNA binding proteins at the single molecule level.

Experimental data from single-molecule DNA-protein experiments, such as experiments using optical traps or magnetic tweezers, typically contain steps, plateaus, or dwell regions that are obscured by thermal and other noise sources. We present a nonparametric method for detecting step-like features in noisy biological data sets. Our algorithm does not assume that the steps can be modeled as Heaviside functions or any particular parametric form. No assumptions about the noise source, such as whether the noise is Gaussian or colored, are made either. Instead, for detection of plateaus, the algorithm uses the novel method of analyzing a probability distribution function of the data values. The vast majority of previously published methods for step detection rely on statistical fitting of step functions with the flat segments linked by vertical segments. Our approach is intended for use on data which cannot be modelled as a series of step functions but applies to step functions as a special case. These type of data traces have, so far, been difficult to characterize effectively. We examine the performance of the algorithm through systematic simulation studies and illustrate the use of our algorithm to analyze single molecule DNA-protein micromanipulation experiments carried out by our laboratory. The simulation results and experimental validation suggest that our method is very robust, avoids overfitting, and functions effectively in the presence of noise sources characteristic of single molecule experiments.