  Analyzing the physical and chemical properties of single DNA based molecular
machines such as polymerases and helicases often necessitates to track stepping
motion on the length scale of base pairs. Although high resolution instruments
have been developed that are capable of reaching that limit, individual steps
are oftentimes hidden by experimental noise which complicates data processing.
Here, we present an effective two-step algorithm which detects steps in a high
bandwidth signal by minimizing an energy based model (Energy based step-finder,
EBS). First, an efficient convex denoising scheme is applied which allows
compression to tupels of amplitudes and plateau lengths. Second, a
combinatorial optimization algorithm formulated on a graph is used to assign
steps to the tupel data while accounting for prior information. Performance of
the algorithm was tested on poissonian stepping data simulated based on
published kinetics data of RNA Polymerase II (Pol II). Comparison to existing
step-finding methods shows that EBS is superior both in speed and precision.
Moreover, the capability to detect backtracked intervals in experimental data
of Pol II as well as to detect stepping behavior of the Phi29 DNA packaging
motor is demonstrated.
