An objective, model-independent method for detection of non-uniform steps in noisy signals

Biophysical techniques, such as single molecule FRET, fluorescence microscopy, single ion-channel patch clamping, and optical tweezers often yield data that are noisy time series containing discrete steps. Here we present a method enabling objective identification of nonuniform steps present in such noisy data. Our method does not require the assumption of any underlying kinetic or state models and is thus particularly useful for analysis of novel and poorly understood systems. In contrast to other model-independent methods, no parameters or other information is taken from the user. We find that, at high noise levels, our method exceeds the performance of other model-independent methods in accurately locating steps in simulated noisy data.