SmBEVO: a computer vision approach to rapid baseline correction of single-molecule time series

Abstract

For many biophysical systems, single-molecule time series provide valuable mechanistic and dynamic information which is often obscured in ensemble measures due to averaging over asynchronous events and heterogeneous conformations. Standard amplitude-based approaches for analyzing these data such as Hidden Markov Models or changepoint analyses assume that each underlying molecular state has a constant signal amplitude over the course of the time series. However, time series generated by real-world experiments are often corrupted by baseline drift which must be corrected prior to application of any amplitude-based technique.