Hidden Markov analysis of trajectories in single-molecule experiments and the effects of missed events

The ever more complex fluctuation patterns discovered by single molecule experiments require statistical methods to analyze multi-state hopping traces of long lengths. Hidden Markov modeling is a statistical tool that offers the scalability to analyze even complex data and extract kinetic information. We give an introduction on how to implement hidden Markov modeling for the analysis of single molecule force spectroscopic traces, deal with missed events, and test the method on a calcium binding protein. Complex fluctuation patterns discovered by single-molecule experiments require statistical methods to analyze long multistate hopping traces. The authors give an introduction on how to implement hidden Markov modeling for this purpose and test the method on a calcium binding protein.