InferenceMAP: mapping of single-molecule dynamics with Bayesian inference

Single-molecule imaging has become ubiquitous in biophysics, biology, biochemistry and biotechnology, covering a large range of in vitro and in vivo applications. This ever-growing field now requires new and reliable statistical tools for data analysis. This is especially true for high-density single-molecule tracking methods that yield massive amounts of data and invite the use of statistics-based methods for analysis. Of particular importance is the extraction of dynamic properties (such as diffusion and transport parameters) and the ability to map these properties at different spatial scales (up to the full extent of the cell).Bayesian analysis is a powerful method that has recently garnered interest in the treatment of single-molecule trajectories. Previously, we have shown that it provides an efficient means for estimating the relevant physical parameters that characterize the motion of individual molecules. Of particular importance, we have shown that interaction fields (which are systematically neglected in most approaches) play a paramount role in the long-term dynamics of biomolecules.With this motivation, we present InferenceMAP, an interactive software tool that uses a powerful Bayesian technique to extract the parameters that describe the motion of individual molecules from single-molecule trajectories. The main features of our tool include:⋅A versatile calculation platform for estimating dynamic parameters, including the ability to specify relevant prior probabilities.⋅Adaptive meshing methods to conform to different temporal and spatial scales⋅The ability to generate vast three-dimensional landscapes of single-molecule dynamicsWe present relevant applications inside lipid rafts, glycine receptors, and HIV assembly platforms.