Maximum-likelihood model fitting for quantitative analysis of SMLM data

ABSTRACT: Quantitative data analysis is important for any single-molecule localization microscopy (SMLM) workflow to extract biological insights from the coordinates of the single fluorophores. However, current approaches are restricted to simple geometries or require identical structures. Here, we present LocMoFit (Localization Model Fit), an open-source framework to fit an arbitrary model to localization coordinates. It extracts meaningful parameters from individual structures and can select the most suitable model. In addition to analyzing complex, heterogeneous and dynamic structures for in situ structural biology, we demonstrate how LocMoFit can assemble multi-protein distribution maps of 6 nuclear pore components, calculate single-particle averages without any assumption about geometry or sym

ORGANISM(S): Homo sapiens (human)

SUBMITTER:  

PROVIDER: S-BIAD563 | bioimages |

REPOSITORIES: bioimages