Maximum-likelihood position sensing and actively controlled electrokinetic transport for single-molecule trapping

2008 ◽  
Author(s):  
Lloyd Davis ◽  
Zbigniew Sikorski ◽  
William Robinson ◽  
Guoqing Shen ◽  
Xiaoxuan Li ◽  
...  
Keyword(s):  
Maximum Likelihood ◽  
Single Molecule ◽  
Electrokinetic Transport ◽  
Position Sensing ◽  
Molecule Trapping

Analysis of the Steps in Single-Molecule Photobleaching Traces by Using the Hidden Markov Model and Maximum-Likelihood Clustering

2014 ◽  
Vol 9 (8) ◽  
pp. 2303-2308 ◽  
Author(s):  
Jinghe Yuan ◽  
Kangmin He ◽  
Ming Cheng ◽  
Jianqiang Yu ◽  
Xiaohong Fang
Keyword(s):  
Maximum Likelihood ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Single Molecule ◽  
Hidden Markov

The Cat That Caught the Canary: What To Do with Single-Molecule Trapping

ACS Nano ◽  
2011 ◽  
Vol 5 (7) ◽  
pp. 5296-5299 ◽  
Author(s):  
Adam E. Cohen ◽  
Alexander P. Fields
Keyword(s):  
Single Molecule ◽  
Molecule Trapping

scholarly journals Maximum-likelihood model fitting for quantitative analysis of SMLM data

2021 ◽  
Author(s):  
Yu-Le Wu ◽  
Philipp Hoess ◽  
Aline Tschanz ◽  
Ulf Matti ◽  
Markus Mund ◽  
...  
Keyword(s):  
Quantitative Analysis ◽  
Maximum Likelihood ◽  
Single Molecule ◽  
Model Fitting ◽  
Super Resolution ◽  
Likelihood Estimation ◽  
Nuclear Pore ◽  
Analysis Workflow ◽  
Likelihood Model ◽  
Protein Density

Quantitative analysis is an important part of any single-molecule localization microscopy (SMLM) data analysis workflow to extract biological insights from the coordinates of the single fluorophores, but current approaches are restricted to simple geometries or do not work on heterogenous structures. Here, we present LocMoFit (Localization Model Fit), an open-source framework to fit an arbitrary model directly to the localization coordinates in SMLM data. Using maximum likelihood estimation, this tool extracts the most likely parameters for a given model that best describe the data, and can select the most likely model from alternative models. We demonstrate the versatility of LocMoFit by measuring precise dimensions of the nuclear pore complex and microtubules. We also use LocMoFit to assemble static and dynamic multi-color protein density maps from thousands of snapshots. In case an underlying geometry cannot be postulated, LocMoFit can perform single-particle averaging of super-resolution structures without any assumption about geometry or symmetry. We provide extensive simulation and visualization routines to validate the robustness of LocMoFit and tutorials based on example data to enable any user to increase the information content they can extract from their SMLM data.

Maximum likelihood-based analysis of single-molecule photon arrival trajectories

2011 ◽  
Vol 134 (5) ◽  
pp. 054112 ◽  
Author(s):  
Marta Hajdziona ◽  
Andrzej Molski
Keyword(s):  
Maximum Likelihood ◽  
Single Molecule
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