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  • nrc
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  • Clinical Research Assistant
  • Clinical Research Nurse
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  • Pharmacist
  • PhD Student
  • Physician
  • Post-doc
  • Prize
  • Project Manager
  • Research Associate
  • Research Engineer
  • Retired scientist
  • Technician
  • Undergraduate Student
  • Veterinary
  • Visiting Scientist
  • Deputy Director of Center
  • Deputy Director of Department
  • Deputy Director of National Reference Center
  • Deputy Head of Facility
  • Director of Center
  • Director of Department
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Scientific Fields
Diseases
Organisms
Applications
Technique
Starting Date
18
Aug 2021
Status
Ongoing
Members
4
Structures
3

About

Single-molecule localization microscopy (SMLM) allows studying the dynamics of molecules in cells and resolving the biophysical properties that underlie cellular function. With the continuously growing amount of data produced by every single experiment, the computational cost of quantifying these properties increases and becomes a major bottleneck in SMLM data analysis. Mining these data requires an integrated and efficient analysis toolbox.

TRamWAy is an open-source Python library that features:

  1. a conservative tracking procedure for localization data,
  2. a range of sampling techniques for meshing the spatio-temporal support of the data,
  3. computationally efficient solvers for inverse models, with the option of plugging in user-defined functions,
  4. a collection of analysis tools and a simple web-based interface.

It can additionally process in parallel the many data files in a dataset and regions of interest in each file. For example, from a local IPython notebook, it can connect to a remote HPC cluster, submit jobs and retrieve the generated data files onto the local host.

(a) Common processing steps for a single data file or region of interest, including tracking the localized molecules, resolving in space and time the biophysical properties of the molecules and their environment, and analyzing patterns in the resulting maps or movies. (b) Automatic parallelization for entire datasets with multiple regions of interest per image stack.