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Related Concept Videos

Electron Microscope Tomography and Single-particle Reconstruction01:07

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Transmission electron microscopy (TEM) can be used to determine the 3D structure of biological samples with the help of techniques such as electron microscope tomography and single-particle reconstruction. While single-particle reconstruction can examine macromolecules and macromolecular complexes in vitro conditions only, tomography permits the study of cell components or small cells in vivo.
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Cryo-electron Microscopy01:28

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Conventional electron microscopy (EM) involves dehydration, fixation, and staining of biological samples, which distorts the native state of biological molecules and results in several artifacts. Also, the high-energy electron beam damages the sample and makes it difficult to obtain high-resolution images. These issues can be addressed using cryo-EM, which uses frozen samples and gentler electron beams. The technique was developed by Jacques Dubochet, Joachim Frank, and Richard Henderson, for...
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Related Experiment Video

Updated: May 6, 2026

A Robust Single-Particle Cryo-Electron Microscopy cryo-EM Processing Workflow with cryoSPARC, RELION, and Scipion
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A Robust Single-Particle Cryo-Electron Microscopy cryo-EM Processing Workflow with cryoSPARC, RELION, and Scipion

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gEMpicker: a highly parallel GPU-accelerated particle picking tool for cryo-electron microscopy.

Thai V Hoang1, Xavier Cavin, Patrick Schultz

  • 1Inria Nancy - Grand Est, 615 rue du Jardin Botanique, 54600 Villers-lès-Nancy, France. VanThai.Hoang@inria.fr.

BMC Structural Biology
|October 23, 2013
PubMed
Summary
This summary is machine-generated.

gEMpicker accelerates particle picking in cryo-electron microscopy using graphics processing units (GPUs). This computational tool significantly speeds up the process, enabling faster high-resolution 3D reconstructions of macromolecular assemblies.

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User-friendly, High-throughput, and Fully Automated Data Acquisition Software for Single-particle Cryo-electron Microscopy
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Area of Science:

  • Structural Biology
  • Biophysics
  • Computational Biology

Background:

  • Accurate particle picking in cryo-electron micrographs is crucial for high-resolution 3D structure determination of macromolecular assemblies.
  • Processing millions of 2D particle images is computationally intensive and poses a bottleneck for achieving sub-nanometer resolution.

Purpose of the Study:

  • To develop a highly parallel, accelerated particle picking tool for cryo-electron microscopy.
  • To overcome the computational limitations in processing large datasets of particle images.

Main Methods:

  • Developed gEMpicker, a correlation-based particle picking software utilizing multiple graphics processing units (GPUs).
  • Implemented a hierarchy of parallel programming techniques for distribution across multiple GPUs and CPU cores in a computer cluster.
  • Employed an optimal reduction algorithm for combining cluster calculation results.

Main Results:

  • gEMpicker achieves results comparable to existing methods like FindEM on standard datasets.
  • A speed-up of approximately 27x was observed when running gEMpicker on a modern GPU compared to a single CPU core.
  • The processing speed scales almost linearly with the number of nodes in a computer cluster.

Conclusions:

  • GPU-accelerated workstations and computer clusters enable significantly higher particle picking throughput.
  • This increased efficiency facilitates faster high-resolution 3D reconstructions in cryo-electron microscopy research.