Nature Communications · 2020 · 84 citations · 39 references
MedicineBiophysicsComputer VisionScientific VisualizationStructural BioinformaticsCryo-electron MicroscopyElectron MicroscopySpecimen ProcessingAutomated MicroscopyCryo-electron Microscopy Data AnalysisCryo-electron Microscopy ReconstructionCryo-electron Microscopy RefinementCryo-electron Microscopy Structure DeterminationCryogenic Electron MicroscopyParticle PickingRefinement
Single particle cryo-EM requires full automation to allow high-throughput structure determination. Although software packages exist where parts of the cryo-EM pipeline are automated, a complete solution that offers reliable on-the-fly processing, resulting in high-resolution structures, does not exist. Here we present TranSPHIRE: A software package for fully-automated processing of cryo-EM datasets during data acquisition. TranSPHIRE transfers data from the microscope, automatically applies the common pre-processing steps, picks particles, performs 2D clustering, and 3D refinement parallel to image recording. Importantly, TranSPHIRE introduces a machine learning-based feedback loop to re-train its picking model to adapt to any given data set live during processing. This elegant approach enables TranSPHIRE to process data more effectively, producing high-quality particle stacks. TranSPHIRE collects and displays all metrics and microscope settings to allow users to quickly evaluate data during acquisition. TranSPHIRE can run on a single work station and also includes the automated processing of filaments.
39
MotionCor2: anisotropic correction of beam-induced motion for improved cryo-electron microscopy
Shawn Zheng, Eugene Palovcak, Jean‐Paul Armache et al. · Nature Methods · 2017 · 8.9K citations · Full text