Pipeline for particle picking in cryo-electron microscopy images using convolutional neural networks trained from positive and unlabeled examples. Also featuring micrograph and tomogram denoising with DNNs.
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Updated
Sep 30, 2024 - Jupyter Notebook
Pipeline for particle picking in cryo-electron microscopy images using convolutional neural networks trained from positive and unlabeled examples. Also featuring micrograph and tomogram denoising with DNNs.
A collaborative list of awesome CryoEM (Cryo Electron Microscopy) resources.
A curated list of awesome computational cryo-EM methods.
register 3D point clouds using rotation, translation, and scale transformations.
TomoBEAR is a configurable and customizable modular pipeline for streamlined processing of cryo-electron tomographic data for subtomogram averaging.
The Swiss Army knife for carbohydrate structure validation, refinement and analysis
Overcoming the preferred orientation problem in cryoEM with self-supervised deep-learning
example set up for Relion on AWS ParallelCluster for CryoEM
A Docker-based distribution of Appion-Protomo.
.MRC and .EM thumbnailer for GNOME
volumetric image toolkit for simple feature detection (segment tomograms)
Plot the 2D histogram of Euler angles covered by a set of cryo-EM particles
KLT picker: Particle picking using data-driven optimal templates (Python version)
Plot the histogram of local resolution values of a cryo-EM reconstruction
Plot the class distribution as a function of iteration from a Class2D or Class3D job from RELION
Report the number of particles in each class from a run_data.star file produced by RELION
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