Semi-automated selection of cryo-EM particles in RELION-1.3

By Sjors H. W. Scheres. 2015

375 citations recorded by the source corpus.

The selection of particles suitable for high-resolution cryo-EM structure determination from noisy micrographs may represent a tedious and time-consuming step. Here, a semi-automated particle selection procedure is presented that has been implemented within the open-source software RELION. At the heart of the procedure lies a fully CTF-corrected template-based picking algorithm, which is supplemented by a fast sorting algorithm and reference-free 2D class averaging to remove false positives. With only limited user-interaction, the proposed procedure yields results that are comparable to manual particle selection. Together with an improved graphical user interface, these developments further contribute to turning RELION from a stand-alone refinement program into a convenient image processing pipeline for the entire single-particle approach.

Subfields: Materials Chemistry, Structural Biology, Surfaces, Coatings and Films

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Materials Chemistry

1. Shaoda He
2. Sjors H. W. Scheres
3. Dari Kimanius
4. Erik Lindahl
5. Björn Forsberg
6. Ouyang Qi
7. Youdong Mao
8. Yanan Zhu

In Structural Biology

1. Shaoda He
2. Dari Kimanius
3. Takanori Nakane
4. Erik Lindahl
5. Björn Forsberg
6. W. Hagen
7. Jasenko Zivanov
8. Youdong Mao

In Surfaces, Coatings and Films

1. Shaoda He
2. Dari Kimanius
3. Erik Lindahl
4. Takanori Nakane
5. Youdong Mao
6. Ouyang Qi
7. Björn Forsberg
8. Jasenko Zivanov

Citations in the corpus

• New tools for automated high-resolution cryo-EM structure determination in RELION-3 (2018, methodology): “The recommended procedure for the selection of particles from micrographs in previous versions of RELION was to calculate templates for reference-based auto-picking from particles that were manually picked in a subset of the micrographs ( Scheres, 2015 ).”
• Processing of Structurally Heterogeneous Cryo-EM Data in RELION. (2016, methodology): “The resulting regularized likelihood optimization algorithm has proven useful for both highresolution reconstruction as well as 2D or 3D classification in a wide range of experimental studies (Bai, McMullan, & Scheres, 2015).”
• A deep convolutional neural network approach to single-particle recognition in cryo-electron microscopy (2016, methodology): “A comparison of the precision-recall curves between DeepEM, RELION [36] and TMACS [16] suggests that DeepEM outperforms these two template-matching based methods (Additional file 1: Figure S4).”
• Automated Particle Picking in Cryo-Electron Micrographs using Deep Regression (2018, methodology): “We selected 10 micrographs from the EMPIAR-10017 dataset (β-galactosidase) to train our FCRN model and FasterRCNN.”
• Decoding Mammalian Ribosome-mRNA States by Translational GTPase Complexes (2016, methodology): “Reference-free two-dimensional class averagingwas used to discard non-ribosomal particles, with those picked using RELION subjected to an additional sorting step (Scheres, 2015).”
• Cryo-EM structure of the adenosine A2A receptor coupled to an engineered heterotrimeric G protein (2018, neutral): “Construction of the 399 thioredoxin-A2AR fusion protein and C-terminally truncated A2AR (1-317), both containing 400 the N154A mutation, is described elsewhere(29).”