scholarly journals A self-supervised workflow for particle picking in cryo-EM

IUCrJ ◽  
2020 ◽  
Vol 7 (4) ◽  
pp. 719-727 ◽  
Author(s):  
Donal M. McSweeney ◽  
Sean M. McSweeney ◽  
Qun Liu
Keyword(s):  
Data Analysis ◽  
High Resolution ◽  
Single Particle ◽  
Particle Analysis ◽  
Data Sets ◽  
Particle Sizes ◽  
User Input ◽  
Class Average ◽  
Resolution Single ◽  
Selection Of

High-resolution single-particle cryo-EM data analysis relies on accurate particle picking. To facilitate the particle picking process, a self-supervised workflow has been developed. This includes an iterative strategy, which uses a 2D class average to improve training particles, and a progressively improved convolutional neural network for particle picking. To automate the selection of particles, a threshold is defined (%/Res) using the ratio of percentage class distribution and resolution as a cutoff. This workflow has been tested using six publicly available data sets with different particle sizes and shapes, and can automatically pick particles with minimal user input. The picked particles support high-resolution reconstructions at 3.0 Å or better. This workflow is a step towards automated single-particle cryo-EM data analysis at the stage of particle picking. It may be used in conjunction with commonly used single-particle analysis packages such as Relion, cryoSPARC, cisTEM, SPHIRE and EMAN2.

scholarly journals A Self-Supervised Workflow for Particle Picking in Cryo-EM

2020 ◽  
Author(s):  
Donal M. McSweeney ◽  
Sean M. McSweeney ◽  
Qun Liu
Keyword(s):  
Data Analysis ◽  
High Resolution ◽  
Single Particle ◽  
Particle Analysis ◽  
Data Sets ◽  
Particle Sizes ◽  
User Input ◽  
Class Average ◽  
Resolution Single ◽  
Selection Of

AbstractHigh-resolution single-particle cryo-EM data analysis relies on accurate particle picking. To facilitate the particle picking process, we have developed a self-supervised workflow. Our workflow includes an iterative strategy to use the 2D class average to improve training particles and a progressively improved convolutional neural network (CNN) for particle picking. To automate the selection of particles, we define a threshold (%/Res) using the ratio of percentage class distribution and resolution as a cutoff. Our workflow has been tested using six publicly available data sets with different particle sizes and shapes, and is able to automatically pick particles with minimal user input. The picked particles support high-resolution reconstructions at 3.0 Å or better. Our workflow offers a way toward automated single-particle Cryo-EM data analysis at the stage of particle picking. The workflow may be used in conjunction with commonly used single-particle analysis packages such as Relion, cryoSPARC, cisTEM, SPHIRE, and EMAN2.

scholarly journals High-resolution Single Particle Analysis from Electron Cryo-microscopy Images Using SPHIRE

10.3791/55448 ◽  
2017 ◽  
Author(s):  
Toshio Moriya ◽  
Michael Saur ◽  
Markus Stabrin ◽  
Felipe Merino ◽  
Horatiu Voicu ◽  
...  
Keyword(s):  
High Resolution ◽  
Single Particle ◽  
Particle Analysis ◽  
Single Particle Analysis ◽  
Resolution Single ◽  
Microscopy Images

scholarly journals A Fast and Effective Microfluidic Spraying-Plunging Method for High-Resolution Single-Particle Cryo-EM

Structure ◽  
2017 ◽  
Vol 25 (4) ◽  
pp. 663-670.e3 ◽  
Author(s):  
Xiangsong Feng ◽  
Ziao Fu ◽  
Sandip Kaledhonkar ◽  
Yuan Jia ◽  
Binita Shah ◽  
...  
Keyword(s):  
High Resolution ◽  
Single Particle ◽  
Resolution Single

Corrigendum to “A test-bed for optimizing high-resolution single particle reconstructions” [J. Struct. Biol. 163 (2008) 29–39]

2010 ◽  
Vol 171 (2) ◽  
pp. 244
Author(s):  
Scott M. Stagg ◽  
Gabriel C. Lander ◽  
Joel Quispe ◽  
Neil R. Voss ◽  
Anchi Cheng ◽  
...  
Keyword(s):  
High Resolution ◽  
Single Particle ◽  
Test Bed ◽  
Resolution Single

High-resolution single-particle 3D analysis on GroEL prepared by cryo-negative staining

Micron ◽  
2008 ◽  
Vol 39 (7) ◽  
pp. 934-943 ◽  
Author(s):  
Sacha De Carlo ◽  
Nicolas Boisset ◽  
Andreas Hoenger
Keyword(s):  
High Resolution ◽  
Single Particle ◽  
Negative Staining ◽  
3D Analysis ◽  
Resolution Single

scholarly journals Analysis of ionospheric electrodynamic parameters on mesoscales – a review of selected techniques using data from ground-based observation networks and satellites

2011 ◽  
Vol 29 (3) ◽  
pp. 467-491 ◽  
Author(s):  
H. Vanhamäki ◽  
O. Amm
Keyword(s):  
Data Analysis ◽  
Data Sets ◽  
Practical Applications ◽  
Analysis Methods ◽  
Ionospheric Electrodynamics ◽  
Simplifying Assumptions ◽  
Using Data ◽  
Measured Input ◽  
Data Analysis Methods ◽  
Selection Of

Abstract. We present a review of selected data-analysis methods that are frequently applied in studies of ionospheric electrodynamics and magnetosphere-ionosphere coupling using ground-based and space-based data sets. Our focus is on methods that are data driven (not simulations or statistical models) and can be used in mesoscale studies, where the analysis area is typically some hundreds or thousands of km across. The selection of reviewed methods is such that most combinations of measured input data (electric field, conductances, magnetic field and currents) that occur in practical applications are covered. The techniques are used to solve the unmeasured parameters from Ohm's law and Maxwell's equations, possibly with help of some simplifying assumptions. In addition to reviewing existing data-analysis methods, we also briefly discuss possible extensions that may be used for upcoming data sets.

Sign in / Sign up

Export Citation Format

Share Document