DoG Picker and TiltPicker: software tools to facilitate particle selection in single particle electron microscopy.

By Neil R. Voss, C. Yoshioka, M. Radermacher, Clinton S. Potter, Bridget O. Carragher. 2009

600 citations recorded by the source corpus.

Subfields: Biophysics, Structural Biology

How researchers see this paper

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

In Biophysics

1. Erik Lindahl
2. A. Ward
3. David Baker
4. Sjors H. W. Scheres

In Structural Biology

1. Dari Kimanius
2. Takanori Nakane
3. Erik Lindahl
4. Björn Forsberg
5. W. Hagen
6. Jasenko Zivanov
7. D. Veesler
8. Sjors H. W. Scheres

Citations in the corpus

• Induction of Potent Neutralizing Antibody Responses by a Designed Protein Nanoparticle Vaccine for Respiratory Syncytial Virus (2019, methodology): “Particles were picked from aligned doseweighted micrographs using DoG Picker (Voss et al., 2009).”
• New tools for automated high-resolution cryo-EM structure determination in RELION-3 (2018, methodology): “Our method is related to the approach in DoGpicker ( Voss et al., 2009 ), which uses a Difference-of-Gaussian (DoG) filter.”
• Electron-Microscopy-Based Epitope Mapping Defines Specificities of Polyclonal Antibodies Elicited during HIV-1 BG505 Envelope Trimer Immunization (2018, methodology): “Particles were picked using DoG Picker (Voss et al., 2009) and 2D classes were generated using MSA/MRA (Ogura et al.”
• Glycan Shield and Fusion Activation of a Deltacoronavirus Spike Glycoprotein Fine-Tuned for Enteric Infections (2017, neutral): “Particles were automatically picked using DoGPicker (66).”
• SARS-CoV-2 Infection Depends on Cellular Heparan Sulfate and ACE2 (2020, methodology): “Particles on the micrographs were picked using DogPicker (Voss et al., 2009), stack with a box size of 200 pixels, and 2D classified with RELION 3.0 (Scheres, 2012).”
• DeepPicker: a Deep Learning Approach for Fully Automated Particle Picking in Cryo-EM (2016, methodology): “The unsupervised approaches distinguish the images of particle-like objects from background noise in micrographs via an unsupervised learning manner (i.e., without any labeled training data) [14, 15].”
• Immunogenicity and structures of a rationally designed prefusion MERS-CoV spike antigen (2017, methodology): “CTF estimation was then performed using CTFFind3 (64), and candidate MERS S-2P/G4 projection images were identified using a difference-of-Gaussians approach (61).”
• Prefusion structure of a human coronavirus spike protein (2016, methodology): “Particles were picked from a subset of the data employing a difference-of-Gaussians approach 33 and aligned using reference-free 2D classification employing iterative MRA/MSA using a binning factor of two 34 .”