Difference between revisions of "2018Heimowitz ApplePicker"
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Heimowitz, A., Anden, J. & Singer, A. APPLE Picker: Automatic Particle Picking, a Low-Effort Cryo-EM Framework. Journal of Structural Biology, 2018, 204(2), 215-227. | Heimowitz, A., Anden, J. & Singer, A. APPLE Picker: Automatic Particle Picking, a Low-Effort Cryo-EM Framework. Journal of Structural Biology, 2018, 204(2), 215-227. | ||
| − | Abstract | + | == Abstract == |
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Particle picking is a crucial first step in the computational pipeline of single-particle cryo-electron microscopy (cryo-EM). Selecting particles from the micrographs is difficult especially for small particles with low contrast. As high-resolution reconstruction typically requires hundreds of thousands of particles, manually picking that many particles is often too time-consuming. While template-based particle picking is currently a popular approach, it may suffer from introducing manual bias into the selection process. In addition, this approach is still somewhat time-consuming. This paper presents the APPLE (Automatic Particle Picking with Low user Effort) picker, a simple and novel approach for fast, accurate, and template-free particle picking. This approach is evaluated on publicly available datasets containing micrographs of beta-galactosidase, T20S proteasome, 70S ribosome and keyhole limpet hemocyanin projections. | Particle picking is a crucial first step in the computational pipeline of single-particle cryo-electron microscopy (cryo-EM). Selecting particles from the micrographs is difficult especially for small particles with low contrast. As high-resolution reconstruction typically requires hundreds of thousands of particles, manually picking that many particles is often too time-consuming. While template-based particle picking is currently a popular approach, it may suffer from introducing manual bias into the selection process. In addition, this approach is still somewhat time-consuming. This paper presents the APPLE (Automatic Particle Picking with Low user Effort) picker, a simple and novel approach for fast, accurate, and template-free particle picking. This approach is evaluated on publicly available datasets containing micrographs of beta-galactosidase, T20S proteasome, 70S ribosome and keyhole limpet hemocyanin projections. | ||
| − | Keywords | + | == Keywords == |
| − | Links | + | |
| + | == Links == | ||
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https://www.ncbi.nlm.nih.gov/pubmed/30134153 | https://www.ncbi.nlm.nih.gov/pubmed/30134153 | ||
| − | Related software | + | == Related software == |
https://github.com/PrincetonUniversity/APPLEpicker | https://github.com/PrincetonUniversity/APPLEpicker | ||
https://github.com/PrincetonUniversity/APPLEpicker-python | https://github.com/PrincetonUniversity/APPLEpicker-python | ||
| − | Related methods | + | == Related methods == |
| − | Comments | + | == Comments == |
Revision as of 15:42, 25 October 2018
Citation
Heimowitz, A., Anden, J. & Singer, A. APPLE Picker: Automatic Particle Picking, a Low-Effort Cryo-EM Framework. Journal of Structural Biology, 2018, 204(2), 215-227.
Abstract
Particle picking is a crucial first step in the computational pipeline of single-particle cryo-electron microscopy (cryo-EM). Selecting particles from the micrographs is difficult especially for small particles with low contrast. As high-resolution reconstruction typically requires hundreds of thousands of particles, manually picking that many particles is often too time-consuming. While template-based particle picking is currently a popular approach, it may suffer from introducing manual bias into the selection process. In addition, this approach is still somewhat time-consuming. This paper presents the APPLE (Automatic Particle Picking with Low user Effort) picker, a simple and novel approach for fast, accurate, and template-free particle picking. This approach is evaluated on publicly available datasets containing micrographs of beta-galactosidase, T20S proteasome, 70S ribosome and keyhole limpet hemocyanin projections.
Keywords
Links
https://www.ncbi.nlm.nih.gov/pubmed/30134153
Related software
https://github.com/PrincetonUniversity/APPLEpicker https://github.com/PrincetonUniversity/APPLEpicker-python
