2018Heimowitz ApplePicker - Revision history

Amit Singer at 15:45, 25 October 2018

2018-10-25T15:45:02Z

← Older revision		Revision as of 15:45, 25 October 2018
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	== Citation ==		== 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.		Heimowitz, A., Andén, 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 ==

Amit Singer at 15:42, 25 October 2018

2018-10-25T15:42:58Z

← Older revision		Revision as of 15:42, 25 October 2018
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	https://github.com/PrincetonUniversity/APPLEpicker		https://github.com/PrincetonUniversity/APPLEpicker

	https://github.com/PrincetonUniversity/APPLEpicker-python		https://github.com/PrincetonUniversity/APPLEpicker-python

Amit Singer at 15:42, 25 October 2018

2018-10-25T15:42:37Z

← Older revision		Revision as of 15:42, 25 October 2018
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	Citation		== 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.		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 ==

	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 ==

	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 ==

Amit Singer: Created page with "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...."

2018-10-25T15:40:50Z

Created page with "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...."

New page

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

Related methods

Comments