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

Scheres SH - J. Struct. Biol. (2014)

Bottom Line: Here, a semi-automated particle selection procedure is presented that has been implemented within the open-source software RELION.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.

View Article: PubMed Central - PubMed

Affiliation: MRC Laboratory of Molecular Biology, Cambridge Biomedical Campus, Francis Crick Avenue, Cambridge CB2 0QH, UK. Electronic address: scheres@mrc-lmb.cam.ac.uk.

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The “Einstein-from-noise” pitfall. (A) Class averages for the 15 largest classes (ordered from larger to smaller) after sorting and 2D class averaging of the auto-picked particles that were picked with a threshold of 0.1. Class averages indicated with an asteriks were identified as artificial classes caused by template bias (see Section 5). (B) Examples of particle images assigned to one of the artificial classes: the third class in A. No clear particles are visible. The lower-right image shows the average of all assigned particles in this class without any CTF correction. (C) Examples of particle images assigned to a good class: the first class in A. Particles are clearly visible. The lower-right image shows the average of all assigned particles in this class without any CTF correction. (D) 3D map obtained from 17,082 particles that were assigned to artificial classes.
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f0025: The “Einstein-from-noise” pitfall. (A) Class averages for the 15 largest classes (ordered from larger to smaller) after sorting and 2D class averaging of the auto-picked particles that were picked with a threshold of 0.1. Class averages indicated with an asteriks were identified as artificial classes caused by template bias (see Section 5). (B) Examples of particle images assigned to one of the artificial classes: the third class in A. No clear particles are visible. The lower-right image shows the average of all assigned particles in this class without any CTF correction. (C) Examples of particle images assigned to a good class: the first class in A. Particles are clearly visible. The lower-right image shows the average of all assigned particles in this class without any CTF correction. (D) 3D map obtained from 17,082 particles that were assigned to artificial classes.

Mentions: However, template-based particle picking does come with a potentially dangerous pitfall. As was pointed out recently in a series of comments on a controversial cryo-EM structure of the HIV-1 envelope glycoprotein trimer (Henderson, 2013; van Heel, 2013), using templates to select particles from noisy micrographs may be subject to strong template bias. This was termed “Einstein-from-noise”, in reference to the classical experiment where pure-noise images are aligned to a picture of Einstein in order to reproduce the Einstein image from averaging over noise only, see also (Shatsky et al., 2009). The template-based picking algorithm in RELION does not form an exception to this general problem. To illustrate this, the auto-picking algorithm was re-run on all -galactosidase micrographs, but this time with a much lower threshold of 0.1. This led to 70,942 particles being picked, of which 62,230 were selected after sorting. Reference-free 2D class averaging with these particles revealed several artificial, “Einstein-from-noise” classes (indicated with an asterisk in Fig. 5A). Whereas good classes show high-resolution protein-like features, the artificial classes show merely low-resolution ghosts of the templates with superimposed high-resolution noise. Another noticeable difference between these classes is the angular accuracy that RELION estimates (Scheres, 2012b): for true classes this accuracy is often better than for artificial classes. Analysis of the individual particles that were assigned to the artificial classes shows that they are mostly empty particles (Fig. 5B). Moreover, averaging of these particles (without CTF-correction or masking) shows a black circle around the ghost image of the template. This black circle is the ghost image of the circular mask around the template image, which had slightly negative, i.e. black, values in the background. Particles assigned to good classes are clearly visible in individual images, and averaging over these does not show the black circle (Fig. 5C), which should not be mistaken for the typical black “aura” around an average that has not been CTF-corrected.


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

Scheres SH - J. Struct. Biol. (2014)

The “Einstein-from-noise” pitfall. (A) Class averages for the 15 largest classes (ordered from larger to smaller) after sorting and 2D class averaging of the auto-picked particles that were picked with a threshold of 0.1. Class averages indicated with an asteriks were identified as artificial classes caused by template bias (see Section 5). (B) Examples of particle images assigned to one of the artificial classes: the third class in A. No clear particles are visible. The lower-right image shows the average of all assigned particles in this class without any CTF correction. (C) Examples of particle images assigned to a good class: the first class in A. Particles are clearly visible. The lower-right image shows the average of all assigned particles in this class without any CTF correction. (D) 3D map obtained from 17,082 particles that were assigned to artificial classes.
© Copyright Policy - CC BY
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4318617&req=5

f0025: The “Einstein-from-noise” pitfall. (A) Class averages for the 15 largest classes (ordered from larger to smaller) after sorting and 2D class averaging of the auto-picked particles that were picked with a threshold of 0.1. Class averages indicated with an asteriks were identified as artificial classes caused by template bias (see Section 5). (B) Examples of particle images assigned to one of the artificial classes: the third class in A. No clear particles are visible. The lower-right image shows the average of all assigned particles in this class without any CTF correction. (C) Examples of particle images assigned to a good class: the first class in A. Particles are clearly visible. The lower-right image shows the average of all assigned particles in this class without any CTF correction. (D) 3D map obtained from 17,082 particles that were assigned to artificial classes.
Mentions: However, template-based particle picking does come with a potentially dangerous pitfall. As was pointed out recently in a series of comments on a controversial cryo-EM structure of the HIV-1 envelope glycoprotein trimer (Henderson, 2013; van Heel, 2013), using templates to select particles from noisy micrographs may be subject to strong template bias. This was termed “Einstein-from-noise”, in reference to the classical experiment where pure-noise images are aligned to a picture of Einstein in order to reproduce the Einstein image from averaging over noise only, see also (Shatsky et al., 2009). The template-based picking algorithm in RELION does not form an exception to this general problem. To illustrate this, the auto-picking algorithm was re-run on all -galactosidase micrographs, but this time with a much lower threshold of 0.1. This led to 70,942 particles being picked, of which 62,230 were selected after sorting. Reference-free 2D class averaging with these particles revealed several artificial, “Einstein-from-noise” classes (indicated with an asterisk in Fig. 5A). Whereas good classes show high-resolution protein-like features, the artificial classes show merely low-resolution ghosts of the templates with superimposed high-resolution noise. Another noticeable difference between these classes is the angular accuracy that RELION estimates (Scheres, 2012b): for true classes this accuracy is often better than for artificial classes. Analysis of the individual particles that were assigned to the artificial classes shows that they are mostly empty particles (Fig. 5B). Moreover, averaging of these particles (without CTF-correction or masking) shows a black circle around the ghost image of the template. This black circle is the ghost image of the circular mask around the template image, which had slightly negative, i.e. black, values in the background. Particles assigned to good classes are clearly visible in individual images, and averaging over these does not show the black circle (Fig. 5C), which should not be mistaken for the typical black “aura” around an average that has not been CTF-corrected.

Bottom Line: Here, a semi-automated particle selection procedure is presented that has been implemented within the open-source software RELION.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.

View Article: PubMed Central - PubMed

Affiliation: MRC Laboratory of Molecular Biology, Cambridge Biomedical Campus, Francis Crick Avenue, Cambridge CB2 0QH, UK. Electronic address: scheres@mrc-lmb.cam.ac.uk.

Show MeSH