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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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Particle selection for the KLH data. (A) The ten reference-free class averages (ordered from larger to smaller classes) that were calculated from the manually selected particles. The two classes indicated with an asterisk were selected as templates for the auto-picking. (B) Curves of precision, recall and false discovery rate against the pick threshold. A picking threshold of 0.3 was chosen. (C) The 15 particles with the highest average Z-scores after sorting. (D) The 15 largest classes (ordered from larger to smaller) after 2D class averaging of the auto-picked particles. Particles assigned to the classes indicated with an asterisk were selected for subsequent 3D refinement. (E) 3D map after refinement of the semi-automatically selected particles from the combined near-to-focus (NTF) and far-from-focus (FFF) KLH data sets.
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f0015: Particle selection for the KLH data. (A) The ten reference-free class averages (ordered from larger to smaller classes) that were calculated from the manually selected particles. The two classes indicated with an asterisk were selected as templates for the auto-picking. (B) Curves of precision, recall and false discovery rate against the pick threshold. A picking threshold of 0.3 was chosen. (C) The 15 particles with the highest average Z-scores after sorting. (D) The 15 largest classes (ordered from larger to smaller) after 2D class averaging of the auto-picked particles. Particles assigned to the classes indicated with an asterisk were selected for subsequent 3D refinement. (E) 3D map after refinement of the semi-automatically selected particles from the combined near-to-focus (NTF) and far-from-focus (FFF) KLH data sets.

Mentions: The resulting average Z-scores may then be used for displaying the particles in a sorted manner. Particles with a high average Z-score are often high-contrast false positives such as ice, protein aggregates, carbon edges or pieces of junk, e.g. Fig. 3C. Therefore, visual inspection of the particles on the high-end of the sorted average Z-scores may be a more efficient way of getting rid of bad particles than inspecting the entire data set.


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

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

Particle selection for the KLH data. (A) The ten reference-free class averages (ordered from larger to smaller classes) that were calculated from the manually selected particles. The two classes indicated with an asterisk were selected as templates for the auto-picking. (B) Curves of precision, recall and false discovery rate against the pick threshold. A picking threshold of 0.3 was chosen. (C) The 15 particles with the highest average Z-scores after sorting. (D) The 15 largest classes (ordered from larger to smaller) after 2D class averaging of the auto-picked particles. Particles assigned to the classes indicated with an asterisk were selected for subsequent 3D refinement. (E) 3D map after refinement of the semi-automatically selected particles from the combined near-to-focus (NTF) and far-from-focus (FFF) KLH data sets.
© Copyright Policy - CC BY
Related In: Results  -  Collection

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

f0015: Particle selection for the KLH data. (A) The ten reference-free class averages (ordered from larger to smaller classes) that were calculated from the manually selected particles. The two classes indicated with an asterisk were selected as templates for the auto-picking. (B) Curves of precision, recall and false discovery rate against the pick threshold. A picking threshold of 0.3 was chosen. (C) The 15 particles with the highest average Z-scores after sorting. (D) The 15 largest classes (ordered from larger to smaller) after 2D class averaging of the auto-picked particles. Particles assigned to the classes indicated with an asterisk were selected for subsequent 3D refinement. (E) 3D map after refinement of the semi-automatically selected particles from the combined near-to-focus (NTF) and far-from-focus (FFF) KLH data sets.
Mentions: The resulting average Z-scores may then be used for displaying the particles in a sorted manner. Particles with a high average Z-score are often high-contrast false positives such as ice, protein aggregates, carbon edges or pieces of junk, e.g. Fig. 3C. Therefore, visual inspection of the particles on the high-end of the sorted average Z-scores may be a more efficient way of getting rid of bad particles than inspecting the entire data set.

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