Automated Particle Picking based on Correlation Peak Shape Analysis and Iterative Classification
Cryo-electron microscopy (CEM) in combination with
single particle analysis (SPA) is a widely used technique for
elucidating structural details of macromolecular assemblies at closeto-
atomic resolutions. However, development of automated software
for SPA processing is still vital since thousands to millions of
individual particle images need to be processed. Here, we present our
workflow for automated particle picking. Our approach integrates
peak shape analysis to the classical correlation and an iterative
approach to separate macromolecules and background by
classification. This particle selection workflow furthermore provides
a robust means for SPA with little user interaction. Processing
simulated and experimental data assesses performance of the
presented tools.
[1] Frank, J., Three-dimensional electron microscopy of macromolecular
assemblies: visualization of biological molecules in their native state.
2006: Oxford University Press.
[2] Adiga, P.S., et al., A binary segmentation approach for boxing ribosome
particles in cryo EM micrographs. J Struct Biol, 2004. 145(1-2): p. 142-
51.
[3] Adiga, U., et al., Particle picking by segmentation: a comparative study
with SPIDER-based manual particle picking. J Struct Biol, 2005.
152(3): p. 211-20.
[4] Sander, B., M.M. Golas, and H. Stark, Advantages of CCD detectors for
de novo three-dimensional structure determination in single-particle
electron microscopy. J Struct Biol, 2005. 151(1): p. 92-105.
[5] Nickell, S., et al., Automated cryoelectron microscopy of "single
particles" applied to the 26S proteasome. FEBS Lett, 2007. 581(15): p.
2751-6.
[6] Korinek, A., et al., Computer controlled cryo-electron microscopy -
TOM(2) a software package for high-throughput applications. J Struct
Biol, 2011. 175(3): p. 394-405.
[7] Zhu, Y., et al., Automatic particle detection through efficient Hough
transforms. IEEE Trans Med Imaging, 2003. 22(9): p. 1053-62.
[8] Sigworth, F.J., Classical detection theory and the cryo-EM particle
selection problem. J Struct Biol, 2004. 145(1-2): p. 111-22.
[9] Zhu, Y., et al., Automatic particle selection: results of a comparative
study. J Struct Biol, 2004. 145(1-2): p. 3-14.
[10] Roseman, A.M., Particle finding in electron micrographs using a fast
local correlation algorithm. Ultramicroscopy, 2003. 94(3-4): p. 225-36.
[11] Kumar, B.V., A. Mahalanobis, and R.D. Juday, Correlation Pattern
Recognition. 2005: Cambridge University Press.
[12] Russel, S.J. and P. Norwig, Artificial Intelligence. 1995: Prentice Hall.
[13] Nicholson, W.V. and R.M. Glaeser, Review: automatic particle
detection in electron microscopy. J Struct Biol, 2001. 133(2-3): p. 90-
101.
[14] Caprari, R.S., Method of target detection in images by moment analysis
of correlation peaks. Appl Opt, 1999. 38(8): p. 1317-24.
[15] Volkmann, N., An approach to automated particle picking from electron
micrographs based on reduced representation templates. J Struct Biol,
2004. 145(1-2): p. 152-6.
[16] Woolford, D., et al., SwarmPS: rapid, semi-automated single particle
selection software. J Struct Biol, 2007. 157(1): p. 174-88.
[17] Duda, R.O., P.E. Hart, and D.G. Stork, Pattern Classification. 2. ed.
2000: Wiley-Interscience.
[18] Runkler, T.A., Information Mining. 2000: Vieweg.
[19] Forsyth, D.A. and J. Ponce, Computer Vision - A modern approach.
2003: Pearson Studium.
[20] Nickell, S., et al., TOM software toolbox: acquisition and analysis for
electron tomography. J Struct Biol, 2005. 149(3): p. 227-34.
[21] Forster, F., et al., Classification of cryo-electron sub-tomograms using
constrained correlation. J Struct Biol, 2008. 161(3): p. 276-86.
[22] Nickell, S., et al., Structural analysis of the 26S proteasome by
cryoelectron tomography. Biochem Biophys Res Commun, 2007.
353(1): p. 115-20.
[23] Scheres, S.H., et al., Image processing for electron microscopy singleparticle
analysis using XMIPP. Nat Protoc, 2008. 3(6): p. 977-90.
[24] Saxton, W.O. and W. Baumeister, The correlation averaging of a
regularly arranged bacterial cell envelope protein. J Microsc, 1982.
127(Pt 2): p. 127-138.
[25] Short, J.M., SLEUTH--a fast computer program for automatically
detecting particles in electron microscope images. J Struct Biol, 2004.
145(1-2): p. 100-10.
[26] Bishop, C.M., Pattern recognition and machine learning. 2009:
Springer.
[27] Bohn, S., et al., Structure of the 26S proteasome from
Schizosaccharomyces pombe at subnanometer resolution. Proc Natl
Acad Sci U S A, 2010. 107(49): p. 20992-7.
[1] Frank, J., Three-dimensional electron microscopy of macromolecular
assemblies: visualization of biological molecules in their native state.
2006: Oxford University Press.
[2] Adiga, P.S., et al., A binary segmentation approach for boxing ribosome
particles in cryo EM micrographs. J Struct Biol, 2004. 145(1-2): p. 142-
51.
[3] Adiga, U., et al., Particle picking by segmentation: a comparative study
with SPIDER-based manual particle picking. J Struct Biol, 2005.
152(3): p. 211-20.
[4] Sander, B., M.M. Golas, and H. Stark, Advantages of CCD detectors for
de novo three-dimensional structure determination in single-particle
electron microscopy. J Struct Biol, 2005. 151(1): p. 92-105.
[5] Nickell, S., et al., Automated cryoelectron microscopy of "single
particles" applied to the 26S proteasome. FEBS Lett, 2007. 581(15): p.
2751-6.
[6] Korinek, A., et al., Computer controlled cryo-electron microscopy -
TOM(2) a software package for high-throughput applications. J Struct
Biol, 2011. 175(3): p. 394-405.
[7] Zhu, Y., et al., Automatic particle detection through efficient Hough
transforms. IEEE Trans Med Imaging, 2003. 22(9): p. 1053-62.
[8] Sigworth, F.J., Classical detection theory and the cryo-EM particle
selection problem. J Struct Biol, 2004. 145(1-2): p. 111-22.
[9] Zhu, Y., et al., Automatic particle selection: results of a comparative
study. J Struct Biol, 2004. 145(1-2): p. 3-14.
[10] Roseman, A.M., Particle finding in electron micrographs using a fast
local correlation algorithm. Ultramicroscopy, 2003. 94(3-4): p. 225-36.
[11] Kumar, B.V., A. Mahalanobis, and R.D. Juday, Correlation Pattern
Recognition. 2005: Cambridge University Press.
[12] Russel, S.J. and P. Norwig, Artificial Intelligence. 1995: Prentice Hall.
[13] Nicholson, W.V. and R.M. Glaeser, Review: automatic particle
detection in electron microscopy. J Struct Biol, 2001. 133(2-3): p. 90-
101.
[14] Caprari, R.S., Method of target detection in images by moment analysis
of correlation peaks. Appl Opt, 1999. 38(8): p. 1317-24.
[15] Volkmann, N., An approach to automated particle picking from electron
micrographs based on reduced representation templates. J Struct Biol,
2004. 145(1-2): p. 152-6.
[16] Woolford, D., et al., SwarmPS: rapid, semi-automated single particle
selection software. J Struct Biol, 2007. 157(1): p. 174-88.
[17] Duda, R.O., P.E. Hart, and D.G. Stork, Pattern Classification. 2. ed.
2000: Wiley-Interscience.
[18] Runkler, T.A., Information Mining. 2000: Vieweg.
[19] Forsyth, D.A. and J. Ponce, Computer Vision - A modern approach.
2003: Pearson Studium.
[20] Nickell, S., et al., TOM software toolbox: acquisition and analysis for
electron tomography. J Struct Biol, 2005. 149(3): p. 227-34.
[21] Forster, F., et al., Classification of cryo-electron sub-tomograms using
constrained correlation. J Struct Biol, 2008. 161(3): p. 276-86.
[22] Nickell, S., et al., Structural analysis of the 26S proteasome by
cryoelectron tomography. Biochem Biophys Res Commun, 2007.
353(1): p. 115-20.
[23] Scheres, S.H., et al., Image processing for electron microscopy singleparticle
analysis using XMIPP. Nat Protoc, 2008. 3(6): p. 977-90.
[24] Saxton, W.O. and W. Baumeister, The correlation averaging of a
regularly arranged bacterial cell envelope protein. J Microsc, 1982.
127(Pt 2): p. 127-138.
[25] Short, J.M., SLEUTH--a fast computer program for automatically
detecting particles in electron microscope images. J Struct Biol, 2004.
145(1-2): p. 100-10.
[26] Bishop, C.M., Pattern recognition and machine learning. 2009:
Springer.
[27] Bohn, S., et al., Structure of the 26S proteasome from
Schizosaccharomyces pombe at subnanometer resolution. Proc Natl
Acad Sci U S A, 2010. 107(49): p. 20992-7.
@article{"International Journal of Engineering, Mathematical and Physical Sciences:63721", author = "Hrabe Thomas and Beck Florian and Nickell Stephan", title = "Automated Particle Picking based on Correlation Peak Shape Analysis and Iterative Classification", abstract = "Cryo-electron microscopy (CEM) in combination with
single particle analysis (SPA) is a widely used technique for
elucidating structural details of macromolecular assemblies at closeto-
atomic resolutions. However, development of automated software
for SPA processing is still vital since thousands to millions of
individual particle images need to be processed. Here, we present our
workflow for automated particle picking. Our approach integrates
peak shape analysis to the classical correlation and an iterative
approach to separate macromolecules and background by
classification. This particle selection workflow furthermore provides
a robust means for SPA with little user interaction. Processing
simulated and experimental data assesses performance of the
presented tools.", keywords = "Cryo-electron Microscopy, Single Particle Analysis,
Image Processing.", volume = "6", number = "1", pages = "111-7", }