National Center for Macromolecular Imaging

Segger

Segger is a tool for segmenting 3D density maps obtained using cryo-electron microscopy (CryoEM). It is a plugin for UCSF Chimera.

  • The main UI for Segger is the Segment Map dialog which can be launched from Tools - Volume Data.
    • The segmentation approach is based on watershed method, a non-parametric clustering technique related to mean-shift clustering. Unlike parametric techniques (e.g. k-means clustering), the number of clusters does not need to be known at the start. Also, the resulting regions are not dependent on any other input.
    • While this method produces many small regions in noisy and high-resolution maps, this is overcome by grouping of regions using scale-space filtering.
    • Grouping information is stored hierarchically, so grouped regions can be interactively ungrouped and regrouped to adjust the results.
  • The Fit to Segments dialog allows the use of segmentations to rigidly dock atomic structures into a density map, and can also show docking statistics such as Z-score for determining confidence of fit.

Highlights:

  • April 2017 - Paper in Elife on AAA+ unfoldase which cites Segger. Cryo-EM is shedding light on amazing molecular mechanisms.
  • May 2017 - Paper in Science uses Segger to segment conformations of the cardiac ryanodine receptor.
  • June 2016 - Poster on SGIV, a large virus with multiple icosahedral layers, segmented using Segger, presented at Gordon Research Conference in Hong Kong.
  • May 2016 - A paper in Nature on how neurotransmitters work - nice segmentations using Segger.
  • February 2016 - We published a paper on a new P22 virion asymmetric reconstruction, analyzed using some new extensions in Segger (see below).
  • November 2015 - Segger was presented at the NCMI 2015 Workshop
  • October 2015 - Update to v.1.9.2 (in Chimera Daily Build Oct 20 or later) includes some new tools: radial segmentation (rSeg), loop modeling using segmented regions (SegLoop), probabilistic modeling from multiple flexible fitting results (ProMod).
  • September 2015 - Segger is cited in a Nature paper on eIF3, where segmented densities are used for focused classification.
  • March 2015 - We used Segger to segment a Nuclear Receptor complex. Challenging at low resolutions, had to use many maps and antibodies to identify various parts with certainty. PMC
  • June 2014 - Poster at Gordon Research Conference on Cryo-EM, Girona, Spain.
  • January 2013 - Paper introducing Segger is one of the top cited papers between 2009-2013 in Journal of Structural Biology. Now at ~150 and counting.
  • October 2013 - Segger presented at NCMI workshop on Cryo-EM methods.
  • August 2012 - Segger presented at The 6th Brazil School for Single Particle Cryo-EM, Socorro, Brazil.

Tutorials:

  1. Segment Map
  2. Fit to Segments
  3. Segmenting with Fitted Models
  4. Docking Scores
  5. Resolution of Subcomponents (Extract)
  6. Radial/Icosahedral Segmentation (rSeg)
  7. Loop modeling (SegLoop)
  8. Probabilistic Modeling (ProMod)


Documentation at Chimera site:


Videos:

Publications:

  • Resolution and Probabilistic Structural Models of Subcomponents Derived from CryoEM Maps of Mature P22 Bacteriophage.
    G. Pintilie, D.H. Chen, C.A. Haase-Pettingell, J.A. King, W. Chiu
    • Biophys. J., vol. 110, no. 4, pp. 827-839, Feb. 2016. PubMed
    • This paper describes the application of Segger, Extract, rSeg, SegLoop, and ProMod for the analysis of an asymmetric reconstruction of the P22 Bacteriophage, leading to some new insights into the structure and function of its various subcomponents - tail, hub, portal, coat, DNA.
  • Comparison of Segger and other methods for segmentation and rigid-body docking of molecular components in cryo-EM density maps.
    G. Pintilie, W. Chiu
    • Biopolymers, vol 97(9), Sep. 2012, pp. 742-60 Pubmed
  • Quantitative analysis of cryo-EM density map segmentation by watershed and scale-space filtering, and fitting of structures by alignment to regions
    G. Pintilie, J. Zhang, T. Goddard, W. Chiu, D. Gossard
  • Identifying Components in 3D Density Maps of Protein Nanomachines by Multi-scale Segmentation
    G. Pintilie, J. Zhang, W. Chiu, D. Gossard
    • In Life Science Systems and Applications Workshop, IEEE/NIH, pp. 44-47, 2009, IEEExplore DSpace


Examples:


For questions, comments, etc., email pintiliebcm.edu