EMAN2.2 Release Notes

This is not an all-inclusive list, it includes only the more interesting/useful changes since EMAN2.12

Single Particle Analysis

• Many, deep improvements to refinement
  • Substantial refinement changes and new filtering techniques
  • Optional tophat filter (similar to Relion post processing), side chains often (but not always) look even better than Relion/CryoSparc
• Local resolution and filtration
  • can be enabled in refinement to provide local detail appropriate to local resolution
• Several new methods for conformational/compositional heterogeneity
  • Including multi-model refinement with or without alignment, masked particle subtraction, 2-D and 3-D Deep Learning approaches (experimental)
• New bad particle identification strategy
  • Proven to produce better maps in several projects!
• Automatic CTF
  • Used to be several manual steps. Entire process now automated.
  • Easy and fast refinement at progressive resolutions within a single project
• New e2boxer (particle picker)
  • Fixes the problems with the old particle picker
  • New (optional) neural network picker for difficult projects
• Stochastic Gradient Descent initial model generator (experimental)
• Automatic magnification anisotropy correction tool
  • Post-processing program which corrects for the common microscope anisotropy problem on FEI scopes
  • Automatic, does not require additional data collection
• New direct detector movie aligner
  • All new program. Competitive with other alignment programs in quality
  • Workflow for handling movies in EMAN2 projects
• New localweight averager (experimental)
  • excludes "bad" parts of individual particles (overlaps, contamination, etc.)
• New 2-D registration algorithm
  • scales well with box size
  • more accurate going into "refine" alignment (in many cases refine can be skipped)

Subtomogram Averaging

• New subtomogram averaging tools
  • New pipelines for subtomogram averaging and classification
• Up to 20x faster 3-D alignments,
  • now practical to study 10,000 300x300x300 particles on a single workstation
• New automatic missing wedge identification/compensation in alignment/averaging

Tomogram Segmentation

• Workflow for semi-automatic tomogram annotation/segmentation
  • Uses convolutional neural network technology with user guided training of features.

Overall Changes

• Anaconda Python based distribution

  • Integrates SciPy, Theano, PyLearn and other toolkits

• New installers, with better OpenMPI/Pydusa support

• GitHub

  • Source code is now managed via a public GitHub repository (cryoem/eman2)

• Windows 10/7 64 bit
  • Initial support, poorly tested, but available for the first time (EMAN2 only, SPARX/SPHIRE do not support this platform)