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| = EMAN2.2 Release Notes= | = EMAN2.2 Release Notes = This is not an all-inclusive list, it includes only the more interesting/useful changes since EMAN2.12 |
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| * Integrates SciPy, Theano, PyLearn and other toolkits * GitHub * Source code is now managed via a public GitHub repository (cryoem/eman2) |
* Integrates !SciPy, Theano, !PyLearn and other toolkits * !GitHub * Source code is now managed via a public !GitHub repository (cryoem/eman2) |
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 look even better than Relion
- 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
- 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
- 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
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
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)