EMAN2 Release Notes
Changes in the 2.91 release include
EMAN2 changes
- Full PPPT subtomogram averaging pipeline capable of near-atomic resolution
- Numerous improvements for in-situ subtomogram averaging
- New dedicated deep-learning tomography particle picker
- Simultaneous (visual) picking of multiple macromolecular species
- New options for tilt series alignment in tricky cases
- Data compression support for both CryoEM and CryoET
- Native HDF5 mechanism
- lossless or lossy with user-selectable bit count
- Files still readable by Chimera
- No quality/resolution loss
- Typical 10-20x size reduction, dramatically improves disk I/O and network transfer
- Extensive work on e2boxer
- improved deep learning picker
- Fixed problems with reference-based and local pickers
- Added simple inline instructions
- Deep learning GMM for single particle variability studies (e2gmm.py, see arxiv paper, experimental)
- New local resolution and filtration based on mFSC (Penczek), for SPA and SPT (iterative)
- New visualization options for volume stacks
Support for EER format & oversampling (counting-mode Falcon 4 images)
- Colored isosurfaces functioning, and automatic local resolution display when computed
- Speed improvements for image I/O operations
- Better integration with Numpy and Jupyter image object data sharing (advanced users)
- Improved option for x/y/z projections in e2display
- Drag and drop support for rearranging images in tiled image display
- Python 3 based (finally!)
Changes in the 2.31 release include
EMAN2 changes
- General changes:
- New browser options for display of stacks of 3-D volumes
- RCTboxer works properly again
- Fixed a problem reading A/pix from FEI-style MRC files
- New Processor for bit-compression of image files (makes images more compressible)
- PPPT Subtomogram averaging:
- Automatic CTF based handedness checking for tomograms
Focused refinement https://blake.bcm.edu/emanwiki/EMAN2/e2tomo_more#Focused_refinement
- Better parallelism
- Slightly improved tilt series alignment (resolution improvement)
SPHIRE 1.3 changes
- Support for processing helical specimens
- AutoSPHIRE - automatic refinement tool
- Integration of crYOLO
- Cinderella for 2D class selection
Changes in the 2.3 release include
- A complete CryoET pipeline from tilt series through subnanometer resolution hybrid subtomogram averaging
- fiducial-less fully automated tilt series alignment (also works with fiducials)
- rapid tiled Fourier reconstruction
- full tilt/geometry aware CTF correction
- multi class 3-D particle picker with new ties to deep-learning annotation
- SGD automatic initial model generation
- traditional 3-D subtomogram averaging
- per-particle per-tilt hybrid subtomogram/single particle reconstruction to subnanometer resolution
- A new switchable filter in e2boxer making it dramatically easier to distinguish particles in images
- New improvements to bispectral classification for 2-D unsupervised classification and 3-D refinement
- Focused classification in 3-D refinement available from the workflow interface
- Improvements to e2extractsubparticles as an alternative to focused classification
- Upgrade from Qt4 to Qt5 as part of the process of transitioning to Python3 over the next year
- Many minor bugfixes
Changes in the 2.21a release include
- Fix bug causing "missing SNR" problem during refinement in specific situations
- Testing support for bispectrum based class-averaging and 3-D refinement. ~10-50x faster. Better 2-D class averages
- Better support for GPU in Neural Network tomogram segmentation and particle picking
- Direct support for phase plates in CTF correction with adjustable phase slider and autofitting (first version, room for improvement). Issues with astigmatism in this version.
- Better GUI display of CTF and Astigmatism
- e2symsearch3d bugs fixed
- Many subtomogram averaging bugs fixed. New pipeline under development.
- Many improvements to e2evalrefine for particle and class-average assessment
Changes from EMAN 2.12 -> EMAN 2.2
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
- Phase plate CTF correction
- Supports phase shifts covering full 360 degree range, with explicit 'phase' slider
- No automatic fitting of phase shift in this version (next minor release)
- 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)