zorroautomator 0.7.4b0

Automator

The Automator for Zorro and 2dx is a GUI interface to Zorro.

It has the following additional dependencies:

• PySide

Automator also comes with the Skulk Manager tool which may be used as a daemon to watch a directory for new image stacks and automatically process them.

Automator is LGPL license.

Feature List

• Import: DM4, MRC, HDF5, stacked TIFF

• Apply gain reference to SerialEM 4/8-bit MRC stacks.

• Fourier cropping of super-resolution stacks.

• Can operate on Sun Grid Engine cluster.

• Flexible filters: dose filtering, low-pass filtering

• Stochastic hot pixel filter detects per-stack radiation damage to the detector

• CTF estimation with: CTFFIND4.1, GCTF

• Particle picking with: Gautomatch (alpha-status)

• Independent (separate even-odd frame) and non-independent FRC resolution estimators.

• Archiving with: 7z, pigz, lbzip2

• Output of diagnostic PNGs

Citations

McLeod, R.A., Kowal, J., Ringler, P., Stahlberg, H. 2017. Robust image alignment for cryogenic transmission electron microscopy. J. Struct. Biol. 197:279-293.

http://www.sciencedirect.com/science/article/pii/S1047847716302520

Zorro and Automator make use of or interface with the following 3rd party programs:

CTF estimation CTFFIND4.1:

Rohou, A., Grigorieff, N., 2015. CTFFIND4: Fast and accurate defocus estimation from electron micrographs. Journal of Structural Biology, Recent Advances in Detector Technologies and Applications for Molecular TEM 192, 216-221. doi:10.1016/j.jsb.2015.08.008

CTF estimation from GCTF:

Zhang, K., 2016. Gctf: Real-time CTF determination and correction. Journal of Structural Biology 193, 1-12. doi:10.1016/j.jsb.2015.11.003

4/8-bit MRC from SerialEM:

Mastronarde, D.N. 2005. Automated electron microscope tomography using robust prediction of specimen movements. J. Struct. Biol. 152:36-51.

Zorro’s dose filter is ported from Unblur:

Grant, T., Grigorieff, N., 2015. Measuring the optimal exposure for single particle cryo-EM using a 2.6 Å reconstruction of rotavirus VP6. eLife Sciences e06980. doi:10.7554/eLife.06980