Cryo-electron tomography (CET) is a three-dimensional imaging technique for structural studies of macromolecules under close-to-native conditions. In-depth analysis of macromolecule populations depicted in a tomograms requires identification of subtomograms corresponding to putative particles, averaging of subtomograms to enhance their signal, and classification to capture the structural variations among them. Here, we introduce the open-source platform PyTom that unifies standard tomogram processing steps in a single python-based toolbox. For subtomogram averaging, we implemented an adaptive adjustment of scoring and sampling that clearly improves the resolution of averages compared to static strategies. Furthermore, we present a novel stochastic classification method that yields significantly more accurate classification results than two deterministic approaches in simulations. Finally, we demonstrate that the PyTom workflow yields faithful results for the analysis of 60S ribosomes in yeast cell lysate.
Download: PyTom on GitHub
Usage: PyTom contains a fairly extensive documentation including a tutorial, which can be found in this wiki:
Other useful things:
- A little hack into UCSF Chimera to interactively classify particles detected in a tomogram volumedialog.py.gz. The file needs to be copied into your Chimera directory. For example, on a Mac it would be located in: