The research in the SIA Unit focuses primarily on the development of computational electron cryo-microscopy and cellular electron cryo-tomography workflows combining innovative computational, artificial intelligence, and data science tools. These tools are combined with advanced experimental techniques developed with our collaborators to bridge information between the atomic and cellular scales, covering more than six orders of magnitude from Ångstroms to tens of microns. Cellular electron cryo-tomography opens the exciting prospect of directly studying molecular interactions and structures at the near-atomic scale, in situ, within the full context of cryogenically preserved, unstained and unprocessed cells. The SIA unit is aiming to provide the means to obtain, evaluate, quantify, and validate this data using transformative computational tools in a close feedback loop with experimental workflows, putting us at the forefront of this rapidly developing field.
The SIA team implements their software algorithms in a python-based framework called pyCoAn. CoAn stands for Correlative Analysis of electron microscopy data. pyCoAn incorporates many analysis algorithms primarily targeted at cellular cryogenic tomography including denoising, segmentation, and pattern recognition. It is similar in concept to Matlab, but tailor-made for Computational Analysis of electron microscopy data. In addition to the software developed in the SIA Unit, pyCoAn also provides seamless access to a multitude of image- and data-processing software packages with a unified interface. This allows the end-user great flexibility in data analysis and allows them to focus on the questions at hand, rather than spending time figuring out how to reformat data from package A into something package B can use.
The package is publically available at GitHub: