Section: New Software and Platforms

Software for cryo-electron tomography

Participant : Charles Kervrann [(contact)] .

TubuleJ: Straightening of microtubule cryo-EM projection views

The TubuleJ software (APP deposit number: IDDN.FR.001.240023.000.S.P.2011.000.21000) written in java (plug-in ImageJ ) is devoted to the analysis of microtubules and helical structures in 2D cryo-electron microscope images. The software straightens curved microtubule images by estimating automatically points locations on the microtubule axis. The estimation of microtubule principal axis relies on microtubule cylindrical shape analyzed in the Fourier domain. A user-friendly interface enables to filter straight fiber images by selecting manually the layer lines of interest in the Fourier domain. This software can be used to generate a set of 2D projection views from a single microtubule projection view and a few parameters of this microtubule structure. These projection views are then back projected, by using the imod plug-in (http://rsbweb.nih.gov/ij/ ), to reconstruct 3D microtubules.

On-line demo: see http://equipes.igdr.univ-rennes1.fr/en/tips/Software/TubuleJ/ .

Collaborators: Sophie Blestel and Denis Chrétien (UMR 6290, CNRS, University of Rennes 1).

 

Cryo-Seg: Segmentation of tomograms in cryo-electron microscopy

The Cryo-Seg software written in c ++ and java (plug-in mageJ ) has been developed to detect microtubule structures and helical structures in 2D cryo-electron microscope images. Cryo-electron tomography allows 3D observation of biological specimens in their hydrated state. Segmentation is formulated as Maximum A Posteriori estimation problem and exploits image patches to take into account spatial contexts (Markov Random Fields). Because of the contrast anisotropy in the specimen thickness direction, the whole tomogram is segmented section by section, with an automatic update of reference patches. This algorithm has been evaluated on synthetic data and on cryo-electron tomograms of in vitro microtubules [19] . On real data, this segmentation method extracts the most contrasted regions of microtubules, and 3D visualization is improved.

Collaborators: Sophie Blestel and Denis Chrétien (UMR 6290, CNRS-University of Rennes 1).