SUMMARY: Interpretation of electron tomograms is difficult due to the high noise levels. Thus, denoising techniques are needed to improve the signal-to-noise ratio. XMSF (Microscopy Mean Shift Filtering) is a fast, user-friendly application that succeeds in filtering noise while preserving the structures of interest. It is based on the extension to 3D of a method widely applied in other image processing fields under very different scenarios. XMSF has been tested for a variety of tomograms, showing a great potential to become a state-of-the-art filtering program in electron tomography. Applied iteratively, the algorithm yields pre-segmented volumes facilitating posterior segmentation tasks. Moreover, execution times remain low thanks to parallel computing techniques to exploit current multicore computers. AVAILABILITY: http://sites.google.com/site/xmsfilter/

BACKGROUND: Tilt series are commonly used in electron tomography as a means of collecting three-dimensional information from two-dimensional projections. A common problem encountered is the projection alignment prior to 3D reconstruction. Current alignment techniques usually employ gold particles or image derived markers to correctly align the images. When these markers are not present, correlation between adjacent views is used to align them. However, sequential pairwise correlation is prone to bias and the resulting alignment is not always optimal. RESULTS: In this paper we introduce an algorithm to find regions of the tilt series which can be tracked within a subseries of the tilt series. These regions act as landmarks allowing the determination of the alignment parameters. We show our results with synthetic data as well as experimental cryo electron tomography. CONCLUSION: Our algorithm is able to correctly align a single-tilt tomographic series without the help of fiducial markers thanks to the detection of thousands of small image patches that can be tracked over a short number of images in the series.

UNLABELLED: Electron tomography is the leading technique to elucidate the structure of complex biological specimens. Due to the resolution needs, huge reconstructions are required. Grid computing has the potential to face the significant computational demands involved. However, there are a number of key issues, such as stability or difficult user-grid interaction, that currently preclude fully exploitation of its potential. EGEETomo is a user-friendly application that facilitates the interaction with the grid for the non-specialized user and automates job submission and supervision. In addition, EGEETomo is supplied with an automated fault recovery mechanism, which is key to make all the work transparent to the user. EGEETomo significantly accelerates tomographic reconstruction by exploiting the computational resources in the EGEE grid with minimal user intervention. AVAILABILITY: http://www.ace.ual.es/~jrbcast/EGEETomo.tar.gz

Three-Dimensional Electron Microscopy (3DEM) has turned out to be a major player in structural proteomics. In 3DEM, tens of thousands of EM images from a macromolecule at different views are combined to derive its structure by means of a 3D reconstruction algorithm. However, for an optimal reconstruction, the most suitable algorithm has to be used and its parameters have to be tuned for the macromolecule under study and the experimental conditions found. The use of phantoms is central to objective comparison of reconstruction algorithms and optimization of their parameters. Phan3D is a tool intended to provide a fully visual and interactive environment that facilitates the design of phantoms resembling biological specimens in 3DEM. Phan3D has been developed as an alternative to the tedious, error-prone and old-fashioned phantom design that is based on hand-written text description files.

This work addresses the application of blob-based series expansion methods in structure determination of macromolecules from the perspective of computational speed and convergence rate. This work explores the use of traditional series expansion methods, such as algebraic reconstruction techniques, as well as other very recently developed methods: such as averaging sequential strings or component averaging methods characterized by a really fast convergence, achieving least-squares solutions in a few iterations.