Jose-Roman Bilbao-Castro

Marker-free image registration of electron tomography tilt-series

BackgroundTilt 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.ResultsIn 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.ConclusionOur 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.

Automatic particle selection from electron micrographs using machine learning techniques

The 3D reconstruction of biological specimens using Electron Microscopy is currently capable of achieving subnanometer resolution. Unfortunately, this goal requires gathering tens of thousands of projection images that are frequently selected manually from micrographs. In this paper we introduce a new automatic particle selection that learns from the user which particles are of interest. The training phase is semi-supervised so that the user can correct the algorithm during picking and specifically identify incorrectly picked particles. By treating such errors specially, the algorithm attempts to minimize the number of false positives. We show that our algorithm is able to produce datasets with fewer wrongly selected particles than previously reported methods. Another advantage is that we avoid the need for an initial reference volume from which to generate picking projections by instead learning which particles to pick from the user. This package has been made publicly available in the open-source package Xmipp.

Exploiting desktop supercomputing for three-dimensional electron microscopy reconstructions using ART with blobs

Three-dimensional electron microscopy allows direct visualization of biological macromolecules close to their native state. The high impact of this technique in the structural biology field is highly correlated with the development of new image processing algorithms. In order to achieve subnanometer resolution, the size and number of images involved in a three-dimensional reconstruction increase and so do computer requirements. New chips integrating multiple processors are hitting the market at a reduced cost. This high-integration, low-cost trend has just begun and is expected to bring real supercomputers to our laboratory desktops in the coming years. This paper proposes a parallel implementation of a computation-intensive algorithm for three-dimensional reconstruction, ART, that takes advantage of the computational power in modern multicore platforms. ART is a sophisticated iterative reconstruction algorithm that has turned out to be well suited for the conditions found in three-dimensional electron microscopy. In view of the performance obtained in this work, these modern platforms are expected to play an important role to face the future challenges in three-dimensional electron microscopy.

Phan3D: design of biological phantoms in 3D electron microscopy

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.

Parallel iterative reconstruction methods for structure determination of biological specimens by electron microscopy

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.

A fast iterative convolution weighting approach for gridding-based direct Fourier three-dimensional reconstruction with correction for the contrast transfer function.

We describe a fast and accurate method for the reconstruction of macromolecular complexes from a set of projections. Direct Fourier inversion (in which the Fourier Slice Theorem plays a central role) is a solution for dealing with this inverse problem. Unfortunately, the set of projections provides a non-equidistantly sampled version of the macromolecule Fourier transform in the single particle field (and, therefore, a direct Fourier inversion) may not be an optimal solution. In this paper, we introduce a gridding-based direct Fourier method for the three-dimensional reconstruction approach that uses a weighting technique to compute a uniform sampled Fourier transform. Moreover, the contrast transfer function of the microscope, which is a limiting factor in pursuing a high resolution reconstruction, is corrected by the algorithm. Parallelization of this algorithm, both on threads and on multiple CPUs, makes the process of three-dimensional reconstruction even faster. The experimental results show that our proposed gridding-based direct Fourier reconstruction is slightly more accurate than similar existing methods and presents a lower computational complexity both in terms of time and memory, thereby allowing its use on larger volumes. The algorithm is fully implemented in the open-source Xmipp package and is downloadable from http://xmipp.cnb.csic.es.

XMSF: Structure-preserving noise reduction and pre-segmentation in microscope tomography

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/

Parameter optimization in 3D reconstruction on a large scale grid

Reaching as high structural resolution as possible in 3D electron microscopy of biological specimens is crucial to understanding their function and interactions. Technical and biological limitations make electron microscopy projections of such specimens quite noisy. Under those circumstances, the broadly used Weighted Back-Projection algorithm presents some limitations for 3D reconstruction. Iterative tomographic reconstruction algorithms are well suited to provide high resolution 3D structures under such noisy conditions. Nevertheless, these iterative algorithms present two major challenges: computational expensiveness and some free parameters which need to be correctly tuned to obtain the best possible resolution. This work applies global optimization techniques to search for the optimal set of parameters and makes use of the high-throughput capabilities of grid computing to perform the required computations. Fault tolerance techniques have been included in our application to deal with the dynamic nature and complexity of large scale computational grids. The approach for parameter optimization presented here has been successfully evaluated in the European EGEE grid, obtaining good levels of speedup, throughput and transfer rates.

On the suitability of Biological structure determination by electron microscopy to Grid Computing

The present contribution describes a potential application of Grid Computing in Bioinformatics. High resolution structure determination of biological specimens is critical in BioSciences to understanding the biological function. The problem is computational intensive. Distributed and Grid Computing are thus becoming essential. This contribution analyzes the use of Grid Computing and its potential benefits in the field of electron microscope tomography of biological specimens.

EGEETomo: a user-friendly, fault-tolerant and grid-enabled application for 3D reconstruction in electron tomography.

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