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.

Marker-free image registration of electron tomography tilt-series

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.

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.

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

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

XMIPP: a new generation of an open-source image processing package for electron microscopy

X-windows based microscopy image processing package (Xmipp) is a specialized suit of image processing programs, primarily aimed at obtaining the 3D reconstruction of biological specimens from large sets of projection images acquired by transmission electron microscopy. This public-domain software package was introduced to the electron microscopy field eight years ago, and since then it has changed drastically. New methodologies for the analysis of single-particle projection images have been added to classification, contrast transfer function correction, angular assignment, 3D reconstruction, reconstruction of crystals, etc. In addition, the package has been extended with functionalities for 2D crystal and electron tomography data. Furthermore, its current implementation in C++, with a highly modular design of well-documented data structures and functions, offers a convenient environment for the development of novel algorithms. In this paper, we present a general overview of a new generation of Xmipp that has been re-engineered to maximize flexibility and modularity, potentially facilitating its integration in future standardization efforts in the field. Moreover, by focusing on those developments that distinguish Xmipp from other packages available, we illustrate its added value to the electron microscopy community.

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.