Biomedical subjects

Zeyun Yu

Publications and source records attributed to Zeyun Yu.

4 recordsLinked to original sources

PubMed2006

Automated segmentation of molecular subunits in electron cryomicroscopy density maps.

Electron cryomicroscopy (cryoEM) is capable of imaging large macromolecular machines composed of multiple components. However, it is currently only possible to achieve moderate resolution at which it may be possible to computationally extract the individual components in the machine. In this work, we present application details of an automated method for detecting and segmenting the components of a large machine in an experimentally determined density map. This method is applicable to object with and without symmetry and takes advantage of global and local symmetry axes if present. We have applied this segmentation algorithm to several cryoEM data sets already deposited in EMDB with various complexities, symmetries and resolutions and validated the results using manually segmented density and available structures of the components in the PDB. As such, automated segmentation could become a useful tool for the analysis of the ever-increasing number of structures of macromolecular machines derived from cryoEM.

Bacteriophage P22↗

PubMed2005

Automatic ultrastructure segmentation of reconstructed cryoEM maps of icosahedral viruses.

We present an automatic algorithm to segment all the local and global asymmetric units of a three-dimensional density map of icosahedral viruses. This approach is readily applicable to the structural analysis of a broad range of virus structures that are reconstructed using cryo-electron microscopy (cryo-EM) technique. Our algorithm includes three major steps operating on the three dimensional density map: the detection of critical points of the volumetric density function, the detection of global and local symmetry axes, and, finally, the boundary segmentation of all the asymmetric units. We demonstrate the efficacy of our algorithm and report our results on several experimental volumetric datasets, consisting of both reconstructed cryo-EM molecular density maps taken from the European Bioinformatics Institute archive, as well our own synthetically generated (blurred) maps calculated from X-ray resolution molecular structural data taken from the Protein Data Bank.

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PubMedDate not supplied

Volumetric feature extraction and visualization of tomographic molecular imaging.

Electron tomography is useful for studying large macromolecular complex within their cellular context. The associate problems include crowding and complexity. Data exploration and 3D visualization of complexes require rendering of tomograms as well as extraction of all features of interest. We present algorithms for fully automatic boundary segmentation and skeletonization, and demonstrate their applications in feature extraction and visualization of cell and molecular tomographic imaging. We also introduce an interactive volumetric exploration and visualization tool (Volume Rover), which encapsulates implementations of the above volumetric image processing algorithms, and additionally uses efficient multi-resolution interactive geometry and volume rendering techniques for interactive visualization.

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PubMedDate not supplied

Detecting circular and rectangular particles based on geometric feature detection in electron micrographs.

Accurate and automatic particle detection from cryo-electron microscopy (cryo-EM images) is very important for high-resolution reconstruction of large macromolecular structures. In this paper, we present a method for particle picking based on shape feature detection. Two fundamental concepts of computational geometry, namely, the distance transform and the Voronoi diagram, are used for detection of critical features as well as for accurate location of particles from the images or micrographs. Unlike the conventional template-matching methods, our approach detects the particles based on their boundary features instead of intensities. The geometric features derived from the boundaries provide an efficient way for locating particles quickly and accurately, which avoids a brute-force searching for the best position/orientation. Our approach is fully automatic and has been successfully applied to detect particles with approximately circular or rectangular shapes (e.g., KLH particles). Particle detection can be enhanced by multiple sets of parameters used in edge detection and/or by anisotropic filtering. We also discuss the extension of this approach to other types of particles with certain geometric features.

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