Clustering and variance maps for cryo-electron tomography using wedge-masked differences.

ABSTRACT: Cryo-electron tomography provides 3D imaging of frozen hydrated biological samples with nanometer resolution. Reconstructed volumes suffer from low signal-to-noise-ratio (SNR)(1) and artifacts caused by systematically missing tomographic data. Both problems can be overcome by combining multiple subvolumes with varying orientations, assuming they contain identical structures. Clustering (unsupervised classification) is required to ensure or verify population homogeneity, but this process is complicated by the problems of poor SNR and missing data, the factors that led to consideration of multiple subvolumes in the first place. Here, we describe a new approach to clustering and variance mapping in the face of these difficulties. The combined subvolume is taken as an estimate of the true subv

SUBMITTER: Heumann JM 

PROVIDER: S-EPMC3150390 | biostudies-literature | 2011 Sep

REPOSITORIES: biostudies-literature