https://3demmethods.i2pc.es/index.php?action=history&feed=atom&title=2002Frangakis_Template 2026-05-24T20:37:26Z Revision history for this page on the wiki MediaWiki 1.44.2 https://3demmethods.i2pc.es/index.php?title=2002Frangakis_Template&diff=1843&oldid=prev 2009-04-30T06:42:25Z

<p></p> <p><b>New page</b></p><div>== Citation ==<br /> <br /> Frangakis AS, Bohm J, Forster F, Nickell S, Nicastro D, Typke D,<br /> Hegerl R, Baumeister W (2002) Identification of macromolecular<br /> complexes in cryoelectron tomograms of phantom cells. Proc<br /> Natl Acad Sci USA 99:14153–14158<br /> <br /> [http://scholar.google.es/scholar?hl=en&amp;lr=&amp;cites=9982414801760274439 Cited by]<br /> <br /> == Abstract ==<br /> <br /> Electron tomograms of intact frozen-hydrated cells are essentially three-dimensional images of the entire proteome of the cell, and they depict the whole network of macromolecular interactions. However, this information is not easily accessible because of the poor signal-to-noise ratio of the tomograms and the crowded nature of the cytoplasm. Here, we describe a template matching algorithm that is capable of detecting and identifying macromolecules in tomographic volumes in a fully automated manner. The algorithm is based on nonlinear cross correlation and incorporates elements of multivariate statistical analysis. Phantom cells, i.e., lipid vesicles filled with macromolecules, provide a realistic experimental scenario for an assessment of the fidelity of this approach. At the current resolution of approximately 4 nm, macromolecules in the size range of 0.5-1 MDa can be identified with good fidelity.<br /> <br /> == Keywords ==<br /> <br /> Electron tomography, single particles, localization<br /> <br /> == Links ==<br /> <br /> Article http://www.ncbi.nlm.nih.gov/pubmed/12391313<br /> <br /> == Related software ==<br /> <br /> == Related methods ==<br /> <br /> == Comments ==</div>

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