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dc.contributor.authorScheres, Sjors H. W.-
dc.contributor.authorValle, Mikel-
dc.contributor.authorGrob, Patricia-
dc.contributor.authorNogales, Eva-
dc.contributor.authorCarazo, José M.-
dc.identifier.citationJournal of Structural Biology, 166: 2 (2009) 234-240en_US
dc.description.abstractCommonly employed data models for maximum likelihood refinement of electron microscopy images behave poorly in the presence of normalization errors. Small variations in background mean or signal brightness are relatively common in cryo-electron microscopy data, and varying signal-to-noise ratios or artifacts in the images interfere with standard normalization procedures. In this paper, a statistical data model that accounts for normalization errors is presented, and a corresponding algorithm for maximum likelihood classification of structurally heterogeneous projection data is derived. The extended data model has general relevance, since similar algorithms may be derived for other maximum likelihood approaches in the field. The potentials of this approach are illustrated for two structurally heterogeneous data sets: 70S E.coli ribosomes and human RNA polymerase II complexes. In both cases, maximum likelihood classification based on the conventional data model failed, whereas the new approach was capable of revealing previously unobserved conformations.en_US
dc.description.sponsorshipWe thank the Barcelona and the Galicia Supercomputing Centers (BSC-CNS and CESGA) for providing computer resources, James Goodrich for providing the human Alu RNA and Cameron L. Noland for his contribution to data collection in the hRNAPII study. Funding was provided by the Spanish Ministry of Science (CSD2006-00023, BIO2007-67150-C03-1/3) and Comunidad de Madrid (S-GEN-0166-2006), the European Union (FP6-502828), the US National Heart, Lung and Blood Institute and the National Institutes of Health (R01 HL070472, R01 GM63072). E.N. is a Howard Hughes Medical Institute investigator. The content of this work is solely the responsibility of the authors and does not necessarily represent the official views of the National Heart, Lung and Blood Institute or the National Institutes of Health.en_US
dc.format.extent6080 bytes-
dc.subjectSingle particle analysisen_US
dc.subjectStructural heterogeneityen_US
dc.subjectExpectation maximizationen_US
dc.titleMaximum likelihood refinement of electron microscopy data with normalization errorsen_US
dc.description.peerreviewedPeer revieweden_US
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