Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/12230
Share/Export:
logo share SHARE logo core CORE BASE
Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL | DATACITE
Title

Maximum likelihood refinement of electron microscopy data with normalization errors

AuthorsScheres, Sjors H. W.; Valle, Mikel; Grob, Patricia; Nogales, Eva; Carazo, José M.
KeywordsSingle particle analysis
Structural heterogeneity
Classification
Expectation maximization
Issue DateMay-2009
PublisherElsevier
CitationJournal of Structural Biology, 166: 2 (2009) 234-240
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.
Publisher version (URL)http://dx.doi.org/10.1016/j.jsb.2009.02.007
URIhttp://hdl.handle.net/10261/12230
DOI10.1016/j.jsb.2009.02.007
ISSN1047-8477
Appears in Collections:(CNB) Artículos

Show full item record
Review this work

PubMed Central
Citations

14
checked on May 15, 2022

SCOPUSTM   
Citations

24
checked on May 19, 2022

WEB OF SCIENCETM
Citations

23
checked on May 19, 2022

Page view(s)

399
checked on May 24, 2022

Google ScholarTM

Check

Altmetric

Dimensions


Related articles:


WARNING: Items in Digital.CSIC are protected by copyright, with all rights reserved, unless otherwise indicated.