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dc.contributor.authorMurphy, Rebecca R.
dc.contributor.authorDanezis, George
dc.contributor.authorHorrocks, Mathew H.
dc.contributor.authorJackson, Sophie E.
dc.contributor.authorKlenerman, David
dc.date.accessioned2014-08-11T11:23:54Z
dc.date.available2014-08-11T11:23:54Z
dc.date.issued2014-08-08
dc.identifier.citationAnalytical Chemistry 2014, 86 (17), pp 8603–8612. DOI: 10.1021/ac501188ren
dc.identifier.urihttp://www.repository.cam.ac.uk/handle/1810/245661
dc.descriptionThis is the final published version. It's also available from ACS in Analytical Chemistry: http://pubs.acs.org/doi/pdf/10.1021/ac501188r.en
dc.description.abstractIt is of significant biophysical interest to obtain accurate intramolecular distance information and population sizes from single-molecule Förster resonance energy transfer (smFRET) data obtained from biomolecules in solution. Experimental methods of increasing cost and complexity are being developed to improve the accuracy and precision of data collection. However, the analysis of smFRET datasets currently relies on simplistic and often arbitrary methods, for the selection and denoising of fluorescent bursts. Although these methods are satisfactory for the analysis of simple, low-noise systems with intermediate FRET efficiencies, they display systematic inaccuracies when applied to more complex systems. We have developed an inference method for the analysis of smFRET data from solution studies, based on rigorous model-based Bayesian techniques. We implement a Monte-Carlo Markov Chain (MCMC) based algorithm that simultaneously estimates population sizes and intramolecular distance information directly from a raw smFRET dataset, with no intermediate event selection and denoising steps. Here, we present both our parametric model of the smFRET process and the algorithm developed for data analysis. We test the algorithm using a combination of simulated datasets and data from dual-labelled DNA molecules. We demonstrate that our model-based method systematically outperforms threshold-based techniques in accurately inferring both population sizes and intramolecular distances.en
dc.language.isoenen
dc.publisherACSen
dc.rightsAttribution 2.0 UK: England & Wales
dc.rightsCreative Commons Attribution License 2.0 UK
dc.rights.urihttp://creativecommons.org/licenses/by/2.0/uk/
dc.titleBayesian Inference of Accurate Population Sizes and FRET Efficiencies from Single Diffusing Biomoleculesen
dc.typeArticleen
dc.type.versionpublished versionen
prism.endingPage8612
prism.issueIdentifier17
prism.publicationNameAnalytical Chemistry
prism.startingPage8603
prism.volume86
dc.rioxxterms.funderBiotechnology and Biological Sciences Research Council
rioxxterms.versionofrecord10.1021/ac501188r


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Attribution 2.0 UK: England & Wales
Except where otherwise noted, this item's licence is described as Attribution 2.0 UK: England & Wales