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2014-03-18Zeitschriftenartikel DOI: 10.1021/pr401249b
Pre- and Post-Processing Workflow for Affinity Purification Mass Spectrometry Data
dc.contributor.authorFischer, Martina
dc.contributor.authorZilkenat, Susann
dc.contributor.authorGerlach, Roman
dc.contributor.authorWagner, Samuel
dc.contributor.authorRenard, Bernhard Y.
dc.date.accessioned2018-05-07T19:39:27Z
dc.date.available2018-05-07T19:39:27Z
dc.date.created2017-01-16
dc.date.issued2014-03-18none
dc.identifier.otherhttp://edoc.rki.de/oa/articles/reC2eY8db8suk/PDF/27iCgRuCak61Q.pdf
dc.identifier.urihttp://edoc.rki.de/176904/2522
dc.description.abstractThe reliable detection of protein–protein interactions by affinity purification mass spectrometry (AP-MS) is crucial for the understanding of biological processes. Quantitative information can be used to separate truly interacting proteins from false-positives by contrasting counts of proteins binding to specific baits with counts of negative controls. Several approaches have been proposed for computing scores for potential interaction proteins, for example, the commonly used SAINT software. However, it remains a subjective decision where to set the cutoff score for candidate selection; furthermore, no precise control for the expected number of false-positives is provided. In related fields, successful data analysis strongly relies on statistical pre- and post-processing steps, which, so far, have played only a minor role in AP-MS data analysis. We introduce a complete workflow, embedding either the scoring method SAINT or alternatively a two-stage Poisson model into a pre- and post-processing framework. To this end, we investigate different normalization methods and apply a statistical filter adjusted to AP-MS data. Furthermore, we propose permutation and adjustment procedures, which allow the replacement of scores by statistical p values. The performance of the workflow is assessed on simulations as well as on a study focusing on interactions with the T3SS in Salmonella Typhimurium. Preprocessing methods significantly increase the number of detected truly interacting proteins, while a constant false-discovery rate is maintained. The software solution is freely available.eng
dc.language.isoeng
dc.publisherRobert Koch-Institut
dc.subjectAP-MS data analysiseng
dc.subjectlabel-freeeng
dc.subjectpre-post-processingeng
dc.subjectprotein−protein interactionseng
dc.subjectspectral countingeng
dc.subject.ddc610 Medizin
dc.titlePre- and Post-Processing Workflow for Affinity Purification Mass Spectrometry Data
dc.typeperiodicalPart
dc.identifier.urnurn:nbn:de:0257-10050686
dc.identifier.doi10.1021/pr401249b
dc.identifier.doihttp://dx.doi.org/10.25646/2447
local.edoc.container-titleJournal of Proteome Research
local.edoc.fp-subtypeArtikel
local.edoc.type-nameZeitschriftenartikel
local.edoc.container-typeperiodical
local.edoc.container-type-nameZeitschrift
local.edoc.container-urlhttp://pubs.acs.org/doi/abs/10.1021/pr401249b
local.edoc.container-publisher-nameAmerican Chemical Society
local.edoc.container-volume13
local.edoc.container-issue5
local.edoc.container-year2014

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