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2019-06-08Zeitschriftenartikel DOI: 10.25646/6331
Purple: A Computational Workflow for Strategic Selection of Peptides for Viral Diagnostics Using MS-Based Targeted Proteomics
dc.contributor.authorLechner, Johanna
dc.contributor.authorHartkopf, Felix
dc.contributor.authorHiort, Pauline
dc.contributor.authorNitsche, Andreas
dc.contributor.authorGrossegesse, Marica
dc.contributor.authorDoellinger, Joerg
dc.contributor.authorRenard, Bernhard Y.
dc.contributor.authorMuth, Thilo
dc.date.accessioned2019-10-25T07:05:29Z
dc.date.available2019-10-25T07:05:29Z
dc.date.issued2019-06-08none
dc.identifier.other10.3390/v11060536
dc.identifier.urihttp://edoc.rki.de/176904/6343
dc.description.abstractEmerging virus diseases present a global threat to public health. To detect viral pathogens in time-critical scenarios, accurate and fast diagnostic assays are required. Such assays can now be established using mass spectrometry-based targeted proteomics, by which viral proteins can be rapidly detected from complex samples down to the strain-level with high sensitivity and reproducibility. Developing such targeted assays involves tedious steps of peptide candidate selection, peptide synthesis, and assay optimization. Peptide selection requires extensive preprocessing by comparing candidate peptides against a large search space of background proteins. Here we present Purple (Picking unique relevant peptides for viral experiments), a software tool for selecting target-specific peptide candidates directly from given proteome sequence data. It comes with an intuitive graphical user interface, various parameter options and a threshold-based filtering strategy for homologous sequences. Purple enables peptide candidate selection across various taxonomic levels and filtering against backgrounds of varying complexity. Its functionality is demonstrated using data from different virus species and strains. Our software enables to build taxon-specific targeted assays and paves the way to time-efficient and robust viral diagnostics using targeted proteomics.eng
dc.language.isoengnone
dc.publisherRobert Koch-Institut
dc.rights(CC BY 3.0 DE) Namensnennung 3.0 Deutschlandger
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/de/
dc.subjectvirus proteomicseng
dc.subjectmass spectrometryeng
dc.subjectvirus diagnosticseng
dc.subjectdata analysiseng
dc.subjecttargeted proteomicseng
dc.subjectpeptide selectioneng
dc.subjectparallel reaction monitoringeng
dc.subject.ddc610 Medizin und Gesundheitnone
dc.titlePurple: A Computational Workflow for Strategic Selection of Peptides for Viral Diagnostics Using MS-Based Targeted Proteomicsnone
dc.typearticle
dc.identifier.urnurn:nbn:de:kobv:0257-176904/6343-6
dc.identifier.doihttp://dx.doi.org/10.25646/6331
dc.type.versionpublishedVersionnone
local.edoc.container-titleVirusesnone
local.edoc.type-nameZeitschriftenartikel
local.edoc.container-typeperiodical
local.edoc.container-type-nameZeitschrift
local.edoc.container-urlhttps://www.mdpi.com/1999-4915/11/6/536#abstractcnone
local.edoc.container-publisher-nameMDPInone
local.edoc.container-volume11none
local.edoc.container-issue536none
local.edoc.container-reportyear2019none
local.edoc.container-year2019none
local.edoc.container-firstpage1none
local.edoc.container-lastpage23none
local.edoc.rki-departmentMethodenentwicklung und Forschungsinfrastrukturnone
dc.description.versionPeer Reviewednone

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