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2018-09-08Zeitschriftenartikel DOI: 10.25646/5730
PAIPline: pathogen identification in metagenomic and clinical next generation sequencing samples
dc.contributor.authorAndrusch, Andreas
dc.contributor.authorDabrowski, Piotr Wojtek
dc.contributor.authorKlenner, Jeanette
dc.contributor.authorTausch, Simon H.
dc.contributor.authorKohl, Claudia
dc.contributor.authorOsman, Abdalla A.
dc.contributor.authorRenard, Bernhard Y.
dc.contributor.authorNitsche, Andreas
dc.date.accessioned2018-10-04T10:05:25Z
dc.date.available2018-10-04T10:05:25Z
dc.date.issued2018-09-08none
dc.identifier.other10.1093/bioinformatics/bty595
dc.identifier.urihttp://edoc.rki.de/176904/5791
dc.description.abstractMotivation: Next generation sequencing (NGS) has provided researchers with a powerful tool to characterize metagenomic and clinical samples in research and diagnostic settings. NGS allows an open view into samples useful for pathogen detection in an unbiased fashion and without prior hypothesis about possible causative agents. However, NGS datasets for pathogen detection come with different obstacles, such as a very unfavorable ratio of pathogen to host reads. Alongside often appearing false positives and irrelevant organisms, such as contaminants, tools are often challenged by samples with low pathogen loads and might not report organisms present below a certain threshold. Furthermore, some metagenomic profiling tools are only focused on one particular set of pathogens, for example bacteria. Results: We present PAIPline, a bioinformatics pipeline specifically designed to address problems associated with detecting pathogens in diagnostic samples. PAIPline particularly focuses on userfriendliness and encapsulates all necessary steps from preprocessing to resolution of ambiguous reads and filtering up to visualization in a single tool. In contrast to existing tools, PAIPline is more specific while maintaining sensitivity. This is shown in a comparative evaluation where PAIPline was benchmarked along other well-known metagenomic profiling tools on previously published well-characterized datasets. Additionally, as part of an international cooperation project, PAIPline was applied to an outbreak sample of hemorrhagic fevers of then unknown etiology. The presented results show that PAIPline can serve as a robust, reliable, user-friendly, adaptable and generalizable stand-alone software for diagnostics from NGS samples and as a stepping stone for further downstream analyses. Availability and implementation: PAIPline is freely available under https://gitlab.com/rki_bioinformatics/paipline.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.subject.ddc610 Medizin und Gesundheitnone
dc.titlePAIPline: pathogen identification in metagenomic and clinical next generation sequencing samplesnone
dc.typearticle
dc.identifier.urnurn:nbn:de:kobv:0257-176904/5791-3
dc.identifier.doihttp://dx.doi.org/10.25646/5730
dc.type.versionpublishedVersionnone
local.edoc.container-titleBioinformaticsnone
local.edoc.type-nameZeitschriftenartikel
local.edoc.container-typeperiodical
local.edoc.container-type-nameZeitschrift
local.edoc.container-urlhttps://academic.oup.com/bioinformatics/article/34/17/i715/5093217none
local.edoc.container-publisher-nameOxford University Pressnone
local.edoc.container-volume34none
local.edoc.container-issue17none
local.edoc.container-reportyear2018none
local.edoc.container-firstpagei715none
local.edoc.container-lastpagei721none
local.edoc.rki-departmentZentrum für Biologische Gefahren und Spezielle Pathogenenone
dc.description.versionPeer Reviewednone

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