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dc.contributor.authorSimon, L. M.
dc.contributor.authorKarg, S.
dc.contributor.authorWestermann, A. J.
dc.contributor.authorEngel, M.
dc.contributor.authorElbehery, A. H.A.
dc.contributor.authorHense, B.
dc.contributor.authorHeinig, M.
dc.contributor.authorDeng, L.
dc.contributor.authorTheis, F. J.
dc.creatorSimon, L.
dc.date.accessioned2019-12-11T16:04:40Z
dc.date.available2019-12-11T16:04:40Z
dc.date.issued2018-06-01
dc.identifier.citationGigascience. 2018 Jun 1;7(6). pii: 5036539. doi: 10.1093/gigascience/giy070.en_US
dc.identifier.pmid29901703
dc.identifier.doi10.1093/gigascience/giy070
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85050892432&origin=inward
dc.identifier.urihttp://hdl.handle.net/10033/622049
dc.description.abstractBackground: With the advent of the age of big data in bioinformatics, large volumes of data and high-performance computing power enable researchers to perform re-analyses of publicly available datasets at an unprecedented scale. Ever more studies imply the microbiome in both normal human physiology and a wide range of diseases. RNA sequencing technology (RNA-seq) is commonly used to infer global eukaryotic gene expression patterns under defined conditions, including human disease-related contexts; however, its generic nature also enables the detection of microbial and viral transcripts. Findings: We developed a bioinformatic pipeline to screen existing human RNA-seq datasets for the presence of microbial and viral reads by re-inspecting the non-human-mapping read fraction. We validated this approach by recapitulating outcomes from six independent, controlled infection experiments of cell line models and compared them with an alternative metatranscriptomic mapping strategy. We then applied the pipeline to close to 150 terabytes of publicly available raw RNA-seq data from  more than 17,000 samples from more than 400 studies relevant to human disease using state-of-the-art high-performance computing systems. The resulting data from this large-scale re-analysis are made available in the presented MetaMap resource. Conclusions: Our results demonstrate that common human RNA-seq data, including those archived in public repositories, might contain valuable information to correlate microbial and viral detection patterns with diverse diseases. The presented MetaMap database thus provides a rich resource for hypothesis generation toward the role of the microbiome in human disease. Additionally, codes to process new datasets and perform statistical analyses are made available.en_US
dc.language.isoenen_US
dc.publisherOxford University Pressen_US
dc.relation.ispartofGigaScience
dc.relation.ispartofseries6en_US
dc.rightsAttribution-NonCommercial-ShareAlike 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/*
dc.subjectBig dataen_US
dc.subjectHigh-performance computingen_US
dc.subjectHuman diseaseen_US
dc.subjectInfectionen_US
dc.subjectMetatranscriptomicsen_US
dc.subjectMicrobiomeen_US
dc.subjectRNA-seqen_US
dc.subjectSequence read archiveen_US
dc.subjectViromeen_US
dc.titleMetaMap: An atlas of metatranscriptomic reads in human disease-related RNA-seq dataen_US
dc.typeArticleen_US
dc.contributor.departmentHIRI, Helmholtz-Institut für RNA-basierte Infektionsforschung, Josef-Shneider Strasse 2, 97080 Würzburg, Germany.en_US
dc.identifier.journalGigascienceen_US
dc.identifier.eid2-s2.0-85050892432
dc.identifier.scopusidSCOPUS_ID:85050892432
dc.relation.volume7
refterms.dateFOA2019-12-11T16:04:41Z


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