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dc.contributor.authorCollado-Torres, Leonardoen_US
dc.contributor.authorNellore, Abhinaven_US
dc.contributor.authorFrazee, Alyssa C.en_US
dc.contributor.authorWilks, Christopheren_US
dc.contributor.authorLove, Michael I.en_US
dc.contributor.authorLangmead, Benen_US
dc.contributor.authorIrizarry, Rafael A.en_US
dc.contributor.authorLeek, Jeffrey T.en_US
dc.contributor.authorJaffe, Andrew E.en_US
dc.date.accessioned2017-03-28T23:52:14Z
dc.date.issued2017en_US
dc.identifier.citationCollado-Torres, Leonardo, Abhinav Nellore, Alyssa C. Frazee, Christopher Wilks, Michael I. Love, Ben Langmead, Rafael A. Irizarry, Jeffrey T. Leek, and Andrew E. Jaffe. 2017. “Flexible expressed region analysis for RNA-seq with derfinder.” Nucleic Acids Research 45 (2): e9. doi:10.1093/nar/gkw852. http://dx.doi.org/10.1093/nar/gkw852.en
dc.identifier.issnen
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:31731874
dc.description.abstractDifferential expression analysis of RNA sequencing (RNA-seq) data typically relies on reconstructing transcripts or counting reads that overlap known gene structures. We previously introduced an intermediate statistical approach called differentially expressed region (DER) finder that seeks to identify contiguous regions of the genome showing differential expression signal at single base resolution without relying on existing annotation or potentially inaccurate transcript assembly. We present the derfinder software that improves our annotation-agnostic approach to RNA-seq analysis by: (i) implementing a computationally efficient bump-hunting approach to identify DERs that permits genome-scale analyses in a large number of samples, (ii) introducing a flexible statistical modeling framework, including multi-group and time-course analyses and (iii) introducing a new set of data visualizations for expressed region analysis. We apply this approach to public RNA-seq data from the Genotype-Tissue Expression (GTEx) project and BrainSpan project to show that derfinder permits the analysis of hundreds of samples at base resolution in R, identifies expression outside of known gene boundaries and can be used to visualize expressed regions at base-resolution. In simulations, our base resolution approaches enable discovery in the presence of incomplete annotation and is nearly as powerful as feature-level methods when the annotation is complete. derfinder analysis using expressed region-level and single base-level approaches provides a compromise between full transcript reconstruction and feature-level analysis. The package is available from Bioconductor at www.bioconductor.org/packages/derfinder.en
dc.language.isoen_USen
dc.publisherOxford University Pressen
dc.relation.isversionofdoi:10.1093/nar/gkw852en
dc.relation.hasversionhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC5314792/pdf/en
dash.licenseLAAen_US
dc.titleFlexible expressed region analysis for RNA-seq with derfinderen
dc.typeJournal Articleen_US
dc.description.versionVersion of Recorden
dc.relation.journalNucleic Acids Researchen
dash.depositing.authorIrizarry, Rafael A.en_US
dc.date.available2017-03-28T23:52:14Z
dc.identifier.doi10.1093/nar/gkw852*
dash.contributor.affiliatedIrizarry, Rafael


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