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dc.contributor.authorCloonan, N
dc.contributor.authorForrest, ARR
dc.contributor.authorKolle, G
dc.contributor.authorGardiner, BBA
dc.contributor.authorFaulkner, GJ
dc.contributor.authorBrown, MK
dc.contributor.authorTaylor, DF
dc.contributor.authorSteptoe, AL
dc.contributor.authorWani, S
dc.contributor.authorBethel, G
dc.contributor.authorRobertson, AJ
dc.contributor.authorPerkins, AC
dc.contributor.authorBruce, SJ
dc.contributor.authorLee, CC
dc.contributor.authorRanade, SS
dc.contributor.authorPeckham, HE
dc.contributor.authorManning, JM
dc.contributor.authorMcKernan, KJ
dc.contributor.authorGrimmond, SM
dc.date.accessioned2017-05-03T16:56:33Z
dc.date.available2017-05-03T16:56:33Z
dc.date.issued2008
dc.date.modified2009-11-02T05:23:46Z
dc.identifier.issn1548-7091
dc.identifier.doi10.1038/nmeth.1223
dc.identifier.urihttp://hdl.handle.net/10072/23464
dc.description.abstractWe developed a massive-scale RNA sequencing protocol, short quantitative random RNA libraries or SQRL, to survey the complexity, dynamics and sequence content of transcriptomes in a near-complete fashion. This method generates directional, random-primed, linear cDNA libraries that are optimized for next-generation short-tag sequencing. We surveyed the poly(A)+ transcriptomes of undifferentiated mouse embryonic stem cells (ESCs) and embryoid bodies (EBs) at an unprecedented depth (10 Gb), using the Applied Biosystems SOLiD technology. These libraries capture the genomic landscape of expression, state-specific expression, single-nucleotide polymorphisms (SNPs), the transcriptional activity of repeat elements, and both known and new alternative splicing events. We investigated the impact of transcriptional complexity on current models of key signaling pathways controlling ESC pluripotency and differentiation, highlighting how SQRL can be used to characterize transcriptome content and dynamics in a quantitative and reproducible manner, and suggesting that our understanding of transcriptional complexity is far from complete.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.languageEnglish
dc.language.isoeng
dc.publisherNature Publishing Group
dc.publisher.placeUnited Kingdom
dc.publisher.urihttp://www.nature.com/nmeth/index.html
dc.relation.ispartofstudentpublicationN
dc.relation.ispartofpagefrom613
dc.relation.ispartofpageto620
dc.relation.ispartofissue7
dc.relation.ispartofjournalNature Methods
dc.relation.ispartofvolume5
dc.rights.retentionY
dc.subject.fieldofresearchBiological sciences
dc.subject.fieldofresearchBiomedical and clinical sciences
dc.subject.fieldofresearchcode31
dc.subject.fieldofresearchcode32
dc.titleStem cell transcriptome profiling via massive-scale mRNA sequencing
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.date.issued2008
gro.hasfulltextNo Full Text
gro.griffith.authorForrest, Alistair RR.


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