Stem cell transcriptome profiling via massive-scale mRNA sequencing Export

@article{citeulike:2927415, abstract = {We 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.}, address = {[1] Expression Genomics Laboratory, Institute for Molecular Bioscience, The University of Queensland, 306 Carmody Road, St. Lucia, Queensland, 4072, Australia. [2] These authors contributed equally to this work.}, author = {Cloonan, Nicole and Forrest, Alistair R. R. and Kolle, Gabriel and Gardiner, Brooke B. A. and Faulkner, Geoffrey J. and Brown, Mellissa K. and Taylor, Darrin F. and Steptoe, Anita L. and Wani, Shivangi and Bethel, Graeme and Robertson, Alan J. and Perkins, Andrew C. and Bruce, Stephen J. and Lee, Clarence C. and Ranade, Swati S. and Peckham, Heather E. and Manning, Jonathan M. and McKernan, Kevin J. and Grimmond, Sean M.}, citeulike-article-id = {2927415}, citeulike-linkout-0 = {http://dx.doi.org/10.1038/nmeth.1223}, citeulike-linkout-1 = {http://dx.doi.org/10.1038/nmeth.1223}, citeulike-linkout-2 = {http://view.ncbi.nlm.nih.gov/pubmed/18516046}, citeulike-linkout-3 = {http://www.hubmed.org/display.cgi?uids=18516046}, day = {30}, doi = {10.1038/nmeth.1223}, issn = {1548-7105}, journal = {Nature Methods}, keywords = {abi-solid, deep-seq, es, method, rna-seq, stemcell}, month = {May}, number = {7}, pages = {613--619}, posted-at = {2008-11-10 10:13:18}, priority = {2}, publisher = {Nature Publishing Group}, title = {Stem cell transcriptome profiling via massive-scale mRNA sequencing}, url = {http://dx.doi.org/10.1038/nmeth.1223}, volume = {5}, year = {2008} }

TY - JOUR ID - citeulike:2927415 L3 - citeulike-article-id:2927415 N2 - We 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. IS - 7 JF - Nature Methods SN - 1548-7105 AD - [1] Expression Genomics Laboratory, Institute for Molecular Bioscience, The University of Queensland, 306 Carmody Road, St. Lucia, Queensland, 4072, Australia. [2] These authors contributed equally to this work. EP - 619 TI - Stem cell transcriptome profiling via massive-scale mRNA sequencing PB - Nature Publishing Group VL - 5 SP - 613 KW - abi-solid KW - deep-seq KW - es KW - method KW - rna-seq KW - stemcell AU - Cloonan, Nicole AU - Forrest, Alistair AU - Kolle, Gabriel AU - Gardiner, Brooke AU - Faulkner, Geoffrey AU - Brown, Mellissa AU - Taylor, Darrin AU - Steptoe, Anita AU - Wani, Shivangi AU - Bethel, Graeme AU - Robertson, Alan AU - Perkins, Andrew AU - Bruce, Stephen AU - Lee, Clarence AU - Ranade, Swati AU - Peckham, Heather AU - Manning, Jonathan AU - McKernan, Kevin AU - Grimmond, Sean PY - 2008/05/30/ UR - http://dx.doi.org/10.1038/nmeth.1223 ER -