2008
DOI: 10.1038/nmeth.1223
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Stem cell transcriptome profiling via massive-scale mRNA sequencing

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 Bi… Show more

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Cited by 941 publications
(720 citation statements)
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References 31 publications
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“…RNA-Seq overcomes several shortcomings of microarray-based detection of transcripts, including probe cross-hybridization (9), restricted signal dynamic range, and low sensitivity and specificity, which often lead to difficulties in the detection of low abundance transcripts and discrimination between similar sequences. Sequence level transcript information has much greater power to distinguish between paralogous genes, better detection of low abundance transcripts, and allows replicable digital quantification based upon counting sequence reads (10)(11)(12)(13)(14). Furthermore, RNA-Seq can identify transcript sequence polymorphisms, RNA editing sites, and splicing variants, and there is no strict requirement for a reference genome sequence (15).…”
Section: Introductionmentioning
confidence: 99%
“…RNA-Seq overcomes several shortcomings of microarray-based detection of transcripts, including probe cross-hybridization (9), restricted signal dynamic range, and low sensitivity and specificity, which often lead to difficulties in the detection of low abundance transcripts and discrimination between similar sequences. Sequence level transcript information has much greater power to distinguish between paralogous genes, better detection of low abundance transcripts, and allows replicable digital quantification based upon counting sequence reads (10)(11)(12)(13)(14). Furthermore, RNA-Seq can identify transcript sequence polymorphisms, RNA editing sites, and splicing variants, and there is no strict requirement for a reference genome sequence (15).…”
Section: Introductionmentioning
confidence: 99%
“…RNA-seq data are highly reproducible, with few systematic discrepancies among technical replicates (Marioni et al, 2008). RNA-seq technology has been applied to uncovering the entire transcriptome and identifying alternative splicing (AS) and novel transcribed regions (NTRs) as well as chimeric transcripts produced by trans-splicing in human, yeast, mouse, Arabidopsis, and rice (Oryza sativa; Cloonan et al, 2008;Lister et al, 2008;Mortazavi et al, 2008;Nagalakshmi et al, 2008;Pan et al, 2008;Sultan et al, 2008;Wang et al, 2008;Wilhelm et al, 2008;Filichkin et al, 2010;Lu et al, 2010;Zhang et al, 2010).…”
mentioning
confidence: 99%
“…Ultrahigh-throughput RNA sequencing (RNA-seq) is a revolutionary tool for transcriptome profiling, which offers both single-base resolution for annotation and digital gene expression levels at the whole genome scale 10 , and overcomes several limitations of hybridization-based microarrays 10,11 . In particular, the wide dynamic range of RNA-seq allows robust capture of low-expression transcripts, including cytokines/chemokines, signal transduction molecules and transcriptional regulators 10 and direct comparisons of transcriptomes in different samples and under different conditions 10,12 . A detailed understanding of the cellular behaviour after transplantation is crucial to establish the efficacy and safety of stem cell-based therapies 4,5 , and our strategy will be applicable to various stem cell-based approaches.…”
mentioning
confidence: 99%