Abstract
Transcriptome analysis by deep sequencing, more commonly known as RNA-seq is, becoming the method of choice for gene discovery and quantitative splicing detection. We published a double-random priming RNA-seq approach capable of generating strand-specific information [Li et al., Proc Natl Acad Sci USA 105:20179–20184, 2008]. Poly(A)+ RNA from a treated and an untreated sample were utilized to generate RNA-seq libraries that were sequenced on the Illumina GA1 analyzer. Statistical analysis of approximately ten million sequence reads generated from both control and treated cells suggests that this tag density is sufficient for quantitative analysis of gene expression. We were also able to detect a large fraction of reads corresponding to annotated alternative exons, with a subset of the reads matching known and detecting new splice junctions. In this chapter, we provide a detailed, bench-ready protocol for the double-random priming method and provide user-friendly templates for the curve-fitting model described in the paper to estimate the tag density needed for optimal detection of regulated gene expression and alternative splicing.
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Acknowledgments
The authors would like to thank the members of the Yeo and Fu laboratories for critical reading of this manuscript. This research was supported by grants to G.W.Y. and X.D.F. from the US National Institutes of Health (HG004659 and GM084317 and GM052872) for funding this research and the development of this protocol.
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Lovci, M.T., Li, HR., Fu, XD., Yeo, G.W. (2011). RNA-Seq Analysis of Gene Expression and Alternative Splicing by Double-Random Priming Strategy. In: Lu, C., Browse, J., Wallis, J. (eds) cDNA Libraries. Methods in Molecular Biology, vol 729. Humana Press. https://doi.org/10.1007/978-1-61779-065-2_16
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DOI: https://doi.org/10.1007/978-1-61779-065-2_16
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