Advertisement

Next-Generation Sequencing Applied to Flower Development: RNA-Seq

  • Jun He
  • Yuling Jiao
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1110)

Abstract

Genome-wide study of gene expression, or transcriptome profiling, is critical for our understanding of biological functions, including developmental processes. Recent breakthroughs in high-throughput sequencing technologies have revolutionized gene expression profiling to study the transcriptome at the nucleotide level, which is known as RNA-seq. RNA-seq, also called “whole transcriptome shotgun sequencing,” uses next-generation sequencing technologies to sequence cDNA in order to infer a sample’s RNA content. Here we describe a detailed bench-ready protocol to generate RNA-seq libraries for high-throughput single-end or pair-end sequencing compatible with the Illumina sequencing platform.

Keywords

Next-generation sequencing RNA-seq Transcriptome 

Notes

Acknowledgements

We thank B.A. Williams and B.J. Wold for sharing protocols. This work was supported by the Ministry of Agriculture of China (Grant 2011ZX08010-002), by the Knowledge Innovation Program of CAS Grant KYQY-150, and by the Hundred Talents Program of CAS.

References

  1. 1.
    Krizek BA, Fletcher JC (2005) Molecular mechanisms of flower development: an armchair guide. Nat Rev Genet 6:688–698PubMedCrossRefGoogle Scholar
  2. 2.
    Schena M, Shalon D, Davis RW, Brown PO (1995) Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 270:467–470PubMedCrossRefGoogle Scholar
  3. 3.
    Wang Z, Gerstein M, Snyder M (2009) RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 10:57–63PubMedCentralPubMedCrossRefGoogle Scholar
  4. 4.
    Marioni JC, Mason CE, Mane SM, Stephens M, Gilad Y (2008) RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome Res 18:1509–1517PubMedCrossRefGoogle Scholar
  5. 5.
    Morin R, Bainbridge M, Fejes A, Hirst M, Krzywinski M, Pugh T, McDonald H, Varhol R, Jones S, Marra M (2008) Profiling the HeLa S3 transcriptome using randomly primed cDNA and massively parallel short-read sequencing. Biotechniques 45:81–94PubMedCrossRefGoogle Scholar
  6. 6.
    Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B (2008) Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods 5:621–628PubMedCrossRefGoogle Scholar
  7. 7.
    Nagalakshmi U, Wang Z, Waern K, Shou C, Raha D, Gerstein M, Snyder M (2008) The transcriptional landscape of the yeast genome defined by RNA sequencing. Science 320:1344–1349PubMedCentralPubMedCrossRefGoogle Scholar
  8. 8.
    Cloonan N, Forrest AR, Kolle G, Gardiner BB, Faulkner GJ, Brown MK, Taylor DF, Steptoe AL, Wani S, Bethel G, Robertson AJ, Perkins AC, Bruce SJ, Lee CC, Ranade SS, Peckham HE, Manning JM, McKernan KJ, Grimmond SM (2008) Stem cell transcriptome profiling via massive-scale mRNA sequencing. Nat Methods 5:613–619PubMedCrossRefGoogle Scholar
  9. 9.
    Lister R, O’Malley RC, Tonti-Filippini J, Gregory BD, Berry CC, Millar AH, Ecker JR (2008) Highly integrated single-base resolution maps of the epigenome in Arabidopsis. Cell 133:523–536PubMedCentralPubMedCrossRefGoogle Scholar
  10. 10.
    Wilhelm BT, Marguerat S, Watt S, Schubert F, Wood V, Goodhead I, Penkett CJ, Rogers J, Bahler J (2008) Dynamic repertoire of a eukaryotic transcriptome surveyed at single-nucleotide resolution. Nature 453:1239–1243PubMedCrossRefGoogle Scholar
  11. 11.
    Ozsolak F, Milos PM (2011) RNA sequencing: advances, challenges and opportunities. Nat Rev Genet 12:87–98PubMedCentralPubMedCrossRefGoogle Scholar
  12. 12.
    Martin JA, Wang Z (2011) Next-generation transcriptome assembly. Nat Rev Genet 12:671–682PubMedCrossRefGoogle Scholar
  13. 13.
    Malone JH, Oliver B (2011) Microarrays, deep sequencing and the true measure of the transcriptome. BMC Biol 9:34PubMedCentralPubMedCrossRefGoogle Scholar
  14. 14.
    Jiao Y, Meyerowitz EM (2010) Cell-type specific analysis of translating RNAs in developing flowers reveals new levels of control. Mol Syst Biol 6:419PubMedCentralPubMedCrossRefGoogle Scholar
  15. 15.
    Schmid MW, Schmidt A, Klostermeier UC, Barann M, Rosenstiel P, Grossniklaus U (2012) A powerful method for transcriptional profiling of specific cell types in eukaryotes: laser-assisted microdissection and RNA sequencing. PLoS One 7:e29685PubMedCentralPubMedCrossRefGoogle Scholar
  16. 16.
    Yang H, Lu P, Wang Y, Ma H (2011) The transcriptome landscape of Arabidopsis male meiocytes from high-throughput sequencing: the complexity and evolution of the meiotic process. Plant J 65:503–516PubMedCrossRefGoogle Scholar
  17. 17.
    Oshlack A, Robinson MD, Young MD (2010) From RNA-seq reads to differential expression results. Genome Biol 11:220PubMedCentralPubMedCrossRefGoogle Scholar
  18. 18.
    Kvam VM, Liu P, Si Y (2012) A comparison of statistical methods for detecting differentially expressed genes from RNA-seq data. Am J Bot 99:248–256PubMedCrossRefGoogle Scholar
  19. 19.
    Fang Z, Martin JA, Wang Z (2012) Statistical methods for identifying differentially expressed genes in RNA-Seq experiments. Cell Biosci 2:26PubMedCentralPubMedCrossRefGoogle Scholar
  20. 20.
    Garber M, Grabherr MG, Guttman M, Trapnell C (2011) Computational methods for transcriptome annotation and quantification using RNA-seq. Nat Methods 8:469–477PubMedCrossRefGoogle Scholar
  21. 21.
    Gao D, Kim J, Kim H, Phang TL, Selby H, Tan AC, Tong T (2010) A survey of statistical software for analysing RNA-seq data. Hum Genomics 5:56–60PubMedCentralPubMedCrossRefGoogle Scholar
  22. 22.
    Cumbie JS, Kimbrel JA, Di Y, Schafer DW, Wilhelm LJ, Fox SE, Sullivan CM, Curzon AD, Carrington JC, Mockler TC, Chang JH (2011) GENE-counter: a computational pipeline for the analysis of RNA-Seq data for gene expression differences. PLoS One 6:e25279PubMedCentralPubMedCrossRefGoogle Scholar
  23. 23.
    Tang F, Barbacioru C, Nordman E, Li B, Xu N, Bashkirov VI, Lao K, Surani MA (2010) RNA-Seq analysis to capture the transcriptome landscape of a single cell. Nat Protoc 5:516–535PubMedCrossRefGoogle Scholar
  24. 24.
    Marquez Y, Brown JW, Simpson C, Barta A, Kalyna M (2012) Transcriptome survey reveals increased complexity of the alternative splicing landscape in Arabidopsis. Genome Res 22:1184–1195PubMedCrossRefGoogle Scholar
  25. 25.
    Filichkin SA, Priest HD, Givan SA, Shen R, Bryant DW, Fox SE, Wong WK, Mockler TC (2010) Genome-wide mapping of alternative splicing in Arabidopsis thaliana. Genome Res 20:45–58PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, New York 2014

Authors and Affiliations

  1. 1.State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental BiologyChinese Academy of SciencesBeijingChina

Personalised recommendations