Advertisement

Detection of Reverse Transcriptase Termination Sites Using cDNA Ligation and Massive Parallel Sequencing

  • Lukasz J. Kielpinski
  • Mette Boyd
  • Albin Sandelin
  • Jeppe Vinther
Part of the Methods in Molecular Biology book series (MIMB, volume 1038)

Abstract

Detection of reverse transcriptase termination sites is important in many different applications, such as structural probing of RNAs, rapid amplification of cDNA 5′ ends (5′ RACE), cap analysis of gene expression, and detection of RNA modifications and protein–RNA cross-links. The throughput of these methods can be increased by applying massive parallel sequencing technologies.

Here, we describe a versatile method for detection of reverse transcriptase termination sites based on ligation of an adapter to the 3′ end of cDNA with bacteriophage TS2126 RNA ligase (CircLigase™). In the following PCR amplification, Illumina adapters and index sequences are introduced, thereby allowing amplicons to be pooled and sequenced on the standard Illumina platform for genomic DNA sequencing. Moreover, we demonstrate how to map sequencing reads and perform analysis of the sequencing data with freely available tools that do not require formal bioinformatics training. As an example, we apply the method to detection of transcription start sites in mouse liver cells.

Key words

Reverse transcription Termination Sequencing TS2l26 RNA ligase CAGE Galaxy 

Notes

Acknowledgments

The research was funded by the Danish Council for Strategic Research, the Lundbeck Foundation and the Novo Nordisk Foundation. Morten Lindow and Susanna Obad, Santaris Pharma, provided mouse liver samples and RIKEN/Piero Carninci provided the updated CAGE protocol as well as advice ahead of publication.

References

  1. 1.
    Takahashi H, Kato S, Murata M et al (2012) CAGE (cap analysis of gene expression): a protocol for the detection of promoter and transcriptional networks. In: Deplancke B, Gheldof N (eds) Gene regulatory networks, vol 786. Humana, Totowa, NJ, pp 181–200CrossRefGoogle Scholar
  2. 2.
    Motorin Y, Muller S, Behm‐Ansmant I et al (2007) Identification of modified residues in RNAs by reverse transcription‐based methods. Methods Enzymol 425:21–53. doi: 10.1016/s0076-6879(07)25002-5 PubMedCrossRefGoogle Scholar
  3. 3.
    Mortimer SA, Weeks KM (2009) Time-resolved RNA SHAPE chemistry: quantitative RNA structure analysis in one-second snapshots and at single-nucleotide resolution. Nat Protoc 4(10):1413–1421. doi:nprot.2009.126 [pii]10.1038/nprot.2009.126PubMedCrossRefGoogle Scholar
  4. 4.
    König J, Zarnack K, Rot G et al (2010) iCLIP reveals the function of hnRNP particles in splicing at individual nucleotide resolution. Nat Struct Mol Biol 17(7):909–915. doi: 10.1038/nsmb.1838 PubMedCrossRefGoogle Scholar
  5. 5.
    Shibata Y, Carninci P, Watahiki A et al (2001) Cloning full-length, cap-trapper-selected cDNAs by using the single-strand linker ligation method. Biotechniques 30(6):1250–1254PubMedGoogle Scholar
  6. 6.
    Li TW, Weeks KM (2006) Structure-independent and quantitative ligation of single-stranded DNA. Anal Biochem 349(2):242–246. doi: 10.1016/j.ab.2005.11.002 PubMedCrossRefGoogle Scholar
  7. 7.
    Hirzmann J, Luo D, Hahnen J et al (1993) Determination of messenger RNA 5'-ends by reverse transcription of the cap structure. Nucleic Acids Res 21(15):3597–3598PubMedCrossRefGoogle Scholar
  8. 8.
    Zhu YY, Machleder EM, Chenchik A et al (2001) Reverse transcriptase template switching: a SMART approach for full-length cDNA library construction. Biotechniques 30(4):892–897PubMedGoogle Scholar
  9. 9.
    Carninci P, Kasukawa T, Katayama S et al (2005) The transcriptional landscape of the mammalian genome. Science 309(5740):1559–1563. doi: 10.1126/science.1112014 PubMedCrossRefGoogle Scholar
  10. 10.
    Shiraki T, Kondo S, Katayama S et al (2003) Cap analysis gene expression for high-throughput analysis of transcriptional starting point and identification of promoter usage. Proc Natl Acad Sci U S A 100(26):15776–15781. doi: 10.1073/pnas.2136655100 PubMedCrossRefGoogle Scholar
  11. 11.
    Weeks KM, Mauger DM (2011) Exploring RNA structural codes with SHAPE chemistry. Acc Chem Res 44(12):1280–1291. doi: 10.1021/ar200051h PubMedCrossRefGoogle Scholar
  12. 12.
    Lucks JB, Mortimer SA, Trapnell C et al (2011) Multiplexed RNA structure characterization with selective 2'-hydroxyl acylation analyzed by primer extension sequencing (SHAPE-Seq). Proc Natl Acad Sci U S A 108(27):11063–11068. doi: 10.1073/pnas.1106501108 PubMedCrossRefGoogle Scholar
  13. 13.
    Giardine B, Riemer C, Hardison RC et al (2005) Galaxy: a platform for interactive large-scale genome analysis. Genome Res 15(10):1451–1455. doi: 10.1101/Gr.4086505 PubMedCrossRefGoogle Scholar
  14. 14.
    Goecks J, Nekrutenko A, Taylor J et al (2010) Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences. Genome Biol 11(8):R86. doi: 10.1186/Gb-2010-11-8-R86 PubMedCrossRefGoogle Scholar
  15. 15.
    Blankenberg D, Gordon A, Von Kuster G et al (2010) Manipulation of FASTQ data with Galaxy. Bioinformatics 26(14):1783–1785. doi: 10.1093/bioinformatics/btq281 PubMedCrossRefGoogle Scholar
  16. 16.
    Blankenberg D, Von Kuster G, Coraor N et al. (2010) Galaxy: a web-based genome analysis tool for experimentalists. Curr Protoc Mol Biol Chapter 19:Unit 19.10.11–21Google Scholar
  17. 17.
    Langmead B, Trapnell C, Pop M et al (2009) Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol 10(3):R25. doi: 10.1186/Gb-2009-10-3-R25 PubMedCrossRefGoogle Scholar
  18. 18.
    Hannon-Lab, Gordon A (2010) FASTX-toolkit: FASTQ/A short-reads pre-processing tools. http://hannonlab.cshl.edu/fastx_toolkit/
  19. 19.
    R Foundation for Statistical Computing (2012) R: A language and environment for statistical computing, 2151st edn. R Foundation for Statistical Computing, Vienna, AustriaGoogle Scholar
  20. 20.
    Gentleman RC, Carey VJ, Bates DM et al (2004) Bioconductor: open software development for computational biology and bioinformatics. Genome Biol 5(10):R80PubMedCrossRefGoogle Scholar
  21. 21.
    Aird D, Ross MG, Chen WS et al (2011) Analyzing and minimizing PCR amplification bias in Illumina sequencing libraries. Genome Biol 12(2):R18. doi: 10.1186/gb-2011-12-2-r18 PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Lukasz J. Kielpinski
    • 1
  • Mette Boyd
    • 1
    • 2
  • Albin Sandelin
    • 1
    • 2
  • Jeppe Vinther
    • 1
  1. 1.Department of BiologyUniversity of CopenhagenCopenhagenDenmark
  2. 2.Biotech Research and Innovation CentreUniversity of CopenhagenCopenhagenDenmark

Personalised recommendations