Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

FragSeq: transcriptome-wide RNA structure probing using high-throughput sequencing

Abstract

Classical approaches to determine structures of noncoding RNA (ncRNA) probed only one RNA at a time with enzymes and chemicals, using gel electrophoresis to identify reactive positions. To accelerate RNA structure inference, we developed fragmentation sequencing (FragSeq), a high-throughput RNA structure probing method that uses high-throughput RNA sequencing of fragments generated by digestion with nuclease P1, which specifically cleaves single-stranded nucleic acids. In experiments probing the entire mouse nuclear transcriptome, we accurately and simultaneously mapped single-stranded RNA regions in multiple ncRNAs with known structure. We probed in two cell types to verify reproducibility. We also identified and experimentally validated structured regions in ncRNAs with, to our knowledge, no previously reported probing data.

This is a preview of subscription content

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Figure 1: Overview of the FragSeq method.
Figure 2: Visual representation of data at progressive stages in the FragSeq algorithm, from genome-mapped reads to cutting scores.
Figure 3: Comparison of FragSeq with previous probing experiments.
Figure 4: FragSeq cutting scores and coverage for ncRNAs with known structures and long C/D box snoRNAs.
Figure 5: FragSeq probing versus conventional nuclease probing of U15b C/D box snoRNA.

Accession codes

Accessions

Gene Expression Omnibus

References

  1. Gesteland, R., Cech, T. & Atkins, J. (eds). The RNA World 3rd edn. (Cold Spring Harbor Laboratory Press, Cold Spring Harbor, New York, USA, 2005).

  2. Affymetrix/Cold Spring Harbor Laboratory ENCODE Transcriptome Project. Post-transcriptional processing generates a diversity of 5′-modified long and short RNAs. Nature 457, 1028–1032 (2009).

  3. Guttman, M. et al. Chromatin signature reveals over a thousand highly conserved large non-coding RNAs in mammals. Nature 458, 223–227 (2009).

    CAS  Article  Google Scholar 

  4. Ambros, V. microRNAs: tiny regulators with great potential. Cell 107, 823–826 (2001).

    CAS  Article  Google Scholar 

  5. Kapranov, P. et al. RNA maps reveal new RNA classes and a possible function for pervasive transcription. Science 316, 1484–1488 (2007).

    CAS  Article  Google Scholar 

  6. Knapp, G. Enzymatic approaches to probing of RNA secondary and tertiary structure. Methods Enzymol. 2, 192–212 (1989).

    Article  Google Scholar 

  7. Low, J.T. & Weeks, K.M. SHAPE-directed RNA secondary structure prediction. Methods 52, 150–158 (2010).

    CAS  Article  Google Scholar 

  8. Machado-Lima, A., del Portillo, H.A. & Durham, A.M. Computational methods in noncoding RNA research. J. Math. Biol. 56, 15–49 (2008).

    Article  Google Scholar 

  9. Crawford, G.E. et al. Genome-wide mapping of DNase hypersensitive sites using massively parallel signature sequencing (MPSS). Genome Res. 16, 123–131 (2006).

    CAS  Article  Google Scholar 

  10. Ying, Q.-L., Stavridis, M., Griffiths, D., Li, M. & Smith, A. Conversion of embryonic stem cells into neuroectodermal precursors in adherent monoculture. Nat. Biotechnol. 21, 183–186 (2003).

    CAS  Article  Google Scholar 

  11. Desai, N.A. & Shankar, V. Single-strand-specific nucleases. FEMS Microbiol. Rev. 26, 457–491 (2003).

    CAS  Article  Google Scholar 

  12. Cameron, V. & Uhlenbeck, O.C. 3′-phosphatase activity in T4 polynucleotide kinase. Biochemistry 16, 5120–5126 (1977).

    CAS  Article  Google Scholar 

  13. Romier, C., Dominguez, R., Lahm, A., Dahl, O. & Suck, D. Recognition of single-stranded DNA by nuclease P1: high resolution crystal structures of complexes with substrate analogs. Proteins 32, 414–424 (1998).

    CAS  Article  Google Scholar 

  14. Naik, A.K. & Raghavan, S.C. P1 nuclease cleavage is dependent on length of the mismatches in DNA. DNA Repair (Amst.) 7, 1384–1391 (2008).

    CAS  Article  Google Scholar 

  15. Parker, K.A. & Steitz, J.A. Structural analyses of the human U3 ribonucleoprotein particle reveal a conserved sequence available for base pairing with pre-rRNA. Mol. Cell. Biol. 7, 2899–2913 (1987).

    CAS  Article  Google Scholar 

  16. Mougin, A., Gottschalk, A., Fabrizio, P., Lührmann, R. & Branlant, C. Direct probing of RNA structure and RNA-protein interactions in purified HeLa cell's and yeast spliceosomal U4/U6.U5 tri-snRNP particles. J. Mol. Biol. 317, 631–649 (2002).

    CAS  Article  Google Scholar 

  17. Granneman, S. et al. Role of pre-rRNA base pairing and 80S complex formation in subnucleolar localization of the U3 snoRNP. Mol. Cell. Biol. 24, 8600–8610 (2004).

    CAS  Article  Google Scholar 

  18. Kass, S., Tyc, K., Steitz, J.A. & Sollner-Webb, B. The U3 small nucleolar ribonucleoprotein functions in the first step of preribosomal RNA processing. Cell 60, 897–908 (1990).

    CAS  Article  Google Scholar 

  19. Peculis, B.A. & Steitz, J.A. Disruption of U8 nucleolar snRNA inhibits 5.8S and 28S rRNA processing in the Xenopus oocyte. Cell 73, 1233–1245 (1993).

    CAS  Article  Google Scholar 

  20. Tycowski, K., Shu, M. & Steitz, J. Requirement for intron-encoded U22 small nucleolar RNA in 18S ribosomal RNA maturation. Science 266, 1558–1561 (1994).

    CAS  Article  Google Scholar 

  21. Wang, Z., Gerstein, M. & Snyder, M. RNA-Seq: a revolutionary tool for transcriptomics. Nat. Rev. Genet. 10, 57–63 (2009).

    CAS  Article  Google Scholar 

  22. Kertesz, M. et al. Genome-wide measurement of RNA secondary structure in yeast. Nature 467, 103–107 (2010).

    CAS  Article  Google Scholar 

  23. Reuter, J.S. & Mathews, D.H. RNAstructure: software for RNA secondary structure prediction and analysis. BMC Bioinformatics 11, 129 (2010).

    Article  Google Scholar 

  24. Mandal, M. & Breaker, R.R. Gene regulation by riboswitches. Nat. Rev. Mol. Cell Biol. 5, 451–463 (2004).

    CAS  Article  Google Scholar 

  25. Maroney, P., Romfo, C. & Nilsen, T. Nuclease protection of RNAs containing site-specific labels: a rapid method for mapping RNA-protein interactions. RNA 6, 1905–1909 (2000).

    CAS  Article  Google Scholar 

  26. Kiss-László, Z., Henry, Y., Bachellerie, J.P., Caizergues-Ferrer, M. & Kiss, T. Site-specific ribose methylation of preribosomal RNA: a novel function for small nucleolar RNAs. Cell 85, 1077–1088 (1996).

    Article  Google Scholar 

  27. Beard, C., Hochedlinger, K., Plath, K., Wutz, A. & Jaenisch, R. Efficient method to generate single-copy transgenic mice by site-specific integration in embryonic stem cells. Genesis 44, 23–28 (2006).

    CAS  Article  Google Scholar 

  28. Skarnes, W.C. Gene trapping methods for the identification and functional analysis of cell surface proteins in mice. Methods Enzymol. 328, 592–615 (2000).

    CAS  Article  Google Scholar 

  29. Sobczak, K., Michlewski, G., de Mezer, M., Krol, J. & Krzyzosiak, W.J. Trinucleotide repeat system for sequence specificity analysis of RNA structure probing reagents. Anal. Biochem. 402, 40–46 (2010).

    CAS  Article  Google Scholar 

  30. Mortazavi, A., Williams, B.A., Mccue, K., Schaeffer, L. & Wold, B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat. Methods 5, 621–628 (2008).

    CAS  Article  Google Scholar 

Download references

Acknowledgements

A.V.U. was supported in part by US National Institutes of Health (NIH) bioinformatics training grant 1 T32 GM070386-01 and by a US National Science Foundation Graduate Research fellowship. S.K. was supported in part by NIH National Human Genome Research Institute grant U41 HG004568-01. C.S.O. was supported by California Institute for Regenerative Medicine training grant T3-00006. This study was funded in part by NIH R01HG004002 to D.H.M. and NIH 1R03DA026061-01 to S.R.S. We thank D. Bernick, S. Kuersten and O. Uhlenbeck for helpful discussions; Y. Ponty for adding the feature to display enzymatic/chemical modifications to VARNA, the program used to visualize our probing data; E. Farias-Hesson and N. Pourmand of the University of California Santa Cruz Genome Sequencing Center for preparing samples; workers at ABI for carrying out the sequencing; and M. Storm and F. Ng of ABI for facilitating that sequencing run.

Author information

Authors and Affiliations

Authors

Contributions

J.G.U. designed and carried out the experiments. A.V.U. designed and carried out the bioinformatics analysis, except for preparing the read mappings, which S.K. did, with C.S.O. contributing data. J.E.M. programmed additional features in the RNAstructure software. J.G.U., A.V.U. and S.R.S. wrote the manuscript. S.R.S., D.H.M., T.M.L. and D.H. directed the research.

Corresponding author

Correspondence to Sofie R Salama.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–12, Supplementary Table 1, Supplementary Notes 1–3, Supplementary Discussion (PDF 9761 kb)

Supplementary Data 1

Stockholm-format (machine-readable) multiple alignment of U15b C/D box snoRNA homologs, containing structure models that were evaluated. See file for detailed comments. (ZIP 1 kb)

Supplementary Data 2

Stockholm-format (machine-readable) multiple alignment of U22 C/D box snoRNA homologs, containing structure models that were evaluated. See file for detailed comments. (ZIP 1 kb)

Supplementary Data 3

Stockholm-format (machine-readable) multiple alignment of U97 C/D box snoRNA homologs, containing structure models that were evaluated. See file for detailed comments. (ZIP 1 kb)

Supplementary Data 4

FASTA-format file of sequences used for filtering out sequencing reads prior to mapping to genome (see Methods). (ZIP 11 kb)

Supplementary Data 5

Six-column BED-format file containing genomic coordinates (mm9 genome assembly) of all RNAs examined in this study. This can be uploaded to the UCSC Genome Browser as a custom track. (ZIP 0 kb)

Supplementary Software

FragSeq algorithm implementation, configuration files and Readme. All FragSeq algorithm software, scripts and configuration files needed to reproduce the analysis in this paper are provided. The Readme file contains complete instructions on how to rerun our analysis. However, read mappings are not provided owing to their large size and have to be downloaded from the GEO (accession number is listed in the paper; see the Readme file). The script dpToVarna.py is also provided (Supplementary Note 3). (ZIP 45 kb)

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Underwood, J., Uzilov, A., Katzman, S. et al. FragSeq: transcriptome-wide RNA structure probing using high-throughput sequencing. Nat Methods 7, 995–1001 (2010). https://doi.org/10.1038/nmeth.1529

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nmeth.1529

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing