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High-throughput determination of RNA structures

Abstract

RNA performs and regulates a diverse range of cellular processes, with new functional roles being uncovered at a rapid pace. Interest is growing in how these functions are linked to RNA structures that form in the complex cellular environment. A growing suite of technologies that use advances in RNA structural probes, high-throughput sequencing and new computational approaches to interrogate RNA structure at unprecedented throughput are beginning to provide insights into RNA structures at new spatial, temporal and cellular scales.

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Fig. 1: A timeline of biochemical RNA structure probing.
Fig. 2: A common conceptual core for determining RNA structures with high-throughput sequencing.
Fig. 3: Reverse transcription strategies for detecting RNA modifications.
Fig. 4: Strategies for high-throughput sequencing library preparation.
Fig. 5: Applications of high-throughput RNA structure probing.

The three-dimensional structure in the middle panel of this figure is adapted from ref.17, eLife Sciences Publications, CC BY 4.0.

References

  1. 1.

    Gilbert, W. The RNA world. Nature 319, 618 (1986).

    Google Scholar 

  2. 2.

    Sharp, P. A. The centrality of RNA. Cell 136, 577–580 (2009).

    CAS  PubMed  Google Scholar 

  3. 3.

    Cech, T. R. & Steitz, J. A. The noncoding RNA revolution-trashing old rules to forge new ones. Cell 157, 77–94 (2014).

    CAS  Google Scholar 

  4. 4.

    Strobel, E. J., Watters, K. E., Loughrey, D. & Lucks, J. B. RNA systems biology: uniting functional discoveries and structural tools to understand global roles of RNAs. Curr. Opin. Biotechnol. 39, 182–191 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Gottesman, S. & Storz, G. Bacterial small RNA regulators: versatile roles and rapidly evolving variations. Cold Spring Harb. Perspect. Biol. 3 a003798 (2011).

    PubMed  PubMed Central  Google Scholar 

  6. 6.

    Luco, R. F. & Misteli, T. More than a splicing code: integrating the role of RNA, chromatin and non-coding RNA in alternative splicing regulation. Curr. Opin. Genet. Dev. 21, 366–372 (2011).

    CAS  PubMed  Google Scholar 

  7. 7.

    Rinn, J. L. & Chang, H. Y. Genome regulation by long noncoding RNAs. Annu. Rev. Biochem. 81, 145–166 (2012).

    CAS  Google Scholar 

  8. 8.

    Chappell, J. et al. The centrality of RNA for engineering gene expression. Biotechnol. J. 8, 1379–1395 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Cech, T. R. Structural biology. The ribosome is a ribozyme. Science 289, 878–879 (2000).

    CAS  PubMed  Google Scholar 

  10. 10.

    Al-Hashimi, H. M. & Walter, N. G. RNA dynamics: it is about time. Curr. Opin. Struct. Biol. 18, 321–329 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Mustoe, A. M., Brooks, C. L. & Al-Hashimi, H. M. Hierarchy of RNA functional dynamics. Annu. Rev. Biochem. 83, 441–466 (2014). This is a comprehensive review that emphasizes that RNAs are dynamic molecules, with structural changes that occur across many biologically relevant timescales.

    CAS  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Hermann, T. & Patel, D. J. Adaptive recognition by nucleic acid aptamers. Science 287, 820–825 (2000).

    CAS  PubMed  Google Scholar 

  13. 13.

    Fedor, M. J. & Williamson, J. R. The catalytic diversity of RNAs. Nat. Rev. Mol. Cell Biol. 6, 399–412 (2005).

    CAS  PubMed  Google Scholar 

  14. 14.

    Westhof, E. & Patel, D. J. Nucleic acids. From self-assembly to induced-fit recognition. Curr. Opin. Struct. Biol. 7, 305–309 (1997).

    CAS  PubMed  Google Scholar 

  15. 15.

    Keel, A. Y., Rambo, R. P., Batey, R. T. & Kieft, J. S. A general strategy to solve the phase problem in RNA crystallography. Structure 15, 761–772 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Latham, M. P., Brown, D. J., McCallum, S. A. & Pardi, A. NMR methods for studying the structure and dynamics of RNA. Chembiochem 6, 1492–1505 (2005).

    CAS  PubMed  Google Scholar 

  17. 17.

    Cheng, C. Y. et al. Consistent global structures of complex RNA states through multidimensional chemical mapping. eLife 4, e07600 (2015). This paper combines mutate-and-map and MOHCA-seq to provide higher-order structural information and three-dimensional models of RNAs from chemical probing data.

    PubMed  PubMed Central  Google Scholar 

  18. 18.

    Holley, R. W. et al. Structure of a ribonucleic acid. Science 147, 1462–1465 (1965).

    CAS  PubMed  Google Scholar 

  19. 19.

    Noller, H. F. & Chaires, J. B. Functional modification of 16S ribosomal RNA by kethoxal. Proc. Natl Acad. Sci. USA 69, 3115–3118 (1972).

    CAS  PubMed  Google Scholar 

  20. 20.

    Peattie, D. A. & Gilbert, W. Chemical probes for higher-order structure in RNA. Proc. Natl Acad. Sci. USA 77, 4679–4682 (1980).

    CAS  PubMed  Google Scholar 

  21. 21.

    Qu, H. L., Michot, B. & Bachellerie, J. P. Improved methods for structure probing in large RNAs: a rapid ‘heterologous’ sequencing approach is coupled to the direct mapping of nuclease accessible sites. Application to the 5’ terminal domain of eukaryotic 28S rRNA. Nucleic Acids Res. 11, 5903–5920 (1983).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Moazed, D., Stern, S. & Noller, H. F. Rapid chemical probing of conformation in 16 S ribosomal RNA and 30 S ribosomal subunits using primer extension. J. Mol. Biol. 187, 399–416 (1986).

    CAS  PubMed  Google Scholar 

  23. 23.

    Latham, J. A. & Cech, T. R. Defining the inside and outside of a catalytic RNA molecule. Science 245, 276–282 (1989).

    CAS  PubMed  Google Scholar 

  24. 24.

    Moazed, D., Robertson, J. M. & Noller, H. F. Interaction of elongation factors EF-G and EF-Tu with a conserved loop in 23S RNA. Nature 334, 362–364 (1988).

    CAS  PubMed  Google Scholar 

  25. 25.

    Climie, S. C. & Friesen, J. D. In vivo and in vitro structural analysis of the rplJ mRNA leader of Escherichia coli. Protection by bound L10-L7/L12. J. Biol. Chem. 263, 15166–15175 (1988).

    CAS  PubMed  Google Scholar 

  26. 26.

    Wang, X. D. & Padgett, R. A. Hydroxyl radical “footprinting” of RNA: application to pre-mRNA splicing complexes. Proc. Natl Acad. Sci. USA 86, 7795–7799 (1989).

    CAS  PubMed  Google Scholar 

  27. 27.

    Lavery, R. & Pullman, A. A new theoretical index of biochemical reactivity combining steric and electrostatic factors. An application to yeast tRNAPhe. Biophys. Chem. 19, 171–181 (1984).

    CAS  PubMed  Google Scholar 

  28. 28.

    Soukup, G. A. & Breaker, R. R. Relationship between internucleotide linkage geometry and the stability of RNA. RNA 5, 1308–1325 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Chamberlin, S. I. & Weeks, K. M. Mapping local nucleotide flexibility by selective acylation of 2ʹ-amine substituted RNA. J. Am. Chem. Soc. 122, 216–224 (2000).

    CAS  Google Scholar 

  30. 30.

    Merino, E. J., Wilkinson, K. A., Coughlan, J. L. & Weeks, K. M. RNA structure analysis at single nucleotide resolution by selective 2ʹ-hydroxyl acylation and primer extension (SHAPE). J. Am. Chem. Soc. 127, 4223–4231 (2005).

    CAS  PubMed  Google Scholar 

  31. 31.

    Gherghe, C. M., Mortimer, S. A., Krahn, J. M., Thompson, N. L. & Weeks, K. M. Slow conformational dynamics at C2ʹ-endo nucleotides in RNA. J. Am. Chem. Soc. 130, 8884–8885 (2008). This paper presents a clear connection between the chemical kinetics of the SHAPE probing reaction and the resulting reactivity information gleaned.

    CAS  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Gherghe, C. M., Shajani, Z., Wilkinson, K. A., Varani, G. & Weeks, K. M. Strong correlation between SHAPE chemistry and the generalized NMR order parameter (S2) in RNA. J. Am. Chem. Soc. 130, 12244–12245 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Vicens, Q., Gooding, A. R., Laederach, A. & Cech, T. R. Local RNA structural changes induced by crystallization are revealed by SHAPE. RNA 13, 536–548 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. 34.

    McGinnis, J. L., Dunkle, J. A., Cate, J. H. & Weeks, K. M. The mechanisms of RNA SHAPE chemistry. J. Am. Chem. Soc. 134, 6617–6624 (2012). This paper presents a detailed analysis of the nuanced chemical and structural features of RNAs that give rise to SHAPE reactivities.

    CAS  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Das, R., Laederach, A., Pearlman, S. M., Herschlag, D. & Altman, R. B. SAFA: semi-automated footprinting analysis software for high-throughput quantification of nucleic acid footprinting experiments. RNA 11, 344–354 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Mitra, S., Shcherbakova, I. V., Altman, R. B., Brenowitz, M. & Laederach, A. High-throughput single-nucleotide structural mapping by capillary automated footprinting analysis. Nucleic Acids Res. 36, e63 (2008).

    PubMed  PubMed Central  Google Scholar 

  37. 37.

    Wilkinson, K. A. et al. High-throughput SHAPE analysis reveals structures in HIV-1 genomic RNA strongly conserved across distinct biological states. PLoS Biol. 6, e96 (2008).

    PubMed  PubMed Central  Google Scholar 

  38. 38.

    Tyrrell, J., McGinnis, J. L., Weeks, K. M. & Pielak, G. J. The cellular environment stabilizes adenine riboswitch RNA structure. Biochemistry 52, 8777–8785 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Spitale, R. C. et al. RNA SHAPE analysis in living cells. Nat. Chem. Biol. 9, 18–20 (2013).References 38 and 39 demonstrate that SHAPE probes can be used to probe RNA structures within the complex cellular environment.

    CAS  PubMed  Google Scholar 

  40. 40.

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

    CAS  Google Scholar 

  41. 41.

    Underwood, J. G. et al. FragSeq: transcriptome-wide RNA structure probing using high-throughput sequencing. Nat. Methods 7, 995–1001 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. 42.

    Lucks, J. B. et al. Multiplexed RNA structure characterization with selective 2ʹ-hydroxyl acylation analyzed by primer extension sequencing (SHAPE-Seq). Proc. Natl Acad. Sci. USA 108, 11063–11068 (2011).

    CAS  PubMed  Google Scholar 

  43. 43.

    Rouskin, S., Zubradt, M., Washietl, S., Kellis, M. & Weissman, J. S. Genome-wide probing of RNA structure reveals active unfolding of mRNA structures in vivo. Nature 505, 701–705 (2014).

    CAS  Google Scholar 

  44. 44.

    Ding, Y. et al. In vivo genome-wide profiling of RNA secondary structure reveals novel regulatory features. Nature 505, 696–700 (2014).

    CAS  Google Scholar 

  45. 45.

    Spitale, R. C. et al. Structural imprints in vivo decode RNA regulatory mechanisms. Nature 519, 486–490 (2015). This paper presents the icSHAPE approach that uses adduct-mediated pull-downs of modified RNA for transcriptome-wide analysis of RNA structures probed in the cellular environment.

    CAS  PubMed  PubMed Central  Google Scholar 

  46. 46.

    Talkish, J., May, G., Lin, Y., Woolford, J. L. Jr & McManus, C. J. Mod-seq: high-throughput sequencing for chemical probing of RNA structure. RNA 20, 713–720 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Zheng, Q. et al. Genome-wide double-stranded RNA sequencing reveals the functional significance of base-paired RNAs in Arabidopsis. PLoS Genet. 6, e1001141 (2010).

    PubMed  Google Scholar 

  48. 48.

    Li, F. et al. Global analysis of RNA secondary structure in two metazoans. Cell Rep. 1, 69–82 (2012).

    CAS  PubMed  Google Scholar 

  49. 49.

    Loughrey, D., Watters, K. E., Settle, A. H. & Lucks, J. B. SHAPE-Seq 2.0: systematic optimization and extension of high-throughput chemical probing of RNA secondary structure with next generation sequencing. Nucleic Acids Res. 42, e165 (2014).

    PubMed Central  Google Scholar 

  50. 50.

    Siegfried, N. A., Busan, S., Rice, G. M., Nelson, J. A. & Weeks, K. M. RNA motif discovery by SHAPE and mutational profiling (SHAPE-MaP). Nat. Methods 11, 959–965 (2014). This paper introduces the SHAPE-MaP method, which uses reverse transcriptase mutational signatures to map chemical probe adduct locations..

    CAS  PubMed  PubMed Central  Google Scholar 

  51. 51.

    Hector, R. D. et al. Snapshots of pre-rRNA structural flexibility reveal eukaryotic 40S assembly dynamics at nucleotide resolution. Nucleic Acids Res. 42, 12138–12154 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. 52.

    Incarnato, D., Neri, F., Anselmi, F. & Oliviero, S. Genome-wide profiling of mouse RNA secondary structures reveals key features of the mammalian transcriptome. Genome Biol. 15, 491 (2014).

    PubMed  PubMed Central  Google Scholar 

  53. 53.

    Seetin, M. G., Kladwang, W., Bida, J. P. & Das, R. Massively parallel RNA chemical mapping with a reduced bias MAP-seq protocol. Methods Mol. Biol. 1086, 95–117 (2014).

    CAS  PubMed  Google Scholar 

  54. 54.

    Watters, K. E., Yu, A. M., Strobel, E. J., Settle, A. H. & Lucks, J. B. Characterizing RNA structures in vitro and in vivo with selective 2ʹ-hydroxyl acylation analyzed by primer extension sequencing (SHAPE-Seq). Methods 103, 34–48 (2016). This paper is a good resource for beginners and outlines the background and practice of experimental approaches and computational tools used for generating and analysing high-throughput RNA structure chemical probing data.

    CAS  PubMed  PubMed Central  Google Scholar 

  55. 55.

    Watters, K. E., Abbott, T. R. & Lucks, J. B. Simultaneous characterization of cellular RNA structure and function with in-cell SHAPE-Seq. Nucleic Acids Res. 44, e12 (2016).

    PubMed  Google Scholar 

  56. 56.

    Zubradt, M. et al. DMS-MaPseq for genome-wide or targeted RNA structure probing in vivo. Nat. Methods 14, 75–82 (2017).

    CAS  PubMed  Google Scholar 

  57. 57.

    Ritchey, L. E. et al. Structure-seq2: sensitive and accurate genome-wide profiling of RNA structure in vivo. Nucleic Acids Res. 45, e135 (2017). This paper presents a thorough analysis of several important ligation biases in common chemical probing and HTS approaches and suggests strategies to reduce these biases.

    CAS  PubMed  PubMed Central  Google Scholar 

  58. 58.

    Burlacu, E. et al. High-throughput RNA structure probing reveals critical folding events during early 60S ribosome assembly in yeast. Nat. Commun. 8, 714 (2017).

    PubMed  PubMed Central  Google Scholar 

  59. 59.

    Sexton, A. N., Wang, P. Y., Rutenberg-Schoenberg, M. & Simon, M. D. Interpreting reverse transcriptase termination and mutation events for greater insight into the chemical probing of RNA. Biochemistry 56, 4713–4721 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. 60.

    Novoa, E. M., Boeudoin, J.-D., Giraldez, A. J., Mattick, J. S. & Kellis, M. Best practices for genome-wide RNA structure analysis: combination of mutational profiles and drop-off information. Preprint at BioRxiv https://doi.org/10.1101/176883 (2017).By analysing DMS-probed RNAs with both RT-stop and RT-mutate adduct detection methods, references 59 and 60 find that these methods provide complementary information and that adduct detection approaches are strongly influenced by RNA sequence and structure context.

  61. 61.

    Ehresmann, C. et al. Probing the structure of RNAs in solution. Nucleic Acids Res. 15, 9109–9128 (1987).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. 62.

    Knapp, G. Enzymatic approaches to probing of RNA secondary and tertiary structure. Methods Enzymol. 180, 192–212 (1989).

    CAS  PubMed  Google Scholar 

  63. 63.

    Kwok, C. K., Ding, Y., Tang, Y., Assmann, S. M. & Bevilacqua, P. C. Determination of in vivo RNA structure in low-abundance transcripts. Nat. Commun. 4, 2971 (2013).

    PubMed  Google Scholar 

  64. 64.

    Smola, M. J., Calabrese, J. M. & Weeks, K. M. Detection of RNA-protein interactions in living cells with SHAPE. Biochemistry 54, 6867–6875 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. 65.

    Smola, M. J. et al. SHAPE reveals transcript-wide interactions, complex structural domains, and protein interactions across the Xist lncRNA in living cells. Proc. Natl Acad. Sci. USA 113, 10322–10327 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  66. 66.

    Lee, B. et al. Comparison of SHAPE reagents for mapping RNA structures inside living cells. RNA 23, 169–174 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  67. 67.

    Mitchell, D. III et al. Glyoxals as in vivo RNA structural probes of guanine base pairing. RNA 24, 114–124 (2017).

    PubMed  Google Scholar 

  68. 68.

    Feng, C. et al. Light-activated chemical probing of nucleobase solvent accessibility inside cells. Nat. Chem. Biol. 14, 276–283 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  69. 69.

    Wan, Y., Kertesz, M., Spitale, R. C., Segal, E. & Chang, H. Y. Understanding the transcriptome through RNA structure. Nat. Rev. Genet. 12, 641–655 (2011). This is a thorough Review of high-throughput enzymatic approaches to mapping RNA structure.

    CAS  Google Scholar 

  70. 70.

    Tijerina, P., Mohr, S. & Russell, R. DMS footprinting of structured RNAs and RNA-protein complexes. Nat. Protoc. 2, 2608–2623 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  71. 71.

    Mortimer, S. A., Johnson, J. S. & Weeks, K. M. Quantitative analysis of RNA solvent accessibility by N-silylation of guanosine. Biochemistry 48, 2109–2114 (2009).

    CAS  PubMed  Google Scholar 

  72. 72.

    Brunel, C. & Romby, P. Probing RNA structure and RNA-ligand complexes with chemical probes. Methods Enzymol. 318, 3–21 (2000).

    CAS  PubMed  Google Scholar 

  73. 73.

    McGinnis, J. L., Duncan, C. D. & Weeks, K. M. High-throughput SHAPE and hydroxyl radical analysis of RNA structure and ribonucleoprotein assembly. Methods Enzymol. 468, 67–89 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  74. 74.

    Wilkinson, K. A., Merino, E. J. & Weeks, K. M. RNA SHAPE chemistry reveals nonhierarchical interactions dominate equilibrium structural transitions in tRNA(Asp) transcripts. J. Am. Chem. Soc. 127, 4659–4667 (2005).

    CAS  PubMed  Google Scholar 

  75. 75.

    Bindewald, E. et al. Correlating SHAPE signatures with three-dimensional RNA structures. RNA 17, 1688–1696 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  76. 76.

    Mortimer, S. A. & Weeks, K. M. A fast-acting reagent for accurate analysis of RNA secondary and tertiary structure by SHAPE chemistry. J. Am. Chem. Soc. 129, 4144–4145 (2007).

    CAS  PubMed  Google Scholar 

  77. 77.

    Steen, K. A., Rice, G. M. & Weeks, K. M. Fingerprinting noncanonical and tertiary RNA structures by differential SHAPE reactivity. J. Am. Chem. Soc. 134, 13160–13163 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  78. 78.

    Mortimer, S. A. & Weeks, K. M. Time-resolved RNA SHAPE chemistry. J. Am. Chem. Soc. 130, 16178–16180 (2008).

    CAS  PubMed  Google Scholar 

  79. 79.

    Mortimer, S. A. & Weeks, K. M. C2ʹ-endo nucleotides as molecular timers suggested by the folding of an RNA domain. Proc. Natl Acad. Sci. USA 106, 15622–15627 (2009).

    CAS  PubMed  Google Scholar 

  80. 80.

    Watters, K. E., Strobel, E. J., Yu, A. M., Lis, J. T. & Lucks, J. B. Cotranscriptional folding of a riboswitch at nucleotide resolution. Nat. Struct. Mol. Biol. 23, 1124–1131 (2016). This paper establishes a method of coupling SHAPE-Seq with transcription arrest to capture nascent RNA folding pathways.

    CAS  PubMed  PubMed Central  Google Scholar 

  81. 81.

    Strobel, E. J., Watters, K. E., Nedialkov, Y., Artsimovitch, I. & Lucks, J. B. Distributed biotin-streptavidin transcription roadblocks for mapping cotranscriptional RNA folding. Nucleic Acids Res. 45, e109 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  82. 82.

    Adilakshmi, T., Lease, R. A. & Woodson, S. A. Hydroxyl radical footprinting in vivo: mapping macromolecular structures with synchrotron radiation. Nucleic Acids Res. 34, e64 (2006).

    PubMed  PubMed Central  Google Scholar 

  83. 83.

    Aviran, S. & Pachter, L. Rational experiment design for sequencing-based RNA structure mapping. RNA 20, 1864–1877 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  84. 84.

    Aviran, S., Lucks, J. B. & Pachter, L. RNA structure characterization from chemical mapping experiments. Proc. 2011 49th Annual Allerton Conference on Communication, Control, and Computing 1743–1750 (Monticello, IL, USA, 2011). This paper presents an intuitive formula to calculate chemical probing reactivities from RT-stop information that takes into account reverse transcriptase fall-off and false positive background signals.

  85. 85.

    Kladwang, W. et al. Standardization of RNA chemical mapping experiments. Biochemistry 53, 3063–3065 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  86. 86.

    Homan, P. J. et al. Single-molecule correlated chemical probing of RNA. Proc. Natl Acad. Sci. USA 111, 13858–13863 (2014). This paper presents RING-MaP, which uses high DMS modification rates, multiple adduct detection and computational analyses to identify through-space intermolecular contacts as well as the presence of RNA structural subpopulations that contribute to the ensemble of folds.

    CAS  PubMed  Google Scholar 

  87. 87.

    Krokhotin, A., Mustoe, A. M., Weeks, K. M. & Dokholyan, N. V. Direct identification of base-paired RNA nucleotides by correlated chemical probing. RNA 23, 6–13 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  88. 88.

    Lackey, L., Coria, A., Woods, C., McArthur, E. & Laederach, A. Allele-specific SHAPE-MaP assessment of the effects of somatic variation and protein binding on mRNA structure. RNA 24, 513–528 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  89. 89.

    Fang, R., Moss, W. N., Rutenberg-Schoenberg, M. & Simon, M. D. Probing Xist RNA structure in cells using targeted structure-seq. PLoS Genet. 11, e1005668 (2015).

    PubMed  PubMed Central  Google Scholar 

  90. 90.

    Watters, K. E. et al. Probing of RNA structures in a positive sense RNA virus reveals selection pressure for structural elements. Nucleic Acids Res. 16, 2573–2584 (2018).

    Google Scholar 

  91. 91.

    Mauger, D. M. et al. Functionally conserved architecture of hepatitis C virus RNA genomes. Proc. Natl Acad. Sci. USA 112, 3692–3697 (2015).

    CAS  PubMed  Google Scholar 

  92. 92.

    Metzker, M. L. Sequencing technologies — the next generation. Nat. Rev. Genet. 11, 31–46 (2010).

    CAS  Google Scholar 

  93. 93.

    Hafner, M. et al. RNA-ligase-dependent biases in miRNA representation in deep-sequenced small RNA cDNA libraries. RNA 17, 1697–1712 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  94. 94.

    Kwok, C. K., Ding, Y., Sherlock, M. E., Assmann, S. M. & Bevilacqua, P. C. A hybridization-based approach for quantitative and low-bias single-stranded DNA ligation. Anal. Biochem. 435, 181–186 (2013).

    CAS  PubMed  Google Scholar 

  95. 95.

    Kutchko, K. M. et al. Multiple conformations are a conserved and regulatory feature of the RB1 5ʹ UTR. RNA 21, 1274–1285 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  96. 96.

    Woods, C. T. et al. Comparative visualization of the RNA suboptimal conformational ensemble in vivo. Biophys. J. 113, 290–301 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  97. 97.

    Spasic, A., Assmann, S. M., Bevilacqua, P. C. & Mathews, D. H. Modeling RNA secondary structure folding ensembles using SHAPE mapping data. Nucleic Acids Res. 46, 314–323 (2018).

    PubMed  Google Scholar 

  98. 98.

    Li, H. & Aviran, S. Statistical modeling of RNA structure profiling experiments enables parsimonious reconstruction of structure landscapes. Nat. Commun. 9, 606 (2018). This paper presents a computational method that takes chemical probing data and extracts subpopulations of RNA structures that contribute to the ensemble of RNA folds within the experimental mixture.

    PubMed  PubMed Central  Google Scholar 

  99. 99.

    Mlynsky, V. & Bussi, G. Molecular dynamics simulations reveal an interplay between SHAPE reagent binding and RNA flexibility. J. Phys. Chem. Lett. 9, 313–318 (2018).

    CAS  PubMed  Google Scholar 

  100. 100.

    Choudhary, K. et al. Metrics for rapid quality control in RNA structure probing experiments. Bioinformatics 32, 3575–3583 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  101. 101.

    Busan, S. & Weeks, K. M. Accurate detection of chemical modifications in RNA by mutational profiling (MaP) with ShapeMapper 2. RNA 24, 143–148 (2018).

    CAS  PubMed  Google Scholar 

  102. 102.

    Meyer, S., Carlson, P. D. & Lucks, J. B. Characterizing the structure-function relationship of a naturally ocurring RNA thermometer. Biochemistry 56, 6629–6638 (2017).

    CAS  PubMed  Google Scholar 

  103. 103.

    Choi, E. K., Ulanowicz, K. A., Nguyen, Y. A. H., Frandsen, J. K. & Mitton-Fry, R. M. SHAPE analysis of the htrA RNA thermometer from Salmonella enterica. RNA 23, 1569–1581 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  104. 104.

    Kladwang, W., VanLang, C. C., Cordero, P. & Das, R. A two-dimensional mutate-and-map strategy for non-coding RNA structure. Nat. Chem. 3, 954–962 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  105. 105.

    Rocca-Serra, P. et al. Sharing and archiving nucleic acid structure mapping data. RNA 17, 1204–1212 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  106. 106.

    Cordero, P., Lucks, J. B. & Das, R. An RNA Mapping DataBase for curating RNA structure mapping experiments. Bioinformatics 28, 3006–3008 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  107. 107.

    Yesselman, J. D. et al. Updates to the RNA mapping database (RMDB), version 2. Nucleic Acids Res. 46, D375–D379 (2018).

    PubMed  Google Scholar 

  108. 108.

    Steen, K. A., Malhotra, A. & Weeks, K. M. Selective 2ʹ-hydroxyl acylation analyzed by protection from exoribonuclease. J. Am. Chem. Soc. 132, 9940–9943 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  109. 109.

    Tian, S., Kladwang, W. & Das, R. Allosteric mechanism of the V. vulnificus adenine riboswitch resolved by four-dimensional chemical mapping. eLife 7, e29602 (2018).

    PubMed  PubMed Central  Google Scholar 

  110. 110.

    Deigan, K. E., Li, T. W., Mathews, D. H. & Weeks, K. M. Accurate SHAPE-directed RNA structure determination. Proc. Natl Acad. Sci. USA 106, 97–102 (2009). This paper demonstrates that adding experimental SHAPE data to computational folding algorithms considerably improves RNA structural predictions.

    CAS  PubMed  Google Scholar 

  111. 111.

    Wu, Y. et al. Improved prediction of RNA secondary structure by integrating the free energy model with restraints derived from experimental probing data. Nucleic Acids Res. 43, 7247–7259 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  112. 112.

    Washietl, S., Hofacker, I. L., Stadler, P. F. & Kellis, M. RNA folding with soft constraints: reconciliation of probing data and thermodynamic secondary structure prediction. Nucleic Acids Res. 40, 4261–4272 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  113. 113.

    Lorenz, R., Hofacker, I. L. & Stadler, P. F. RNA folding with hard and soft constraints. Algorithms Mol. Biol. 11, 8 (2016).

    PubMed  PubMed Central  Google Scholar 

  114. 114.

    Lorenz, R., Luntzer, D., Hofacker, I. L., Stadler, P. F. & Wolfinger, M. T. SHAPE directed RNA folding. Bioinformatics 32, 145–147 (2016).

    CAS  PubMed  Google Scholar 

  115. 115.

    Lorenz, R., Wolfinger, M. T., Tanzer, A. & Hofacker, I. L. Predicting RNA secondary structures from sequence and probing data. Methods 103, 86–98 (2016). This is a thorough review of the different computational approaches that use chemical probing data to improve computational RNA structure modelling.

    CAS  PubMed  Google Scholar 

  116. 116.

    Ouyang, Z., Snyder, M. P. & Chang, H. Y. SeqFold: genome-scale reconstruction of RNA secondary structure integrating high-throughput sequencing data. Genome Res. 23, 377–387 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  117. 117.

    Tan, Z., Sharma, G. & Mathews, D. H. Modeling RNA secondary structure with sequence comparison and experimental mapping data. Biophys. J. 113, 330–338 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  118. 118.

    Seetin, M. G. & Mathews, D. H. RNA structure prediction: an overview of methods. Methods Mol. Biol. 905, 99–122 (2012).

    CAS  PubMed  Google Scholar 

  119. 119.

    Sahoo, S., Switnicki, M. P. & Pedersen, J. S. ProbFold: a probabilistic method for integration of probing data in RNA secondary structure prediction. Bioinformatics 32, 2626–2635 (2016).

    CAS  PubMed  Google Scholar 

  120. 120.

    Zarringhalam, K., Meyer, M. M., Dotu, I., Chuang, J. H. & Clote, P. Integrating chemical footprinting data into RNA secondary structure prediction. PLoS ONE 7, e45160 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  121. 121.

    Corley, M., Solem, A., Qu, K., Chang, H. Y. & Laederach, A. Detecting riboSNitches with RNA folding algorithms: a genome-wide benchmark. Nucleic Acids Res. 43, 1859–1868 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  122. 122.

    Parisien, M. & Major, F. The MC-Fold and MC-Sym pipeline infers RNA structure from sequence data. Nature 452, 51–55 (2008).

    CAS  PubMed  Google Scholar 

  123. 123.

    Gherghe, C. M., Leonard, C. W., Ding, F., Dokholyan, N. V. & Weeks, K. M. Native-like RNA tertiary structures using a sequence-encoded cleavage agent and refinement by discrete molecular dynamics. J. Am. Chem. Soc. 131, 2541–2546 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  124. 124.

    Ding, F., Lavender, C. A., Weeks, K. M. & Dokholyan, N. V. Three-dimensional RNA structure refinement by hydroxyl radical probing. Nat. Methods 9, 603–608 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  125. 125.

    Magnus, M. et al. Computational modeling of RNA 3D structures, with the aid of experimental restraints. RNA Biol. 11, 522–536 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  126. 126.

    Sloma, M. F. & Mathews, D. H. Improving RNA secondary structure prediction with structure mapping data. Methods Enzymol. 553, 91–114 (2015).

    CAS  PubMed  Google Scholar 

  127. 127.

    Hajdin, C. E. et al. Accurate SHAPE-directed RNA secondary structure modeling, including pseudoknots. Proc. Natl Acad. Sci. USA 110, 5498–5503 (2013).

    CAS  PubMed  Google Scholar 

  128. 128.

    Sloma, M. F. & Mathews, D. H. Base pair probability estimates improve the prediction accuracy of RNA non-canonical base pairs. PLoS Comput. Biol. 13, e1005827 (2017).

    PubMed  PubMed Central  Google Scholar 

  129. 129.

    Mathews, D. H. et al. Incorporating chemical modification constraints into a dynamic programming algorithm for prediction of RNA secondary structure. Proc. Natl Acad. Sci. USA 101, 7287–7292 (2004).

    CAS  PubMed  Google Scholar 

  130. 130.

    Kladwang, W., VanLang, C. C., Cordero, P. & Das, R. Understanding the errors of SHAPE-directed RNA structure modeling. Biochemistry 50, 8049–8056 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  131. 131.

    Rose, P. W. et al. The RCSB protein data bank: integrative view of protein, gene and 3D structural information. Nucleic Acids Res. 45, D271–D281 (2017).

    CAS  PubMed  Google Scholar 

  132. 132.

    Kalvari, I. et al. Rfam 13.0: shifting to a genome-centric resource for non-coding RNA families. Nucleic Acids Res. 46, D335–D342 (2018).

    PubMed  Google Scholar 

  133. 133.

    McGinnis, J. L. et al. In-cell SHAPE reveals that free 30S ribosome subunits are in the inactive state. Proc. Natl Acad. Sci. USA 112, 2425–2430 (2015).

    CAS  PubMed  Google Scholar 

  134. 134.

    Burkhardt, D. H. et al. Operon mRNAs are organized into ORF-centric structures that predict translation efficiency. eLife 6, e22037 (2017).

    PubMed  PubMed Central  Google Scholar 

  135. 135.

    Guo, J. U. & Bartel, D. P. RNA G-quadruplexes are globally unfolded in eukaryotic cells and depleted in bacteria. Science 353, aaf5371 (2016).

    PubMed  PubMed Central  Google Scholar 

  136. 136.

    Choudhary, K., Ruan, L., Deng, F., Shih, N. & Aviran, S. SEQualyzer: interactive tool for quality control and exploratory analysis of high-throughput RNA structural profiling data. Bioinformatics 33, 441–443 (2017).

    CAS  PubMed  Google Scholar 

  137. 137.

    Mustoe, A. M. et al. Pervasive regulatory functions of mRNA structure revealed by high-resolution SHAPE probing. Cell 173, 181–195.e18 (2018). This paper applies transcriptomic RNA structure probing with a focused analysis on transcripts with high sequencing coverage for high data quality and found that translation efficiency is regulated by the unfolding of ribosome binding site RNA structures.

    CAS  PubMed  Google Scholar 

  138. 138.

    Silverman, I. M. et al. RNase-mediated protein footprint sequencing reveals protein-binding sites throughout the human transcriptome. Genome Biol. 15, R3 (2014).

    PubMed  PubMed Central  Google Scholar 

  139. 139.

    Seemann, S. E. et al. The identification and functional annotation of RNA structures conserved in vertebrates. Genome Res. 27, 1371–1383 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  140. 140.

    Kawaguchi, R. & Kiryu, H. Parallel computation of genome-scale RNA secondary structure to detect structural constraints on human genome. BMC Bioinformatics 17, 203 (2016).

    PubMed  PubMed Central  Google Scholar 

  141. 141.

    Ramani, V., Qiu, R. & Shendure, J. High-throughput determination of RNA structure by proximity ligation. Nat. Biotechnol. 33, 980–984 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  142. 142.

    Lu, Z. et al. RNA duplex map in living cells reveals higher-order transcriptome structure. Cell 165, 1267–1279 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  143. 143.

    Sharma, E., Sterne-Weiler, T., O’Hanlon, D. & Blencowe, B. J. Global mapping of human RNA-RNA interactions. Mol. Cell 62, 618–626 (2016).

    CAS  Google Scholar 

  144. 144.

    Aw, J. G. et al. In vivo mapping of eukaryotic RNA interactomes reveals principles of higher-order organization and regulation. Mol. Cell 62, 603–617 (2016).

    CAS  Google Scholar 

  145. 145.

    Nguyen, T. C. et al. Mapping RNA-RNA interactome and RNA structure in vivo by MARIO. Nat. Commun. 7, 12023 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  146. 146.

    Sugimoto, Y. et al. hiCLIP reveals the in vivo atlas of mRNA secondary structures recognized by Staufen 1. Nature 519, 491–494 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  147. 147.

    Melamed, S. et al. Global mapping of small RNA-target interactions in bacteria. Mol. Cell 63, 884–897 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  148. 148.

    Kudla, G., Granneman, S., Hahn, D., Beggs, J. D. & Tollervey, D. Cross-linking, ligation, and sequencing of hybrids reveals RNA-RNA interactions in yeast. Proc. Natl Acad. Sci. USA 108, 10010–10015 (2011).

    CAS  PubMed  Google Scholar 

  149. 149.

    Li, B., Tambe, A., Aviran, S. & Pachter, L. PROBer provides a general toolkit for analyzing sequencing-based toeprinting assays. Cell Syst. 4, 568–574.e7 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  150. 150.

    Kladwang, W., Cordero, P. & Das, R. A mutate-and-map strategy accurately infers the base pairs of a 35-nucleotide model RNA. RNA 17, 522–534 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  151. 151.

    Tian, S., Cordero, P., Kladwang, W. & Das, R. High-throughput mutate-map-rescue evaluates SHAPE-directed RNA structure and uncovers excited states. RNA 20, 1815–1826 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  152. 152.

    Cheng, C. Y., Kladwang, W., Yesselman, J. D. & Das, R. RNA structure inference through chemical mapping after accidental or intentional mutations. Proc. Natl Acad. Sci. USA 114, 9876–9881 (2017).

    CAS  Google Scholar 

  153. 153.

    Saldi, T., Fong, N. & Bentley, D. L. Transcription elongation rate affects nascent histone pre-mRNA folding and 3ʹ end processing. Genes Dev. 32, 297–308 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  154. 154.

    Wold, B. & Myers, R. M. Sequence census methods for functional genomics. Nat. Methods 5, 19–21 (2008).

    CAS  PubMed  Google Scholar 

  155. 155.

    Leonard, C. W. et al. Principles for understanding the accuracy of SHAPE-directed RNA structure modeling. Biochemistry 52, 588–595 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  156. 156.

    Ayub, M., Hardwick, S. W., Luisi, B. F. & Bayley, H. Nanopore-based identification of individual nucleotides for direct RNA sequencing. Nano Lett. 13, 6144–6150 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  157. 157.

    Rice, G. M., Leonard, C. W. & Weeks, K. M. RNA secondary structure modeling at consistent high accuracy using differential SHAPE. RNA 20, 846–854 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  158. 158.

    Frieda, K. L. & Block, S. M. Direct observation of cotranscriptional folding in an adenine riboswitch. Science 338, 397–400 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  159. 159.

    Rinaldi, A. J., Lund, P. E., Blanco, M. R. & Walter, N. G. The Shine-Dalgarno sequence of riboswitch-regulated single mRNAs shows ligand-dependent accessibility bursts. Nat. Commun. 7, 8976 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  160. 160.

    Xie, Z., Srividya, N., Sosnick, T. R., Pan, T. & Scherer, N. F. Single-molecule studies highlight conformational heterogeneity in the early folding steps of a large ribozyme. Proc. Natl Acad. Sci. USA 101, 534–539 (2004).

    CAS  PubMed  Google Scholar 

  161. 161.

    Cabili, M. N. et al. Localization and abundance analysis of human lncRNAs at single-cell and single-molecule resolution. Genome Biol. 16, 20 (2015).

    PubMed  PubMed Central  Google Scholar 

  162. 162.

    Shah, S. et al. Single-molecule RNA detection at depth by hybridization chain reaction and tissue hydrogel embedding and clearing. Development 143, 2862–2867 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  163. 163.

    Nomakuchi, T. T., Rigo, F., Aznarez, I. & Krainer, A. R. Antisense oligonucleotide-directed inhibition of nonsense-mediated mRNA decay. Nat. Biotechnol. 34, 164–166 (2016).

    CAS  PubMed  Google Scholar 

  164. 164.

    Finkel, R. S. et al. Treatment of infantile-onset spinal muscular atrophy with nusinersen: a phase 2, open-label, dose-escalation study. Lancet 388, 3017–3026 (2016).

    CAS  PubMed  Google Scholar 

  165. 165.

    Wilkinson, K. A. et al. Influence of nucleotide identity on ribose 2ʹ-hydroxyl reactivity in RNA. RNA 15, 1314–1321 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  166. 166.

    Yu, A. M., Evans, M. E. & Lucks, J. B. Estimating RNA structure chemical probing reactivities from reverse transcriptase stops and mutations. Preprint at BioRxiv. https://doi.org/10.1101/292532 (2018).

    Article  Google Scholar 

  167. 167.

    Aviran, S. et al. Modeling and automation of sequencing-based characterization of RNA structure. Proc. Natl Acad. Sci. USA 108, 11069–11074 (2011).

    CAS  PubMed  Google Scholar 

  168. 168.

    Wu, X. & Bartel, D. P. Widespread influence of 3ʹ-end structures on mammalian mRNA processing and stability. Cell 169, 905–917.e11 (2017). This paper applies transcriptome-wide structure probing alongside several experimental approaches to uncover a widespread role for RNA structure in mRNA 3′ end processing.

    CAS  PubMed  PubMed Central  Google Scholar 

  169. 169.

    Wan, Y. et al. Landscape and variation of RNA secondary structure across the human transcriptome. Nature 505, 706–709 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  170. 170.

    Wan, Y. et al. Genome-wide measurement of RNA folding energies. Mol. Cell 48, 169–181 (2012).

    PubMed  PubMed Central  Google Scholar 

  171. 171.

    Takahashi, M. K. et al. Using in-cell SHAPE-Seq and simulations to probe structure-function design principles of RNA transcriptional regulators. RNA 22, 920–933 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  172. 172.

    Bai, Y., Tambe, A., Zhou, K. & Doudna, J. A. RNA-guided assembly of Rev-RRE nuclear export complexes. eLife 3, e03656 (2014).

    PubMed  PubMed Central  Google Scholar 

  173. 173.

    Tang, Y., Assmann, S. M. & Bevilacqua, P. C. Protein structure is related to RNA structural reactivity in vivo. J. Mol. Biol. 428, 758–766 (2016).

    CAS  PubMed  Google Scholar 

  174. 174.

    Cordero, P. & Das, R. Rich RNA structure landscapes revealed by mutate-and-map analysis. PLoS Comput. Biol. 11, e1004473 (2015).

    PubMed  PubMed Central  Google Scholar 

  175. 175.

    Lavender, C. A. et al. Model-free RNA sequence and structure alignment informed by SHAPE probing reveals a conserved alternate secondary structure for 16S rRNA. PLoS Comput. Biol. 11, e1004126 (2015).

    PubMed  PubMed Central  Google Scholar 

  176. 176.

    Poulsen, L. D., Kielpinski, L. J., Salama, S. R., Krogh, A. & Vinther, J. SHAPE Selection (SHAPES) enrich for RNA structure signal in SHAPE sequencing-based probing data. RNA 21, 1042–1052 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

The authors thank A. Coraor, A. Silverman and R. Batey for informative discussions about the chemical kinetic view of reactivities and K. Watters and P. Carlson for assistance with graphics. The authors also thank D. Mathews and C. Khosla for inspiring the connections between the chemical and statistical perspectives of reactivities and V. Gopalan for historical perspectives. They also thank M. Evans for helpful comments on the manuscript. This work was supported by an Arnold and Mabel Beckman Foundation Postdoctoral Fellowship (to E.J.S.), the Tri-Institutional Training Program in Computational Biology and Medicine (via a National Institutes of Health training grant T32GM083937 to A.M.Y.), the National Institute of General Medical Sciences of the National Institutes of Health (grant numbers 1DP2GM110838 and R01GM120582 to J.B.L.) and Searle Funds at the Chicago Community Trust (to J.B.L.). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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Nature Reviews Genetics thanks P. Bevilacqua, L. Ritchey, C. Douds, A. Laederach and the other anonymous reviewer(s) for their contribution to the peer review of this work.

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Correspondence to Julius B. Lucks.

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Supplementary information

Glossary

Secondary structures

The patterns of base pairing interactions in RNA that create helices, loops, bulges, junctions and single-stranded regions. In addition to Watson–Crick base pairs, RNAs can pair in many non-canonical patterns.

High-throughput sequencing

(HTS). A suite of technologies that can be used to sequence millions to billions of DNA molecules simultaneously. Many experiments can be performed at once because bioinformatics tools can be used to distinguish sequence signals between experiments.

Reactivity

A measure of a chemical probing reaction that contains RNA structural information. Typically, high reactivities indicate unstructured regions, while low reactivities indicate structured regions.

Reverse transcription

(RT). The process by which RNA is enzymatically converted into complementary DNA. RT proceeds along the RNA template in the 3′ to 5′ direction.

Chemical probes

Small molecules that chemically react with RNA molecules in a structure-dependent fashion. Reactions produce adducts that can be detected to give a measure of the structure of an RNA.

Watson–Crick face

The edges of RNA bases that form the canonical adenine–uracil and guanine–cytosine base pairs.

Selective 2′-hydroxyl acylation analysed by primer extension

(SHAPE). A technique that uses a class of chemical probes that modifies the RNA backbone. SHAPE probes can be used to interrogate RNA structure at single-nucleotide resolution.

Tertiary structure

The orientation of secondary structure elements and nucleotides that gives rise to sophisticated three-dimensional structures. Tertiary structures can be stabilized by non-covalent interactions and divalent cations.

RT-stop

An event in which reverse transcriptase stops when encountering a chemical probe adduct on an RNA, producing a truncated cDNA that can be used to map the adduct position.

RT-mutate

An event in which reverse transcriptase produces a mutation when encountering a chemical probe adduct on an RNA. This mutation can be used to map the adduct position.

Pseudo-free energy

An energy term introduced into RNA folding algorithms that incorporates chemical probing data to more accurately model RNA structures. It is not a rigorously derived thermodynamic free energy but captures the relationship between high reactivities corresponding to unpaired regions, hence the use of ‘pseudo’ in its name.

Sensitivity

A measure of accuracy of RNA structure prediction equal to the number of true positive pairs predicted divided by the sum of true positive and false negative pairs predicted. Sensitivity is often used in combination with positive predictive value to assess the predictive accuracy of RNA structure models.

Positive predictive value

(PPV). A measure of accuracy of RNA structure prediction equal to the number of true positive pairs predicted divided by the sum of true positive and false positive pairs predicted. PPV is often used in combination with sensitivity to assess the predictive accuracy of RNA structure models. PPV is equivalent to one minus the false discovery rate.

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Strobel, E.J., Yu, A.M. & Lucks, J.B. High-throughput determination of RNA structures. Nat Rev Genet 19, 615–634 (2018). https://doi.org/10.1038/s41576-018-0034-x

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