Quantitative Biology

, Volume 5, Issue 1, pp 3–24 | Cite as

Comparative and integrative analysis of RNA structural profiling data: current practices and emerging questions

  • Krishna Choudhary
  • Fei Deng
  • Sharon AviranEmail author



Structure profiling experiments provide single-nucleotide information on RNA structure. Recent advances in chemistry combined with application of high-throughput sequencing have enabled structure profiling at transcriptome scale and in living cells, creating unprecedented opportunities for RNA biology. Propelled by these experimental advances, massive data with ever-increasing diversity and complexity have been generated, which give rise to new challenges in interpreting and analyzing these data.


We review current practices in analysis of structure profiling data with emphasis on comparative and integrative analysis as well as highlight emerging questions. Comparative analysis has revealed structural patterns across transcriptomes and has become an integral component of recent profiling studies. Additionally, profiling data can be integrated into traditional structure prediction algorithms to improve prediction accuracy.


To keep pace with experimental developments, methods to facilitate, enhance and refine such analyses are needed. Parallel advances in analysis methodology will complement profiling technologies and help them reach their full potential.


RNA structure profiling high-throughput sequencing RNA secondary structure prediction chemical structure probing SHAPE-Seq 



This work was supported by the National Institutes of Health (NIH) grant (No. HG006860). We thank Chun Kit Kwok and Aviran lab members — Mirko Ledda, Sana Vaziri, Hua Li and Rob Gysel — for insightful comments during the preparation of this manuscript.


  1. 1.
    Sharp, P. A. (2009) The centrality of RNA. Cell, 136, 577–580PubMedCrossRefGoogle Scholar
  2. 2.
    Mortimer, S. A., Kidwell, M. A. and Doudna, J. A. (2014) Insights into RNA structure and function from genome-wide studies. Nat. Rev. Genet., 15, 469–479PubMedCrossRefGoogle Scholar
  3. 3.
    He, L. and Hannon, G. J. (2004) MicroRNAs: small RNAs with a big role in gene regulation. Nat. Rev. Genet., 5, 522–531PubMedCrossRefPubMedCentralGoogle Scholar
  4. 4.
    Mercer, T. R., Dinger, M. E. and Mattick, J. S. (2009) Long noncoding RNAs: insights into functions. Nat. Rev. Genet., 10, 155–159PubMedCrossRefGoogle Scholar
  5. 5.
    Strobel, E. J., Watters, K. E., Loughrey, D. and Lucks, J. B. (2016) RNA systems biology: uniting functional discoveries and structural tools to understand global roles of RNAs. Curr. Opin. Biotechnol., 39, 182–191PubMedPubMedCentralCrossRefGoogle Scholar
  6. 6.
    Al-Hashimi, H. M. (2009) Structural biology: aerial view of the HIV genome. Nature, 460, 696–698PubMedCrossRefGoogle Scholar
  7. 7.
    Gutell, R. R., Lee, J. C. and Cannone, J. J. (2002) The accuracy of ribosomal RNA comparative structure models. Curr. Opin. Struct. Biol., 12, 301–310PubMedCrossRefGoogle Scholar
  8. 8.
    Hofacker, I. L., Fontana,W., Stadler, P. F., Bonhoeffer, L. S., Tacker, M., and Schuster, P. (1994) Fast folding and comparison of RNA secondary structures. Monatsh. Chem., 125, 167–188CrossRefGoogle Scholar
  9. 9.
    Mathews, D. H., Moss, W. N. and Turner, D. H. (2010) Folding and finding RNA secondary structure. Cold Spring Harb. Perspect. Biol., 2, a003665PubMedPubMedCentralCrossRefGoogle Scholar
  10. 10.
    Ehresmann, C., Baudin, F., Mougel, M., Romby, P., Ebel, J.-P. and Ehresmann, B. (1987) Probing the structure of RNAs in solution. Nucleic Acids Res., 15, 9109–9128PubMedPubMedCentralCrossRefGoogle Scholar
  11. 11.
    Weeks, K. M. (2010) Advances in RNA structure analysis by chemical probing. Curr. Opin. Struct. Biol., 20, 295–304PubMedPubMedCentralCrossRefGoogle Scholar
  12. 12.
    Tijerina, P., Mohr, S. and Russell, R. (2007) DMS footprinting of structured RNAs and RNA-protein complexes. Nat. Protoc., 2, 2608–2623PubMedPubMedCentralCrossRefGoogle Scholar
  13. 13.
    Brow, D. A. and Noller, H. F. (1983) Protection of ribosomal RNA from kethoxal in polyribosomes: implication of specific sites in ribosome function. J. Mol. Biol., 163, 27–46PubMedCrossRefGoogle Scholar
  14. 14.
    Tullius, T. D. and Greenbaum, J. A. (2005) Mapping nucleic acid structure by hydroxyl radical cleavage. Curr. Opin. Chem. Biol., 9, 127–134PubMedCrossRefGoogle Scholar
  15. 15.
    Singer, B. (1976) All oxygens in nucleic acids react with carcinogenic ethylating agents. Nature, 264, 333–339PubMedCrossRefGoogle Scholar
  16. 16.
    Fritz, J. J., Lewin, A., Hauswirth, W., Agarwal, A., Grant, M. and Shaw, L. (2002) Development of hammerhead ribozymes to modulate endogenous gene expression for functional studies. Methods, 28, 276–285PubMedCrossRefGoogle Scholar
  17. 17.
    Lindell, M., Romby, P. and Wagner, E. G. H. (2002) Lead(II) as a probe for investigating RNA structure in vivo. RNA, 8, 534–541PubMedPubMedCentralCrossRefGoogle Scholar
  18. 18.
    Lindell, M., Brännvall, M., Wagner, E. G. H. and Kirsebom, L. A. (2005) RNase P RNA in vivo. RNA, 11, 1348–1354PubMedPubMedCentralCrossRefGoogle Scholar
  19. 19.
    Knapp, G. (1989) Enzymatic approaches to probing of RNA secondary and tertiary structure. Methods Enzymol., 180, 192–212PubMedCrossRefGoogle Scholar
  20. 20.
    Wilkinson, K. A., Merino, E. J. andWeeks, K. M. (2006) Selective 2’-hydroxyl acylation analyzed by primer extension (SHAPE): quantitative RNA structure analysis at single nucleotide resolution. Nat. Protoc., 1, 1610–1616PubMedCrossRefGoogle Scholar
  21. 21.
    Zubradt, M., Gupta, P., Persad, S., Lambowitz, A. M., Weissman, J. S. and Rouskin, S. (2017) DMS-MaPseq for genome-wide or targeted RNA structure probing in vivo. Nat. Methods, 14, 75–82PubMedCrossRefGoogle Scholar
  22. 22.
    Smola, M. J., Rice, G. M., Busan, S., Siegfried, N. A. and Weeks, K. M. (2015) Selective 2’-hydroxyl acylation analyzed by primer extension and mutational profiling (SHAPE-MaP) for direct, versatile and accurate RNA structure analysis. Nat. Protoc., 10, 1643–1669PubMedPubMedCentralCrossRefGoogle Scholar
  23. 23.
    Watters, K. E., Yu, A. M., Strobel, E. J., Settle, A. H. and Lucks, J. B. (2016) Characterizing RNA structures in vitro and in vivo with selective 2’-hydroxyl acylation analyzed by primer extension sequencing (SHAPE-Seq). Methods, 103, 34–48PubMedPubMedCentralCrossRefGoogle Scholar
  24. 24.
    Poulsen, L. D., Kielpinski, L. J., Salama, S. R., Krogh, A. and Vinther, J. (2015) SHAPE Selection (SHAPES) enrich for RNA structure signal in SHAPE sequencing-based probing data. RNA, 21, 1042–1052PubMedPubMedCentralCrossRefGoogle Scholar
  25. 25.
    Hector, R. D., Burlacu, E., Aitken, S., Le Bihan, T., Tuijtel, M., Zaplatina, A., Cook, A. G. and Granneman, S. (2014) Snapshots of pre-rRNA structural flexibility reveal eukaryotic 40S assembly dynamics at nucleotide resolution. Nucleic Acids Res., 42, 12138–12154PubMedPubMedCentralCrossRefGoogle Scholar
  26. 26.
    Rouskin, S., Zubradt, M., Washietl, S., Kellis, M. and Weissman, J. S. (2014) Genome-wide probing of RNA structure reveals active unfolding of mRNA structures in vivo. Nature, 505, 701–705PubMedCrossRefGoogle Scholar
  27. 27.
    Kwok, C. K., Ding, Y., Tang, Y., Assmann, S. M. and Bevilacqua, P. C. (2013) Determination of in vivo RNA structure in low-abundance transcripts. Nat. Commun., 4, 2971PubMedCrossRefGoogle Scholar
  28. 28.
    Ding, Y., Tang, Y., Kwok, C. K., Zhang, Y., Bevilacqua, P. C. and Assmann, S. M. (2013) In vivo genome-wide profiling of RNA secondary structure reveals novel regulatory features. Nature, 505, 696–700PubMedCrossRefGoogle Scholar
  29. 29.
    Ding, Y., Kwok, C. K., Tang, Y., Bevilacqua, P. C. and Assmann, S. M. (2015) Genome-wide profiling of in vivo RNA structure at singlenucleotide resolution using structure-seq. Nat. Protoc., 10, 1050–1066PubMedCrossRefGoogle Scholar
  30. 30.
    Kertesz, M., Wan, Y., Mazor, E., Rinn, J. L., Nutter, R. C., Chang, H. Y. and Segal, E. (2010) Genome-wide measurement of RNA secondary structure in yeast. Nature, 467, 103–107PubMedCrossRefGoogle Scholar
  31. 31.
    Underwood, J. G., Uzilov, A. V., Katzman, S., Onodera, C. S., Mainzer, J. E., Mathews, D. H., Lowe, T. M., Salama, S. R.and Haussler, D. (2010) FragSeq: transcriptome-wide RNA structure probing using high-throughput sequencing. Nat. Methods, 7, 995–1001PubMedPubMedCentralCrossRefGoogle Scholar
  32. 32.
    Lucks, J. B., Mortimer, S. A., Trapnell, C., Luo, S., Aviran, S., Schroth, G. P., Pachter, L., Doudna, J. A. and Arkin, A. P. (2011) Multiplexed RNA structure characterization with selective 2’-hydroxyl acylation analyzed by primer extension sequencing (SHAPE-Seq). Proc. Natl. Acad. Sci. USA, 108, 11063–11068PubMedPubMedCentralCrossRefGoogle Scholar
  33. 33.
    Loughrey, D., Watters, K. E., Settle, A. H. and Lucks, J. B. (2014) SHAPE-Seq 2.0: systematic optimization and extension of highthroughput chemical probing of RNA secondary structure with next generation sequencing. Nucleic Acids Res, 42, 000CrossRefGoogle Scholar
  34. 34.
    Wan, Y., Qu, K., Ouyang, Z. and Chang, H. Y. (2013) Genome-wide mapping of RNA structure using nuclease digestion and highthroughput sequencing. Nat. Protoc., 8, 849–869PubMedCrossRefGoogle Scholar
  35. 35.
    Talkish, J., May, G., Lin, Y., Woolford, J. L. and McManus, C. J. (2014) Mod-seq: high-throughput sequencing for chemical probing of RNA structure. RNA, 20, 713–720PubMedPubMedCentralCrossRefGoogle Scholar
  36. 36.
    Incarnato, D., Neri, F., Anselmi, F. and Oliviero, S. (2014) Genomewide profiling of mouse RNA secondary structures reveals key features of the mammalian transcriptome. Genome Biol., 15, 491PubMedPubMedCentralCrossRefGoogle Scholar
  37. 37.
    Kielpinski, L. J. and Vinther, J. (2014) Massive parallel-sequencingbased hydroxyl radical probing of RNA accessibility. Nucleic Acids Res., 42, e70CrossRefGoogle Scholar
  38. 38.
    Seetin, M. G., Kladwang, W., Bida, J. P. and Das, R. (2014) Massively parallel RNA chemical mapping with a reduced bias MAP-seq protocol. In RNA Folding: Methods and Protocols, 95–117. New York: Humana PressCrossRefGoogle Scholar
  39. 39.
    Siegfried, N. A., Busan, S., Rice, G. M., Nelson, J. A. and Weeks, K. M. (2014) RNA motif discovery by SHAPE and mutational profiling (SHAPE-MaP). Nat. Methods, 11, 959–965PubMedPubMedCentralCrossRefGoogle Scholar
  40. 40.
    Spitale, R. C., Flynn, R. A., Zhang, Q. C., Crisalli, P., Lee, B., Jung, J.-W., Kuchelmeister, H. Y., Batista, P. J., Torre, E. A., Kool, E. T., et al. (2015) Structural imprints in vivo decode RNA regulatory mechanisms. Nature. 519, 486–490PubMedPubMedCentralCrossRefGoogle Scholar
  41. 41.
    Kwok, C. K., Sahakyan, A. B. and Balasubramanian, S. (2016) Structural analysis using SHALiPE to reveal RNA G-quadruplex formation in human precursor microRNA. Angew. Chem. Int. Ed. Engl., 55, 8958–8961PubMedCrossRefGoogle Scholar
  42. 42.
    Kwok, C. K., Marsico, G., Sahakyan, A. B., Chambers, V. S. and Balasubramanian, S. (2016) rG4-seq reveals widespread formation of G-quadruplex structures in the human transcriptome. Nat. Methods, 13, 841–844PubMedCrossRefGoogle Scholar
  43. 43.
    Kwok, C. K., Tang, Y., Assmann, S. M. and Bevilacqua, P. C. (2015) The RNA structurome: transcriptome-wide structure probing with next-generation sequencing. Trends Biochem. Sci., 40, 221–232PubMedCrossRefGoogle Scholar
  44. 44.
    Lu, Z. and Chang, H. Y. (2016) Decoding the RNA structurome. Curr. Opin. Struct. Biol., 36, 142–148PubMedPubMedCentralCrossRefGoogle Scholar
  45. 45.
    Kwok, C. K. (2016) Dawn of the in vivo RNA structurome and interactome. Biochem. Soc. Trans., 44, 1395–1410PubMedCrossRefGoogle Scholar
  46. 46.
    Kubota, M., Chan, D. and Spitale, R. C. (2015) RNA structure: merging chemistry and genomics for a holistic perspective. BioEssays, 37, 1129–1138PubMedCrossRefGoogle Scholar
  47. 47.
    Low, J. T. and Weeks, K. M. (2010) SHAPE-directed RNA secondary structure prediction. Methods, 52, 150–158PubMedPubMedCentralCrossRefGoogle Scholar
  48. 48.
    Lorenz, R., Luntzer, D., Hofacker, I. L., Stadler, P. F. and Wolfinger, M. T. (2015) SHAPE directed RNA folding. Bioinformatics, 32, 145–147PubMedPubMedCentralGoogle Scholar
  49. 49.
    Merino, E. J., Wilkinson, K. A., Coughlan, J. L. and Weeks, K. M. (2005) RNA structure analysis at single nucleotide resolution by selective 2’-hydroxyl acylation and primer extension (SHAPE). J. Am. Chem. Soc., 127, 4223–4231PubMedCrossRefGoogle Scholar
  50. 50.
    Lavery, R. and Pullman, A. (1984) A new theoretical index of biochemical reactivity combining steric and electrostatic factors: an application to yeast tRNAPhe. Biophys. Chem., 19, 171–181PubMedCrossRefGoogle Scholar
  51. 51.
    McGinnis, J. L., Dunkle, J. A., Cate, J. H. and Weeks, K. M. (2012) The mechanisms of RNA SHAPE chemistry. J. Am. Chem. Soc., 134, 6617–6624PubMedPubMedCentralCrossRefGoogle Scholar
  52. 52.
    Eddy, S. R. (2014) Computational analysis of conserved RNA secondary structure in transcriptomes and genomes. Annu. Rev. Biophys., 43, 433–456PubMedPubMedCentralCrossRefGoogle Scholar
  53. 53.
    Kutchko, K. M. and Laederach, A. (2016) Transcending the prediction paradigm: novel applications of SHAPE to RNA function and evolution. WIREs RNA, 8, e1374CrossRefGoogle Scholar
  54. 54.
    Aviran, S. and Pachter, L. (2014) Rational experiment design for sequencing-based RNA structure mapping. RNA, 20, 1864–1877PubMedPubMedCentralCrossRefGoogle Scholar
  55. 55.
    Wan, Y., Qu, K., Zhang, Q. C., Flynn, R. A., Manor, O., Ouyang, Z., Zhang, J., Spitale, R. C., Snyder, M. P., Segal, E., et al. (2014) Landscape and variation of RNA secondary structure across the human transcriptome. Nature, 505, 706–709PubMedPubMedCentralCrossRefGoogle Scholar
  56. 56.
    Ritz, J., Martin, J. S. and Laederach, A. (2012) Evaluating our ability to predict the structural disruption of RNA by SNPs. BMC Genomics, 13, S6PubMedPubMedCentralCrossRefGoogle Scholar
  57. 57.
    Watters, K. E., Abbott, T. R. and Lucks, J. B. (2016) Simultaneous characterization of cellular RNA structure and function with in-cell SHAPE-Seq. Nucleic Acids Res., 44, e12CrossRefGoogle Scholar
  58. 58.
    Bai, Y., Tambe, A., Zhou, K. and Doudna, J. A. (2014) RNA-guided assembly of Rev-RRE nuclear export complexes. eLife, 3, e03656CrossRefGoogle Scholar
  59. 59.
    Choudhary, K., Shih, N. P., Deng, F., Ledda, M., Li, B. and Aviran, S. (2016) Metrics for rapid quality control in RNA structure probing experiments. Bioinformatics, 32, 3575–3583PubMedPubMedCentralGoogle Scholar
  60. 60.
    Aviran, S., Lucks, J. B. and Pachter, L. (2011) RNA structure characterization from chemical mapping experiments. In the 49th Annual Allerton Conference on Communication, Control, and Computing, pages 1743–1750Google Scholar
  61. 61.
    Wan, Y., Kertesz, M., Spitale, R. C., Segal, E. and Chang, H. Y. (2011) Understanding the transcriptome through RNA structure. Nat. Rev. Genet., 12, 641–655PubMedCrossRefGoogle Scholar
  62. 62.
    McCaskill, J. S. (1990) The equilibrium partition function and base pair binding probabilities for RNA secondary structure. Biopolymers, 29, 1105–1119PubMedCrossRefGoogle Scholar
  63. 63.
    Ding, Y. and Lawrence, C. E. (2003) A statistical sampling algorithm for RNA secondary structure prediction. Nucleic Acids Res., 31, 7280–7301PubMedPubMedCentralCrossRefGoogle Scholar
  64. 64.
    Rogers, E. and Heitsch, C. (2016) New insights from cluster analysis methods for RNA secondary structure prediction. Wiley Interdiscip. Rev. RNA, 7, 278–294PubMedPubMedCentralCrossRefGoogle Scholar
  65. 65.
    Quarrier, S., Martin, J. S., Davis-Neulander, L., Beauregard, A. and Laederach, A. (2010) Evaluation of the information content of RNA structure mapping data for secondary structure prediction. RNA, 16, 1108–1117PubMedPubMedCentralCrossRefGoogle Scholar
  66. 66.
    Bullard, J. H., Purdom, E., Hansen, K. D. and Dudoit, S. (2010) Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments. BMC Bioinformatics, 11, 94PubMedPubMedCentralCrossRefGoogle Scholar
  67. 67.
    Marioni, J. C., Mason, C. E., Mane, S. M., Stephens, M. and Gilad, Y. (2008) RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome Res., 18, 1509–1517PubMedPubMedCentralCrossRefGoogle Scholar
  68. 68.
    Guo, J. U. and Bartel, D. P. (2016) RNA G-quadruplexes are globally unfolded in eukaryotic cells and depleted in bacteria. Science, 353, aaf5371PubMedPubMedCentralCrossRefGoogle Scholar
  69. 69.
    Robinson, M. D., McCarthy, D. J. and Smyth, G. K. (2010) edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics, 26, 139–140PubMedCrossRefGoogle Scholar
  70. 70.
    Anders, S., and Huber, W. (2012) Differential expression of RNA-Seq data at the gene level-the DESeq package. Heidelberg: European Molecular Biology LaboratoryGoogle Scholar
  71. 71.
    Law, C. W., Chen, Y., Shi, W. and Smyth, G. K. (2014) voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol., 15, R29PubMedPubMedCentralCrossRefGoogle Scholar
  72. 72.
    Leamy, K. A., Assmann, S. M., Mathews, D. H. and Bevilacqua, P. C. (2016) Bridging the gap between in vitro and in vivo RNA folding. Q. Rev. Biophys., 49, e10CrossRefGoogle Scholar
  73. 73.
    Hu, X., Wu, Y., Lu, Z. J. and Yip, K. Y. (2015) Analysis of sequencing data for probing RNA secondary structures and protein- RNA binding in studying posttranscriptional regulations. Brief. Bioinform., 17,1032–1043PubMedGoogle Scholar
  74. 74.
    Cordero, P., Kladwang, W., VanLang, C. C. and Das, R. (2012) Quantitative dimethyl sulfate mapping for automated RNA secondary structure inference. Biochemistry, 51, 7037–7039PubMedPubMedCentralCrossRefGoogle Scholar
  75. 75.
    Lee, B., Flynn, R. A., Kadina, A., Guo, J. K., Kool, E. T. and Chang, H. Y. (2016) Comparison of SHAPE reagents for mapping RNA structures inside living cells. RNA, rna.058784.116Google Scholar
  76. 76.
    Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L. and Wold, B. (2008) Mapping and quantifying mammalian transcriptomes by RNASeq. Nat. Methods, 5, 621–628PubMedCrossRefGoogle Scholar
  77. 77.
    Sorefan, K., Pais, H., Hall, A. E., Kozomara, A., Griffiths-Jones, S., Moulton, V. and Dalmay, T. (2012) Reducing ligation bias of small RNAs in libraries for next generation sequencing. Silence, 3, 4PubMedPubMedCentralCrossRefGoogle Scholar
  78. 78.
    Roberts, A., Trapnell, C., Donaghey, J., Rinn, J. L. and Pachter, L. (2011) Improving RNA-Seq expression estimates by correcting for fragment bias. Genome Biol., 12, R22PubMedPubMedCentralCrossRefGoogle Scholar
  79. 79.
    Li, B., Tambe, A., Aviran, S. and Pachter, L. (2016) Prober: a general toolkit for analyzing sequencing-based ‘toeprinting’ assays. bioRxiv, 063107Google Scholar
  80. 80.
    Aviran, S., Trapnell, C., Lucks, J. B., Mortimer, S. A., Luo, S., Schroth, G. P., Doudna, J. A., Arkin, A. P. and Pachter, L. (2011) Modeling and automation of sequencing-based characterization of RNA structure. Proc. Natl. Acad. Sci. USA, 108, 11069–11074PubMedPubMedCentralCrossRefGoogle Scholar
  81. 81.
    Selega, A., Sirocchi, C., Iosub, I., Granneman, S. and Sanguinetti, G. (2017) Robust statistical modeling improves sensitivity of highthroughput RNA structure probing experiments. Nat. Methods, 14, 83–89PubMedCrossRefGoogle Scholar
  82. 82.
    Deigan, K. E., Li, T. W., Mathews, D. H. and Weeks, K. M. (2009) Accurate SHAPE-directed RNA structure determination. Proc. Natl. Acad. Sci. USA, 106, 97–102PubMedCrossRefGoogle Scholar
  83. 83.
    Sloma, M. F. and Mathews, D. H. (2015) Improving RNA secondary structure prediction with structure mapping data. Methods Enzymol., 553, 91–114PubMedCrossRefGoogle Scholar
  84. 84.
    Trapnell, C., Williams, B. A., Pertea, G., Mortazavi, A., Kwan, G., van Baren, M. J., Salzberg, S. L., Wold, B. J., and Pachter, L. (2010) Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat. Biotechnol., 28, 511–515PubMedPubMedCentralCrossRefGoogle Scholar
  85. 85.
    Li, B. and Dewey, C. N. (2011) RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics, 12, 323PubMedPubMedCentralCrossRefGoogle Scholar
  86. 86.
    Smola, M. J., Calabrese, J. M. and Weeks, K. M. (2015) Detection of RNA-protein interactions in living cells with SHAPE. Biochemistry, 54, 6867–6875PubMedPubMedCentralCrossRefGoogle Scholar
  87. 87.
    Smola, M. J., Christy, T.W., Inoue, K., Nicholson, C. O., Friedersdorf, M., Keene, J. D., Lee, D. M., Calabrese, J. M. and Weeks, K. M. (2016) SHAPE reveals transcript-wide interactions, complex structural domains, and protein interactions across the Xist lncRNA in living cells. Proc. Natl. Acad. Sci. USA, 113, 10322–10327PubMedPubMedCentralCrossRefGoogle Scholar
  88. 88.
    Solem, A. C., Halvorsen, M., Ramos, S. B. and Laederach, A. (2015) The potential of the riboSNitch in personalized medicine. Wiley Interdiscip. Rev. RNA, 6, 517–532PubMedPubMedCentralCrossRefGoogle Scholar
  89. 89.
    Wan, Y., Qu, K., Ouyang, Z., Kertesz, M., Li, J., Tibshirani, R., Makino, D. L., Nutter, R. C., Segal, E. and Chang, H. Y. (2012) Genome-wide measurement of RNA folding energies. Mol. Cell, 48, 169–181PubMedPubMedCentralCrossRefGoogle Scholar
  90. 90.
    Righetti, F., Nuss, A. M., Twittenhoff, C., Beele, S., Urban, K., Will, S., Bernhart, S. H., Stadler, P. F., Dersch, P. and Narberhaus, F. (2016) Temperature-responsive in vitro RNA structurome of Yersinia pseudotuberculosis. Proc. Natl. Acad. Sci. USA, 113, 7237–7242PubMedPubMedCentralCrossRefGoogle Scholar
  91. 91.
    Corley, M., Solem, A., Qu, K., Chang, H. Y.and Laederach, A. (2015) Detecting riboSNitches with RNA folding algorithms: a genome-wide benchmark. Nucleic Acids Res., 43,1859–1868PubMedPubMedCentralCrossRefGoogle Scholar
  92. 92.
    Abdullah, M. B. (1990) On a robust correlation coefficient. Statistician, 39, 455–460CrossRefGoogle Scholar
  93. 93.
    Goodwin, L. D. and Leech, N. L. (2006) Understanding correlation: factors that affect the size of r. J. Exp. Educ., 74, 249–266CrossRefGoogle Scholar
  94. 94.
    Müller, R. and Büttner, P. (1994) A critical discussion of intraclass correlation coefficients. Stat. Med., 13, 2465–2476PubMedCrossRefGoogle Scholar
  95. 95.
    Gastwirth, J. L. (1972) The estimation of the Lorenz curve and Gini index. Rev. Econ. Stat., 54, 306–316CrossRefGoogle Scholar
  96. 96.
    Eddy, S. R. and Durbin, R. (1994) RNA sequence analysis using covariance models. Nucleic Acids Res., 22, 2079–2088PubMedPubMedCentralCrossRefGoogle Scholar
  97. 97.
    Zhang, J.-H., Chung, T. D. and Oldenburg, K. R. (1999) A simple statistical parameter for use in evaluation and validation of high throughput screening assays. J. Biomol. Screen., 4, 67–73PubMedCrossRefGoogle Scholar
  98. 98.
    Pollom, E., Dang, K. K., Potter, E. L., Gorelick, R. J., Burch, C. L., Weeks, K. M. and Swanstrom, R. (2013) Comparison of SIV and HIV-1 genomic RNA structures reveals impact of sequence evolution on conserved and non-conserved structural motifs. PLoS Pathog., 9, e1003294CrossRefGoogle Scholar
  99. 99.
    Cowell, F. A. and Victoria-Feser, M.-P. (1996) Robustness properties of inequality measures. Econometrica, 64, 77–101CrossRefGoogle Scholar
  100. 100.
    Liang, R., Kierzek, E., Kierzek, R. and Turner, D. H. (2010) Comparisons between chemical mapping and binding to isoenergetic oligonucleotide microarrays reveal unexpected patterns of binding to the Bacillus subtilis RNase P RNA specificity domain. Biochemistry, 49, 8155–8168PubMedPubMedCentralCrossRefGoogle Scholar
  101. 101.
    Hawkes, E. J., Hennelly, S. P., Novikova, I. V., Irwin, J. A., Dean, C. and Sanbonmatsu, K. Y. (2016) COOLAIR antisense RNAs form evolutionarily conserved elaborate secondary structures. Cell Reports, 16, 3087–3096PubMedCrossRefGoogle Scholar
  102. 102.
    Xue, Z., Hennelly, S., Doyle, B., Gulati, A. A., Novikova, I. V., Sanbonmatsu, K. Y. and Boyer, L. A. (2016) A G-rich motif in the lncRNA braveheart interacts with a zinc-finger transcription factor to specify the cardiovascular lineage. Mol. Cell, 64, 37–50PubMedCrossRefGoogle Scholar
  103. 103.
    Rice, G. M., Leonard, C.W. andWeeks, K. M. (2014) RNA secondary structure modeling at consistent high accuracy using differential SHAPE. RNA, 20, 846–854PubMedPubMedCentralCrossRefGoogle Scholar
  104. 104.
    Wu, Y., Shi, B., Ding, X., Liu, T., Hu, X., Yip, K. Y., Yang, Z. R., Mathews, D. H., and Lu. Z. J. (2015) Improved prediction of RNA secondary structure by integrating the free energy model with restraints derived from experimental probing data. Nucleic acids res., 43, 7247–7259PubMedPubMedCentralCrossRefGoogle Scholar
  105. 105.
    Choudhary, K., Ruan, L., Deng, F., Shih, N. and Aviran, S. (2016) SEQualyzer: interactive tool for quality control and exploratory analysis of high-throughput RNA structural profiling data. Bioinformatics, btw627Google Scholar
  106. 106.
    Rother, K., Rother, M., Skiba, P. and Bujnicki, J. M. (2014) Automated modeling of RNA 3D structure. In RNA Sequence, Structure, and Function: Computational and Bioinformatic Methods, 395–415. New York: Humana PressCrossRefGoogle Scholar
  107. 107.
    Tabaska, J. E., Cary, R. B., Gabow, H. N. and Stormo, G. D. (1998) An RNA folding method capable of identifying pseudoknots and base triples. Bioinformatics, 14, 691–699PubMedCrossRefGoogle Scholar
  108. 108.
    Rivas, E. and Eddy, S. R. (1999) A dynamic programming algorithm for RNA structure prediction including pseudoknots. J. Mol. Biol., 285, 2053–2068PubMedCrossRefGoogle Scholar
  109. 109.
    Lyngsø, R. B. and Pedersen, C. N. (2000) RNA pseudoknot prediction in energy-based models. J. Comput. Biol., 7, 409–427PubMedCrossRefGoogle Scholar
  110. 110.
    Ruan, J., Stormo, G. D. and Zhang, W. (2004) An iterated loop matching approach to the prediction of RNA secondary structures with pseudoknots. Bioinformatics, 20, 58–66PubMedCrossRefGoogle Scholar
  111. 111.
    Reeder, J. and Giegerich, R. (2004) Design, implementation and evaluation of a practical pseudoknot folding algorithm based on thermodynamics. BMC Bioinformatics, 5, 104PubMedPubMedCentralCrossRefGoogle Scholar
  112. 112.
    Ren, J., Rastegari, B., Condon, A. and Hoos, H. H. (2005) HotKnots: heuristic prediction of RNA secondary structures including pseudoknots. RNA, 11, 1494–1504PubMedPubMedCentralCrossRefGoogle Scholar
  113. 113.
    Cao, S. and Chen, S.-J. (2006) Predicting RNA pseudoknot folding thermodynamics. Nucleic Acids Res., 34, 2634–2652PubMedPubMedCentralCrossRefGoogle Scholar
  114. 114.
    Reeder, J., Steffen, P. and Giegerich, R. (2007) pknotsRG: RNA pseudoknot folding including near-optimal structures and sliding windows. Nucleic Acids Res., 35, W320–W324PubMedPubMedCentralCrossRefGoogle Scholar
  115. 115.
    Sato, K., Kato, Y., Hamada, M., Akutsu, T. and Asai, K. (2011) IPknot: fast and accurate prediction of RNA secondary structures with pseudoknots using integer programming. Bioinformatics, 27, i85–i93Google Scholar
  116. 116.
    Andronescu, M., Condon, A., Turner, D. H. and Mathews, D. H. (2014) The determination of RNA folding nearest neighbor parameters. In RNA Sequence, Structure, and Function: Computational and Bioinformatic Methods, 45–70. New York: Humana PressCrossRefGoogle Scholar
  117. 117.
    Xia, T., SantaLucia, J., Burkard, M. E., Kierzek, R., Schroeder, S. J., Jiao, X., Cox, C., and Turner, D. H. (1998) Thermodynamic parameters for an expanded Nearest-Neighbor model for formation of RNA duplexes withWatson-Crick base pairs. Biochemistry, 14719–14735Google Scholar
  118. 118.
    Mathews, D. H., Sabina, J., Zuker, M. and Turner, D. H. (1999) Expanded sequence dependence of thermodynamic parameters improves prediction of RNA secondary structure. J. Mol. Biol., 288, 911–940PubMedCrossRefGoogle Scholar
  119. 119.
    Nussinov, R., Pieczenik, G., Griggs, J. R. and Kleitman, D. J. (1978) Algorithms for loop matchings. SIAM J. Appl. Math., 35, 68–82CrossRefGoogle Scholar
  120. 120.
    Waterman, M. S. and Smith, T. F. (1978) RNA secondary structure: a complete mathematical analysis. Math. Biosci., 42, 257–266CrossRefGoogle Scholar
  121. 121.
    Nussinov, R. and Jacobson, A. B. (1980) Fast algorithm for predicting the secondary structure of single-stranded RNA. Proc. Natl. Acad. Sci. USA, 77, 6309–6313PubMedPubMedCentralCrossRefGoogle Scholar
  122. 122.
    Zuker, M. and Stiegler, P. (1981) Optimal computer folding of large RNA sequences using thermodynamics and auxiliary information. Nucleic Acids Res., 9, 133–148PubMedPubMedCentralCrossRefGoogle Scholar
  123. 123.
    Zuker, M. and Sankoff, D. (1984) RNA secondary structures and their prediction. Bull. Math. Biol., 46, 591–621CrossRefGoogle Scholar
  124. 124.
    Markham, N. R. and Zuker, M. (2008) UNAFold. In Bioinformatics: Structure, Function and Applications, 3–31. New York: Humana PressCrossRefGoogle Scholar
  125. 125.
    Reuter, J. S. and Mathews, D. H. (2010) RNAstructure: software for RNA secondary structure prediction and analysis. BMC Bioinformatics, 11, 129PubMedPubMedCentralCrossRefGoogle Scholar
  126. 126.
    Lorenz, R., Bernhart, S. H., Höner Zu Siederdissen, C., Tafer, H., Flamm, C., Stadler, P. F. and Hofacker, I. L. (2011) ViennaRNA package 2.0. Algorithms Mol. Biol., 6, 26PubMedPubMedCentralCrossRefGoogle Scholar
  127. 127.
    Eddy, S. R. (2004) How do RNA folding algorithms work? Nat. Biotechnol., 22, 1457–1458Google Scholar
  128. 128.
    Mathews, D. H. and Turner, D. H. (2006) Prediction of RNA secondary structure by free energy minimization. Curr. Opin. Struct. Biol., 16, 270–278PubMedCrossRefGoogle Scholar
  129. 129.
    Shapiro, B. A., Yingling, Y. G., Kasprzak, W. and Bindewald, E. (2007) Bridging the gap in RNA structure prediction. Curr. Opin. Struct. Biol., 17, 157–165PubMedCrossRefGoogle Scholar
  130. 130.
    Bai, Y., Dai, X., Harrison, A., Johnston, C. and Chen, M. (2016) Toward a next-generation atlas of RNA secondary structure. Brief. Bioinform., 17, 63–77PubMedCrossRefGoogle Scholar
  131. 131.
    Ge, P. and Zhang, S. (2015) Computational analysis of RNA structures with chemical probing data. Methods, 79-80, 60–66Google Scholar
  132. 132.
    Doshi, K. J., Cannone, J. J., Cobaugh, C. W. and Gutell, R. R. (2004) Evaluation of the suitability of free-energy minimization using nearest-neighbor energy parameters for RNA secondary structure prediction. BMC Bioinformatics, 5, 105PubMedPubMedCentralCrossRefGoogle Scholar
  133. 133.
    Zuker, M. (1989) On finding all suboptimal foldings of an RNA molecule. Science, 244, 48–52PubMedCrossRefGoogle Scholar
  134. 134.
    Darty, K., Denise, A. and Ponty, Y. (2009) VARNA: interactive drawing and editing of the RNA secondary structure. Bioinformatics, 25, 1974–1975PubMedPubMedCentralCrossRefGoogle Scholar
  135. 135.
    Deng, F., Ledda, M., Vaziri, S. and Aviran, S. (2016) Data-directed RNA secondary structure prediction using probabilistic modeling. RNA, 22, 1109–1119PubMedPubMedCentralCrossRefGoogle Scholar
  136. 136.
    McGinnis, J. L., Liu, Q., Lavender, C. A., Devaraj, A., McClory, S. P., Fredrick, K. andWeeks, K. M. (2015) In-cell SHAPE reveals that free 30S ribosome subunits are in the inactive state. Proc. Natl. Acad. Sci. USA, 112, 2425–2430PubMedPubMedCentralCrossRefGoogle Scholar
  137. 137.
    Mathews, D. H. (2004) Using an RNA secondary structure partition function to determine confidence in base pairs predicted by free energy minimization. RNA, 10, 1178–1190PubMedPubMedCentralCrossRefGoogle Scholar
  138. 138.
    Bernhart, S. H., Hofacker, I. L. and Stadler, P. F. (2006) Local RNA base pairing probabilities in large sequences. Bioinformatics, 22, 614–615PubMedCrossRefGoogle Scholar
  139. 139.
    Ding, Y., Chan, C. Y. and Lawrence, C. E. (2005) RNA secondary structure prediction by centroids in a Boltzmann weighted ensemble. RNA, 11, 1157–1166PubMedPubMedCentralCrossRefGoogle Scholar
  140. 140.
    Do, C. B., Woods, D. A. and Batzoglou, S. (2006) CONTRAfold: RNA secondary structure prediction without physics-based models. Bioinformatics, 22, e90–e98CrossRefGoogle Scholar
  141. 141.
    Lu, Z. J., Gloor, J. W. and Mathews, D. H. (2009) Improved RNA secondary structure prediction by maximizing expected pair accuracy. RNA, 15, 1805–1813PubMedPubMedCentralCrossRefGoogle Scholar
  142. 142.
    Hamada, M., Sato, K. and Asai, K. (2010) Prediction of RNA secondary structure by maximizing pseudo-expected accuracy. BMC Bioinformatics, 11, 586PubMedPubMedCentralCrossRefGoogle Scholar
  143. 143.
    Cordero, P. and Das, R. (2015) Rich RNA structure landscapes revealed by mutate-and-map analysis. PLoS Comput. Biol., 11, e1004473CrossRefGoogle Scholar
  144. 144.
    Breaker, R. R. (2012) Riboswitches and the RNA world. Cold Spring Harb. Perspect. Biol., 4, a003566PubMedPubMedCentralCrossRefGoogle Scholar
  145. 145.
    Parsch, J., Braverman, J. M. and Stephan, W. (2000) Comparative sequence analysis and patterns of covariation in RNA secondary structures. Genetics, 154, 909–921PubMedPubMedCentralGoogle Scholar
  146. 146.
    Gardner, P. P. and Giegerich, R. (2004) A comprehensive comparison of comparative RNA structure prediction approaches. BMC Bioinformatics, 5, 140PubMedPubMedCentralCrossRefGoogle Scholar
  147. 147.
    Cannone, J. J., Subramanian, S., Schnare, M. N., Collett, J. R., D’Souza, L. M., Du, Y., Feng, B., Lin, N., Madabusi, L. V., Müller, K. M., et al. (2002) The comparative RNA web (CRW) site: an online database of comparative sequence and structure information for ribosomal, intron, and other RNAs. BMC Bioinformatics, 3, 2PubMedPubMedCentralCrossRefGoogle Scholar
  148. 148.
    Rupert, L., Stefan, G. and Gerhard, S. (1999) ConStruct: a tool for thermodynamic controlled prediction of conserved secondary structure. Nucleic Acids Res., 27, 4208–4217CrossRefGoogle Scholar
  149. 149.
    Hofacker, I. L., Fekete, M. and Stadler, P. F. (2002) Secondary structure prediction for aligned RNA sequences. J. Mol. Biol., 319, 1059–1066PubMedCrossRefGoogle Scholar
  150. 150.
    Bernhart, S. H., Hofacker, I. L., Will, S., Gruber, A. R. and Stadler, P. F. (2008) RNAalifold: improved consensus structure prediction for RNA alignments. BMC Bioinformatics, 9, 474PubMedPubMedCentralCrossRefGoogle Scholar
  151. 151.
    Knudsen, B. and Hein, J. (2003) Pfold: RNA secondary structure prediction using stochastic context-free grammars. Nucleic Acids Res., 31, 3423–3428PubMedPubMedCentralCrossRefGoogle Scholar
  152. 152.
    Sakakibara, Y., Brown, M., Hughey, R., Mian, I. S., Sjö lander, K., Underwood, R. C. and Haussler, D. (1994) Stochastic context-free grammars for tRNA modeling. Nucleic Acids Res., 22, 5112–5120PubMedPubMedCentralCrossRefGoogle Scholar
  153. 153.
    Knudsen, B. and Hein, J. (1999) RNA secondary structure prediction using stochastic context-free grammars and evolutionary history. Bioinformatics, 15, 446–454PubMedCrossRefGoogle Scholar
  154. 154.
    Sankoff, D. (1985) Simultaneous solution of the RNA folding, alignment and protosequence problems. SIAM J. Appl. Math., 45, 810–825CrossRefGoogle Scholar
  155. 155.
    Havgaard, J. H., Lyngsø, R. B., Stormo, G. D. and Gorodkin, J. (2005) Pairwise local structural alignment of RNA sequences with sequence similarity less than 40%. Bioinformatics, 21, 1815–1824PubMedCrossRefGoogle Scholar
  156. 156.
    Mathews, D. H. and Turner, D. H. (2002) Dynalign: an algorithm for finding the secondary structure common to two RNA sequences. J. Mol. Biol., 317, 191–203PubMedCrossRefGoogle Scholar
  157. 157.
    Harmanci, A. O., Sharma, G. and Mathews, D. H. (2007) Efficient pairwise RNA structure prediction using probabilistic alignment constraints in Dynalign. BMC Bioinformatics, 8, 130PubMedPubMedCentralCrossRefGoogle Scholar
  158. 158.
    Gorodkin, J., Heyer, L. J. and Stormo, G. D. (1997) Finding the most significant common sequence and structure motifs in a set of RNA sequences. Nucleic Acids Res., 25, 3724–3732PubMedPubMedCentralCrossRefGoogle Scholar
  159. 159.
    Perriquet, O., Touzet, H. and Dauchet, M. (2003) Finding the common structure shared by two homologous RNAs. Bioinformatics, 19, 108–116PubMedCrossRefGoogle Scholar
  160. 160.
    Hofacker, I. L., Bernhart, S. H. and Stadler, P. F. (2004) Alignment of RNA base pairing probability matrices. Bioinformatics, 20, 2222–2227PubMedCrossRefGoogle Scholar
  161. 161.
    Hochsmann, M., Toller, T., Giegerich, R. and Kurtz, S. (2003) Local similarity in RNA secondary structures. In Proceedings of the IEEE Bioinformatics Conference, 2003, pages 159–168Google Scholar
  162. 162.
    Siebert, S. and Backofen, R. (2003) MARNA: a server for multiple alignment of RNAs. In Proceedings of the German Conference on Bioinformatics, pages 135–140Google Scholar
  163. 163.
    Hajdin, C. E., Bellaousov, S., Huggins,W., Leonard, C. W., Mathews, D. H. and Weeks, K. M. (2013) Accurate SHAPE-directed RNA secondary structure modeling, including pseudoknots. Proc. Natl. Acad. Sci. USA, 110, 5498–5503PubMedPubMedCentralCrossRefGoogle Scholar
  164. 164.
    Tang, Y., Bouvier, E., Kwok, C. K., Ding, Y., Nekrutenko, A., Bevilacqua, P. C. and Assmann, S. M. (2015) StructureFold: genomewide RNA secondary structure mapping and reconstruction in vivo. Bioinformatics, 31, 2668–2675PubMedCrossRefGoogle Scholar
  165. 165.
    Watts, J. M., Dang, K. K., Gorelick, R. J., Leonard, C. W., Bess, J. W. Jr, Swanstrom, R., Burch, C. L. andWeeks, K. M. (2009) Architecture and secondary structure of an entire HIV-1 RNA genome. Nature, 460, 711–716PubMedPubMedCentralCrossRefGoogle Scholar
  166. 166.
    Montaseri, S., Ganjtabesh, M. and Zare-Mirakabad, F. (2016) Evolutionary algorithm for RNA secondary structure prediction based on simulated SHAPE data. PLoS One, 11, e0166965CrossRefGoogle Scholar
  167. 167.
    Lavender, C. A., Lorenz, R., Zhang, G., Tamayo, R., Hofacker, I. L. and Weeks, K. M. (2015) Model-free RNA sequence and structure alignment informed by SHAPE probing reveals a conserved alternate secondary structure for 16S rRNA. PLoS Comput. Biol., 11, e1004126CrossRefGoogle Scholar
  168. 168.
    Novikova, I. V., Dharap, A., Hennelly, S. P. and Sanbonmatsu, K. Y. (2013) 3S: shotgun secondary structure determination of long noncoding RNAs. Methods, 63, 170–177PubMedCrossRefGoogle Scholar
  169. 169.
    Lorenz, R., Wolfinger, M. T., Tanzer, A. and Hofacker, I. L. (2016) Predicting RNA secondary structures from sequence and probing data. Methods, 103, 86–98PubMedCrossRefGoogle Scholar
  170. 170.
    Zarringhalam, K., Meyer, M. M., Dotu, I., Chuang, J. H. and Clote, P. (2012) Integrating chemical footprinting data into RNA secondary structure prediction. PLoS One, 7, e45160CrossRefGoogle Scholar
  171. 171.
    Washietl, S., Hofacker, I. L., Stadler, P. F. and Kellis, M. (2012) RNA folding with soft constraints: reconciliation of probing data and thermodynamic secondary structure prediction. Nucleic Acids Res., 40, 4261–4272PubMedPubMedCentralCrossRefGoogle Scholar
  172. 172.
    Ouyang, Z., Snyder, M. P. and Chang, H. Y. (2013) SeqFold: genomescale reconstruction of RNA secondary structure integrating highthroughput sequencing data. Genome Res., 23, 377–387PubMedPubMedCentralCrossRefGoogle Scholar
  173. 173.
    Sükösd, Z., Knudsen, B., Kjems, J. and Pedersen, C. N. (2012) PPfold 3.0: fast RNA secondary structure prediction using phylogeny and auxiliary data. Bioinformatics, 28, 2691–2692PubMedCrossRefGoogle Scholar
  174. 174.
    Sahoo, S., Switnicki, M. P. and Pedersen, J. S. (2016) ProbFold: a probabilistic method for integration of probing data in RNA secondary structure prediction. Bioinformatics, 32, 2626–2635PubMedCrossRefGoogle Scholar
  175. 175.
    Kladwang, W., VanLang, C. C., Cordero, P. and Das, R. (2011) A twodimensional mutate-and-map strategy for non-coding RNA structure. Nat. Chem., 3, 954–962PubMedPubMedCentralCrossRefGoogle Scholar
  176. 176.
    Sükösd, Z., Swenson, M. S., Kjems, J. and Heitsch, C. E. (2013) Evaluating the accuracy of SHAPE-directed RNA secondary structure predictions. Nucleic Acids Res., 41, 2807–2816PubMedPubMedCentralCrossRefGoogle Scholar
  177. 177.
    Berkowitz, N. D., Silverman, I. M., Childress, D. M., Kazan, H., Wang, L.-S. and Gregory, B. D. (2016) A comprehensive database of high-throughput sequencing-based RNA secondary structure probing data (Structure Surfer). BMC Bioinformatics, 17, 215PubMedPubMedCentralCrossRefGoogle Scholar
  178. 178.
    Wu, Y., Qu, R., Huang, Y., Shi, B., Liu, M., Li, Y. and Lu, Z. J. (2016) RNAex: an RNA secondary structure prediction server enhanced by high-throughput structure-probing data. Nucleic Acids Res., 44, W294–W301PubMedPubMedCentralCrossRefGoogle Scholar
  179. 179.
    Norris, M., Cheema, J., Kwok, C. K., Hartley, M., Morris, R. J., Aviran, S., and Ding, Y. (2016) FoldAtlas: a repository for genomewide RNA structure probing data. Bioinformatics. DOI: 10.1093/bioinformatics/btw611Google Scholar
  180. 180.
    Li, F., Zheng, Q., Vandivier, L. E., Willmann, M. R., Chen, Y. and Gregory, B. D. (2012) Regulatory impact of RNA secondary structure across the Arabidopsis transcriptome. Plant Cell, 24, 4346–4359PubMedPubMedCentralCrossRefGoogle Scholar
  181. 181.
    Mortimer, S. A., Trapnell, C., Aviran, S., Pachter, L. and Lucks, J. B. (2012) SHAPE-Seq: high-throughput RNA structure analysis. Curr Protoc Chem Biol, 4, 275–297PubMedGoogle Scholar
  182. 182.
    Incarnato, D., Neri, F., Anselmi, F. and Oliviero, S. (2015) RNA structure framework: automated transcriptome-wide reconstruction of RNA secondary structures from highthroughput structure probing data. Bioinformatics, 32, 459–461PubMedCrossRefGoogle Scholar
  183. 183.
    Goecks, J., Nekrutenko, A., Taylor, J. and The Galaxy Team. (2010) Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences. Genome Biol., 11, R86PubMedPubMedCentralCrossRefGoogle Scholar
  184. 184.
    König, J., Zarnack, K., Rot, G., Curk, T., Kayikci, M., Zupan, B., Turner, D. J., Luscombe, N. M. and Ule, J. (2010) iCLIP reveals the function of hnRNP particles in splicing at individual nucleotide resolution. Nat. Struct. Mol. Biol., 17, 909–915PubMedPubMedCentralCrossRefGoogle Scholar
  185. 185.
    Van Nostrand, E. L., Pratt, G. A., Shishkin, A. A., Gelboin-Burkhart, C., Fang, M. Y., Sundararaman, B., Blue, S. M., Nguyen, T. B., Surka, C., Elkins, K., et al. (2016) Robust transcriptome-wide discovery of RNA-binding protein binding sites with enhanced CLIP (eCLIP). Nat. Methods, 13, 508–514PubMedPubMedCentralCrossRefGoogle Scholar
  186. 186.
    Squires, J. E., Patel, H. R., Nousch, M., Sibbritt, T., Humphreys, D. T., Parker, B. J., Suter, C.M. and Preiss, T. (2012) Widespread occurrence of 5-methylcytosine in human coding and non-coding RNA. Nucleic Acids Res., 40, 5023–5033PubMedPubMedCentralCrossRefGoogle Scholar
  187. 187.
    Dominissini, D., Moshitch-Moshkovitz, S., Schwartz, S., Salmon- Divon, M., Ungar, L., Osenberg, S., Cesarkas, K., Jacob-Hirsch, J., Amariglio, N., Kupiec, M., et al. (2012) Topology of the human and mouse m6A RNA methylomes revealed by m6A-seq. Nature, 485, 201–206PubMedCrossRefGoogle Scholar
  188. 188.
    Meyer, K. D., Saletore, Y., Zumbo, P., Elemento, O., Mason, C. E. and Jaffrey, S. R. (2012) Comprehensive analysis of mRNA methylation reveals enrichment in 3’ UTRs and near stop codons. Cell, 149, 1635–1646PubMedPubMedCentralCrossRefGoogle Scholar
  189. 189.
    Edelheit, S., Schwartz, S., Mumbach, M. R., Wurtzel, O. and Sorek, R. (2013) Transcriptome-wide mapping of 5-methylcytidine RNA modifications in bacteria, archaea, and yeast reveals m5C within archaeal mRNAs. PLoS Genet., 9, e1003602CrossRefGoogle Scholar
  190. 190.
    Batista, P. J., Molinie, B., Wang, J., Qu, K., Zhang, J., Li, L., Bouley, D. M., Lujan, E., Haddad, B., Daneshvar, K., et al. (2014) m6A RNA modification controls cell fate transition in mammalian embryonic stem cells. Cell Stem Cell, 15, 707–719PubMedPubMedCentralCrossRefGoogle Scholar
  191. 191.
    T. M. Carlile, M. F. Rojas-Duran, B. Zinshteyn, H. Shin, K. M. Bartoli, and W. V. Gilbert. (2014) Pseudouridine profiling reveals regulated mRNA pseudouridylation in yeast and human cells. Nature, 515, 43–146CrossRefGoogle Scholar
  192. 192.
    Incarnato, D., Anselmi, F., Morandi, E., Neri, F., Maldotti, M., Rapelli, S., Parlato, C., Basile, G. and Oliviero, S. (2016) High-throughput single-base resolution mapping of RNA 2’-O-methylated residues. Nucleic Acids Res., doi: 10.1093/nar/gkw810Google Scholar
  193. 193.
    Kudla, G., Granneman, S., Hahn, D., Beggs, J. D. and Tollervey, D. (2011) Cross-linking, ligation, and sequencing of hybrids reveals RNA-RNA interactions in yeast. Proc. Natl. Acad. Sci. USA, 108, 10010–10015PubMedPubMedCentralCrossRefGoogle Scholar
  194. 194.
    Ramani, V., Qiu, R. and Shendure, J. (2015) High-throughput determination of RNA structure by proximity ligation. Nat. Biotechnol., 33, 980–984PubMedPubMedCentralCrossRefGoogle Scholar
  195. 195.
    Sugimoto, Y., Vigilante, A., Darbo, E., Zirra, A., Militti, C., D’Ambrogio, A., Luscombe, N. M. and Ule, J. (2015) hiCLIP reveals the in vivo atlas of mRNA secondary structures recognized by Staufen 1. Nature, 519, 491–494PubMedPubMedCentralCrossRefGoogle Scholar
  196. 196.
    Sharma, E., Sterne-Weiler, T., O’Hanlon, D. and Blencowe, B. J. (2016) Global mapping of human RNA-RNA interactions. Mol. Cell, 62, 618–626PubMedCrossRefGoogle Scholar
  197. 197.
    Lu, Z., Zhang, Q. C., Lee, B., Flynn, R. A., Smith, M. A., Robinson, J. T., Davidovich, C., Gooding, A. R., Goodrich, K. J., Mattick, J. S., et al. (2016) RNA duplex map in living cells reveals higher-order transcriptome structure. Cell, 165, 1267–1279PubMedPubMedCentralCrossRefGoogle Scholar
  198. 198.
    Aw, J. G. A., Shen, Y., Wilm, A., Sun, M., Lim, X. N., Boon, K.-L., Tapsin, S., Chan, Y.-S., Tan, C.-P., Sim, A. Y., et al. (2016) In vivo mapping of eukaryotic RNA interactomes reveals principles of higherorder organization and regulation. Mol. Cell, 62, 603–617PubMedCrossRefGoogle Scholar

Copyright information

© Higher Education Press and Springer-Verlag GmbH 2017

Authors and Affiliations

  1. 1.Department of Biomedical Engineering and Genome CenterUniversity of California at DavisDavisUSA

Personalised recommendations