Prediction and differential analysis of RNA secondary structure

Abstract

Background: RNA structure is the crucial basis for RNA function in various cellular processes. Over the last decade, high throughput structure profiling (SP) experiments have brought enormous insight into RNA secondary structure.

Results: In this review, we first provide an overview of approaches for RNA secondary structure prediction, including free energy-based algorithms and comparative sequence analysis. Then we introduce SP technologies, databases to document SP data, and pipelines/algorithms to normalize and interpret SP data. Computational frameworks that incorporate SP data in RNA secondary structure prediction are also presented.

Conclusions: We finally discuss potential directions for improvement in the prediction and differential analysis of RNA secondary structure.

Author summary

High throughput structure profiling (SP) experiments help the analysis of RNA secondary structure. In this review, we discuss existing frameworks for the prediction and differential analysis of RNA secondary structure, including computational methods and especially approaches incorporating SP data.

Keywords

RNA secondary structure / prediction / differential analysis / structure profiling

References

[1] Luco, R. F. and Misteli, T. (2011) 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

[2] Licatalosi, D. D. and Darnell, R. B. (2010) RNA processing and its regulation: global insights into biological networks. Nat. Rev. Genet., 11, 75–87

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

[4] Cech, T. R. (2012) The RNA worlds in context. Cold Spring Harb. Perspect. Biol., 4, a006742

[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, 182–191

[6] Halvorsen, M., Martin, J. S., Broadaway, S. and Laederach, A. (2010) Disease-associated mutations that alter the RNA structural ensemble. PLoS Genet., 6, e1001074

[7] Piao, M., Sun, L. and Zhang, Q. C. (2017) RNA regulations and functions decoded by transcriptome-wide RNA structure probing. Genom. Proteom. Bioinf., 15, 267–278

[8] Schroeder, R., Barta, A. and Semrad, K. (2004) Strategies for RNA folding and assembly. Nat. Rev. Mol. Cell Biol., 5, 908–919

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

[10] Sun, L., Fazal, F. M., Li, P., Broughton, J. P., Lee, B., Tang, L., Huang, W., Kool, E. T., Chang, H. Y. and Zhang, Q. C. (2019) RNA structure maps across mammalian cellular compartments. Nat. Struct. Mol. Biol., 26, 322–330

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

[12] Wan, Y., Qu, K., Zhang, Q. C., Flynn, R. A., Manor, O., Ouyang, Z., Zhang, J., Spitale, R. C., Snyder, M. P., Segal, E., (2014) Landscape and variation of RNA secondary structure across the human transcriptome. Nature, 505, 706–709

[13] Choudhary, K., Lai, Y. H., Tran, E. J. and Aviran, S. (2019) dStruct: identifying differentially reactive regions from RNA structurome profiling data. Genome Biol., 20, 40

[14] 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., (2015) Structural imprints in vivo decode RNA regulatory mechanisms. Nature, 519, 486–490

[15] Choudhary, K., Deng, F. and Aviran, S. (2017) Comparative and integrative analysis of RNA structural profiling data: current practices and emerging questions. Quant. Biol., 5, 3–24

[16] Yan, K., Arfat, Y., Li, D., Zhao, F., Chen, Z., Yin, C., Sun, Y., Hu, L., Yang, T. and Qian, A. (2016) Structure prediction: new insights into decrypting long noncoding RNAs. Int. J. Mol. Sci., 17, 132

[17] Lorenz, R., Wolfinger, M. T., Tanzer, A. and Hofacker, I. L. (2016) Predicting RNA secondary structures from sequence and probing data. Methods, 103, 86–98

[18] McCaskill, J. S. (1990) The equilibrium partition function and base pair binding probabilities for RNA secondary structure. Biopolymers, 29, 1105–1119

[19] Zuker, M. (2003) Mfold web server for nucleic acid folding and hybridization prediction. Nucleic Acids Res., 31, 3406–3415

[20] Zuker, M. and Stiegler, P. (1981) Optimal computer folding of large RNA sequences using thermodynamics and auxiliary information. Nucleic Acids Res., 9, 133–148

[21] Carvalho, L. E. and Lawrence, C. E. (2008) Centroid estimation in discrete high-dimensional spaces with applications in biology. Proc. Natl. Acad. Sci. USA, 105, 3209–3214

[22] Zuker, M. (1989) On finding all suboptimal foldings of an RNA molecule. Science, 244, 48–52

[23] Wuchty, S., Fontana, W., Hofacker, I. L. and Schuster, P. (1999) Complete suboptimal folding of RNA and the stability of secondary structures. Biopolymers, 49, 145–165

[24] Ding, Y., Chan, C. Y. and Lawrence, C. E. (2005) RNA secondary structure prediction by centroids in a Boltzmann weighted ensemble. RNA, 11, 1157–1166

[25] Ding, Y. and Lawrence, C. E. (2003) A statistical sampling algorithm for RNA secondary structure prediction. Nucleic Acids Res., 31, 7280–7301

[26] Ding, Y., Chan, C. Y. and Lawrence, C. E. (2006) Clustering of RNA secondary structures with application to messenger RNAs. J. Mol. Biol., 359, 554–571

[27] Do, C. B., Woods, D. A. and Batzoglou, S. (2006) CONTRAfold: RNA secondary structure prediction without physics-based models. Bioinformatics, 22, e90–e98

[28] Hamada, M., Sato, K. and Asai, K. (2010) Prediction of RNA secondary structure by maximizing pseudo-expected accuracy. BMC Bioinformatics, 11, 586

[29] Puton, T., Kozlowski, L. P., Rother, K. M. and Bujnicki, J. M. (2013) CompaRNA: a server for continuous benchmarking of automated methods for RNA secondary structure prediction. Nucleic Acids Res., 41, 4307–4323

[30] Hamada, M. (2015) RNA Secondary Structure Prediction from Multi-Aligned Sequences. In: RNA Bioinformatics, Picardi, E., (ed.), pp. 17–38. Totowa: Humana Press Inc.

[31] Mathews, D. H. and Turner, D. H. (2002) Dynalign: an algorithm for finding the secondary structure common to two RNA sequences. Tinoco. J. Mol. Biol., 317, 191–203

[32] Knudsen, B. and Hein, J. (2003) Pfold: RNA secondary structure prediction using stochastic context-free grammars. Nucleic Acids Res., 31, 3423–3428

[33] Hofacker, I. L. (2003) Vienna RNA secondary structure server. Nucleic Acids Res., 31, 3429–3431

[34] Berman, H. M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T. N., Weissig, H., Shindyalov, I. N. and Bourne, P. E. (2000) The Protein Data Bank. Nucleic Acids Res., 28, 235–242

[35] Andronescu, M., Bereg, V., Hoos, H. H. and Condon, A. (2008) RNA STRAND: the RNA secondary structure and statistical analysis database. BMC Bioinformatics, 9, 340

[36] Hajiaghayi, M., Condon, A. and Hoos, H. H. (2012) Analysis of energy-based algorithms for RNA secondary structure prediction. BMC Bioinformatics, 13, 22

[37] Xu, Z., Almudevar, A. and Mathews, D. H. (2012) Statistical evaluation of improvement in RNA secondary structure prediction. Nucleic Acids Res., 40, e26

[38] Mathews, D. H. (2019) How to benchmark RNA secondary structure prediction accuracy. Methods, 162–163, 60–67

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

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

[41] Strobel, E. J., Yu, A. M. and Lucks, J. B. (2018) High-throughput determination of RNA structures. Nat. Rev. Genet., 19, 615–634

[42] 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, 169–181

[43] Zheng, Q., Ryvkin, P., Li, F., Dragomir, I., Valladares, O., Yang, J., Cao, K., Wang, L. S. and Gregory, B. D. (2010) Genome-wide double-stranded RNA sequencing reveals the functional significance of base-paired RNAs in Arabidopsis. PLoS Genet., 6, e1001141

[44] 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, 995–1001.

[45] 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, 103–107

[46] Smola, M. J. and Weeks, K. M. (2018) In-cell RNA structure probing with SHAPE-MaP. Nat. Protoc., 13, 1181–1195

[47] Saus, E., Willis, J. R., Pryszcz, L. P., Hafez, A., Llorens, C., Himmelbauer, H. and Gabald�n, T. (2018) nextPARS: parallel probing of RNA structures in Illumina. RNA, 24, 609–619

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

[49] 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, 75–82

[50] Ritchey, L. E., Su, Z., Tang, Y., Tack, D. C., Assmann, S. M. and Bevilacqua, P. C. (2017) Structure-seq2: sensitive and accurate genome-wide profiling of RNA structure in vivo. Nucleic Acids Res., 45, e135

[51] Incarnato, D., Anselmi, F., Morandi, E., Neri, F., Maldotti, M., Rapelli, S., Parlato, C., Basile, G. and Oliviero, S. (2017) High-throughput single-base resolution mapping of RNA 2�-O-methylated residues. Nucleic Acids Res., 45, 1433–1441

[52] Chan, D., Feng, C. and Spitale, R. C. (2017) Measuring RNA structure transcriptome-wide with icSHAPE. Methods, 120, 85–90

[53] Flynn, R. A., Zhang, Q. C., Spitale, R. C., Lee, B., Mumbach, M. R. and Chang, H. Y. (2016) Transcriptome-wide interrogation of RNA secondary structure in living cells with icSHAPE. Nat. Protoc., 11, 273–290

[54] 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, 1643–1669

[55] 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, 1042–1052

[56] Ding, Y., Kwok, C. K., Tang, Y., Bevilacqua, P. C. and Assmann, S. M. (2015) Genome-wide profiling of in vivo RNA structure at single-nucleotide resolution using structure-seq. Nat. Protoc., 10, 1050–1066

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

[58] 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, 959–965

[59] 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 in Molecular Biology (Methods and Protocols), Waldsich, C. (ed.), vol 1086, pp. 95–117. Totowa: Humana Press

[60] 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, 701–705

[61] Loughrey, D., Watters, K. E., Settle, A. H. and Lucks, J. B. (2014) 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

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

[63] Homan, P. J., Favorov, O. V., Lavender, C. A., Kursun, O., Ge, X., Busan, S., Dokholyan, N. V. and Weeks, K. M. (2014) Single-molecule correlated chemical probing of RNA. Proc. Natl. Acad. Sci. USA, 111, 13858–13863

[64] 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, 12138–12154

[65] Ding, Y., Tang, Y., Kwok, C. K., Zhang, Y., Bevilacqua, P. C. and Assmann, S. M. (2014) In vivo genome-wide profiling of RNA secondary structure reveals novel regulatory features. Nature, 505, 696–700

[66] 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, 275–297

[67] 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, 11063–11068

[68] Silverman, I.M., Berkowitz, N. D., Gosai, S. J. and Gregory, B. D. (2016) Genome-Wide Approaches for RNA Structure Probing. In: RNA Processing. Advances in Experimental Medicine and Biology, Yeo, G. (eds.), vol 907, pp. 29–59. Cham: Springer

[69] Bevilacqua, P. C., Ritchey, L. E., Su, Z., and Assmann, S. M. (2016) Genome-wide analysis of RNA secondary structure. Annu. Rev. Genet., 50, 235–266

[70] Kubota, M., Tran, C. and Spitale, R. C. (2015) Progress and challenges for chemical probing of RNA structure inside living cells. Nat. Chem. Biol., 11, 933–941

[71] 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, 221–232

[72] Yesselman, J. D., Tian, S., Liu, X., Shi, L., Li, J. B. and Das, R. (2018) Updates to the RNA mapping database (RMDB), version 2. Nucleic Acids Res., 46, D375–D379

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

[74] Rocca-Serra, P., Bellaousov, S., Birmingham, A., Chen, C., Cordero, P., Das, R., Davis-Neulander, L., Duncan, C. D., Halvorsen, M., Knight, R., (2011) Sharing and archiving nucleic acid structure mapping data. RNA, 17, 1204–1212

[75] 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, 215

[76] Norris, M., Kwok, C. K., Cheema, J., Hartley, M., Morris, R. J., Aviran, S. and Ding, Y. (2017) FoldAtlas: a repository for genome-wide RNA structure probing data. Bioinformatics, 33, 306–308

[77] 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–W301

[78] Kladwang, W., Mann, T. H., Becka, A., Tian, S., Kim, H., Yoon, S. and Das, R. (2014) Standardization of RNA chemical mapping experiments. Biochemistry, 53, 3063–3065

[79] Selega, A., Sirocchi, C., Iosub, I., Granneman, S. and Sanguinetti, G. (2017) Robust statistical modeling improves sensitivity of high-throughput RNA structure probing experiments. Nat. Methods, 14, 83–89

[80] Low, J. T. and Weeks, K. M. (2010) SHAPE-directed RNA secondary structure prediction. Methods, 52, 150–158

[81] Tang, Y., Bouvier, E., Kwok, C. K., Ding, Y., Nekrutenko, A., Bevilacqua, P. C. and Assmann, S. M. (2015) StructureFold: genome-wide RNA secondary structure mapping and reconstruction in vivo. Bioinformatics, 31, 2668–2675

[82] 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, 11069–11074

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

[84] Zou, C. and Ouyang, Z. (2015) Joint modeling of RNase footprint sequencing profiles for genome-wide inference of RNA structure. Nucleic Acids Res., 43, 9187–9197

[85] 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, 97–102

[86] Mathews, D. H., Disney, M. D., Childs, J. L., Schroeder, S. J., Zuker, M. and Turner, D. H. (2004) Incorporating chemical modification constraints into a dynamic programming algorithm for prediction of RNA secondary structure. Proc. Natl. Acad. Sci. USA, 101, 7287–7292

[87] Qi, L., Lucks, J. B., Liu, C. C., Mutalik, V. K. and Arkin, A. P. (2012) Engineering naturally occurring trans-acting non-coding RNAs to sense molecular signals. Nucleic Acids Res., 40, 5775–5786

[88] Deng, F., Ledda, M., Vaziri, S. and Aviran, S. (2016) Data-directed RNA secondary structure prediction using probabilistic modeling. RNA, 22, 1109–1119

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

[90] 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, 7247–7259

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

[92] Li, H. and Aviran, S. (2018) Statistical modeling of RNA structure profiling experiments enables parsimonious reconstruction of structure landscapes. Nat. Commun., 9, 606

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

[94] 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., (2016) RNA duplex map in living cells reveals higher-order transcriptome structure. Cell, 165, 1267–1279

[95] 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., (2016) In vivo mapping of eukaryotic RNA interactomes reveals principles of higher-order organization and regulation. Mol. Cell, 62, 603–617

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

ACKNOWLEDGEMENTS

H. L. acknowledge the following fundings: the National Natural Science Foundation of China (No. 11601259) and Shanghai Municipal Science and Technology Major Project (No. 2017SHZDZX01).

COMPLIANCE WITH ETHICS GUIDELINES

The authors Bo Yu, Yao Lu, Qiangfeng Cliff Zhang and Lin Hou declare that they have no conflict of interests.

This article is a review article and does not contain any studies with human or animal subjects performed by any of the authors.

RIGHTS & PERMISSIONS

2020 Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature