Understanding the transcriptome through RNA structure

Key Points

  • In addition to carrying information in their linear sequences of nucleotides (primary structure), RNA molecules fold into intricate shapes. Pairing of local nucleotides can create secondary structures such as hairpins and stem–loops, and interactions among distantly located sequences can create tertiary structures.

  • RNA structures are involved in a wide range of cellular processes, including transcriptional and post-transcriptional regulation and sensing metabolites.

  • Structures can occur in coding or non-coding RNAs, and learning more about RNA structure will improve our understanding of the transcriptome.

  • Computational predictions of RNA structures have been important and continue to be refined and also combined with experimental methods.

  • Experimental methods, which can involve enzymes or chemicals, to differentiate single- or double-stranded RNAs are now being scaled-up through coupling to next-generation sequencing.

Abstract

RNA structure is crucial for gene regulation and function. In the past, transcriptomes have largely been parsed by primary sequences and expression levels, but it is now becoming feasible to annotate and compare transcriptomes based on RNA structure. In addition to computational prediction methods, the recent advent of experimental techniques to probe RNA structure by high-throughput sequencing has enabled genome-wide measurements of RNA structure and has provided the first picture of the structural organization of a eukaryotic transcriptome — the 'RNA structurome'. With additional advances in method refinement and interpretation, structural views of the transcriptome should help to identify and validate regulatory RNA motifs that are involved in diverse cellular processes and thereby increase understanding of RNA function.

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Figure 1: Diversity and dynamics of RNA structures.
Figure 2: Predicting structural motifs for RNA-binding-protein targets in mRNAs from different organisms.
Figure 3: Structure probing by RNA footprinting followed by gel or capillary electrophoresis.
Figure 4: PARS and Frag-seq methods.
Figure 5: Structural organization of the mRNA transcriptome.

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Acknowledgements

We gratefully acknowledge the support of the US National Institutes of Health (NIH) (R01-HG004361), the Agency of Science, Technology and Research of Singapore (Y.W.) and the A.P. Giannini Foundation (R.C.S.). E.S. is the incumbent of the Soretta and Henry Shapiro career development chair. H.Y.C. is an Early Career Scientist of the Howard Hughes Medical Institute.

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Correspondence to Howard Y. Chang.

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Competing interests

Yue Wan, Michael Kertesz, Eran Segal and Howard Y. Chang are holders of a patent for parallel analysis of RNA structure (PARS). Robert C. Spitale declares no competing financial interests.

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FURTHER INFORMATION

Eran Segal's homepage

Howard Chang's homepage

CAFA and SNPfold

iPARS application for iPhone and iPad

SAFA

Glossary

Sequence covariation

Nucleotide substitutions that differ between two or more homologous genes but retain the potential for Watson–Crick base pairing in an RNA molecule in each sequence, thus suggesting a selective pressure to retain those base pairings.

SELEX

(Systematic evolution of ligands by exponential enrichment). In the context of RNA, this is a method for identifying consensus protein binding sequences on RNA substrates by in vitro selection of short RNAs that bind preferentially to RNA-binding proteins.

RNAcompete

An in vitro method to identify the structural and linear sequence motifs of RNAs that interact strongly with RNA-binding proteins in a complex pool of k-mer RNAs.

PUF

(Pumilio family). This is a family of evolutionarily conserved RNA-binding proteins. They preferentially bind to the 3′UTR of mRNAs to regulate gene expression.

Small nucleolar RNAs

(snoRNAs). RNAs that are involved in guiding the modification of other RNAs, such as ribosomal RNAs, tRNAs and small nuclear RNAs.

Dynamic programming

A method for solving complex problems by breaking them down into simpler 'sub-problems'. This method is used by most RNA structure-prediction algorithms to efficiently scan the entire landscape of possible secondary structures.

Stochastic context-free grammars

(SCFGs). Mathematical models in which base pairings in an RNA molecule are described as a set of production rules, each augmented with a probability.

Machine learning methods

Algorithms that use empirical data (called the training set) to capture characteristics of unknown underlying phenomena and improve predictions about new data (called the test set).

Pseudoknots

RNA topologies that contain non-nested nucleotide pairings.

Heuristics

An experience-based method of problem solving that is used in cases in which an exhaustive search is impractical to speed up the process of finding a solution. There is usually some loss of accuracy.

Single-hit kinetics

The kinetics of reactions involving chemical and enzymatic probes that react with RNA, such that, on average, there is only one cut per molecule.

Nucleocapsid

A coat of proteins that surrounds the genomic content of a virus.

Gag-Pol

The Gag polyprotein is processed into several proteins including the matrix, capsid, spacer peptides, p6 and nucleocapsid proteins. Pol includes reverse transcriptase, integrase and protease.

Env

(Envelope protein). This is found on the surface of the retroviruses and contains glycoproteins that enable the virus to recognize and enter host cells.

Ash1 localization elements

Sequences that are required to properly localize Ash1 mRNAs to the yeast bud tip.

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Wan, Y., Kertesz, M., Spitale, R. et al. Understanding the transcriptome through RNA structure. Nat Rev Genet 12, 641–655 (2011). https://doi.org/10.1038/nrg3049

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