RNA structure from deep sequencing

from Nature Biotechnology by Eric Westhof

Solving the three-dimensional structure of an RNA molecule means laborious study by X-ray crystallography or nuclear magnetic resonance spectroscopy. However, faster complementary methods are on the horizon, many involving deep sequencing and sophisticated computational analysis. In this issue, Ramani et al.1 use deep sequencing and proximity ligation to identify nucleotide regions that interact in folded RNA molecules. The method provides an entirely new source of information on intramolecular RNA interactions that, with further improvement, may enable accurate prediction of RNA structure.

Single-stranded RNA molecules have a strong tendency to fold back on themselves, locally and globally, creating complex spatial architectures. Folding relies on stacking hydrogen bonds between nucleobases. All base-base interactions that involve at least two ‘standard’ hydrogen bonds can be classified into 12 families. Each family is a 4 × 4 matrix of the four RNA bases—U, C, A, G2. The common Watson-Crick pairs belong to one of these families, and the other 11 families are made up of non-Watson-Crick pairs.

Watson-Crick pairs form the double-stranded hairpins of RNA secondary structure. The remaining families are involved in the formation of RNA modules—the building blocks of tertiary structure—and long-range intramolecular contacts. RNA architecture can therefore be viewed as the hierarchical assembly of preformed hairpins defined by Watson-Crick base pairs and RNA modules maintained by non-Watson-Crick base pairs. Computational approaches to solving RNA structure often follow this model, determining secondary structure before building up tertiary structure.

Our knowledge of RNA structure derives from studies using X-ray crystallography and nuclear magnetic resonance spectroscopy. However, many RNAs and RNA-protein complexes are not amenable to these time-consuming methods for a variety of reasons, such as a requirement for large quantities of starting RNA or understanding of the optimal solubility and crystallization conditions that preserve molecular integrity. This has led to the development of alternative approaches for inferring RNA structure.


RPL scores were calculated for the top 25,000 interacting windows in the yeast large subunit rRNA (red) and projected onto the known secondary structure (blue). The image is adapted from Figures 1 and 2 in Ramani et al.

Phylogenetic analysis, which looks for patterns of nucleotide co-variation across conserved RNA sequences, is very useful for predicting RNA secondary structure. Homologous sequences are expected to yield similar folds and maintain the same number and lengths of core helices. However, this method requires sufficient sequence variation data and careful sequence alignments.

Another type of in silico approach relies on experimentally derived energies of base-paired stacks to compute the secondary structures of a given RNA sequence… (read more…)

Westhof E. (2015) RNA structure from deep sequencing. Nat Biotechnol 33(9):928-9. [abstract]

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