  Many non-coding RNAs are known to play a role in the cell directly linked to
their structure. Structure prediction based on the sole sequence is however a
challenging task. On the other hand, thanks to the low cost of sequencing
technologies, a very large number of homologous sequences are becoming
available for many RNA families. In the protein community, it has emerged in
the last decade the idea of exploiting the covariance of mutations within a
family to predict the protein structure using the direct-coupling-analysis
(DCA) method. The application of DCA to RNA systems has been limited so far. We
here perform an assessment of the DCA method on 17 riboswitch families,
comparing it with the commonly used mutual information analysis and with
state-of-the-art R-scape covariance method. We also compare different flavors
of DCA, including mean-field, pseudo-likelihood, and a proposed stochastic
procedure (Boltzmann learning) for solving exactly the DCA inverse problem.
Boltzmann learning outperforms the other methods in predicting contacts
observed in high resolution crystal structures.
