Integrating High-Throughput RNA-RNA Interaction Data Into RNA Secondary Structure Prediction

In recent years, several methods for detecting RNA-RNA interactions have become available that use a combination of crosslinking, ligation, and sequencing of the resulting chimeric reads. In principle, such data also convey information on intramolecular helices. They are, however, not accurate enough to identify base pairs directly. Instead, only regions of direct contacts can be inferred. Here, we show that such data can be incorporated as pseudo-energies into RNA secondary structure prediction algorithms by assigning a bonus term to all potential pairs between crosslinked intervals. Using simulated data, we show that given sufficient coverage, such data can push the accuracy of the predicted structure to a base pair-wise MCC of above 90%. Moreover, we observe that the beneficial effect of such interval-wise pseudo-energies is quite robust w.r.t. the length of the interval and the value of the bonus term, but depends strongly on the fraction of the sequence that is covered by significant interaction data.