Structure probing data enhances RNA-RNA interaction prediction

Abstract Experimental structure probing data has been shown to improve thermodynamics-based RNA secondary structure prediction. To this end, chemical reactivity information (as provided by SHAPE) is incorporated, which encodes whether or not individual nucleotides are involved in intra-molecular structure. Since inter-molecular RNA-RNA interactions are often confined to unpaired RNA regions, SHAPE data is even more promising to improve interaction prediction. Here we show how such experimental data can be incorporated seamlessly into accessibility-based RNA-RNA interaction prediction approaches, as implemented in IntaRNA. This is possible via the computation and use of unpaired probabilities that incorporate the structure probing information. We show that experimental SHAPE data can significantly improve RNA-RNA interaction prediction. We evaluate our approach by investigating interactions of the spliceosomal U1 snRNA with its target splice sites. When SHAPE data is incorporated, known target sites are predicted with increased precision and specificity. Availabilityhttps://github.com/BackofenLab/IntaRNA Contactmmann@informatik.uni-freiburg.de,backofen@informatik.uni-freiburg.de