Annotation of the local context of the RNA secondary structure improves the classification and prediction of A-minors.

Non-coding RNAs play a crucial role in various cellular processes in living organisms, and RNA functions heavily depend on molecule structures composed of stems, loops, and various tertiary motifs. Among those, the most frequent are A-minor interactions, which are often involved in the formation of more complex motifs such as kink-turns and pseudoknots. We present a novel classification of A-minors in terms of RNA secondary structure where each nucleotide of an A-minor is attributed to the stem or loop, and each pair of nucleotides is attributed to their relative position within the secondary structure. By analyzing classes of A-minors in known RNA structures, we found that the largest classes are mostly homogeneous and preferably localize with known A-minor co-motifs, e.g. tetraloop-tetraloop receptor and coaxial stacking. Detailed analysis of local A-minors within internal loops revealed a novel recurrent RNA tertiary motif, the across-bulged motif. Interestingly, the motif resembles the previously known GAAA/11nt motif but with the local adenines performing the role of the GAAA-tetraloop. By using machine learning, we show that particular classes of local A-minors can be predicted from sequence and secondary structure. The proposed classification is the first step toward automatic annotation of not only A-minors and their co-motifs but various types of RNA tertiary motifs as well.