A simple protocol for the inference of RNA global pairwise alignments

RNA alignment is an important step in the annotation and characterization of unknown RNAs, and several methods have been developed to meet the need of fast and accurate alignments. Being the performances of the aligning methods affected by the input RNA features, finding the most suitable method is not trivial. Indeed, no available method clearly outperforms the others. Here we present a simple workflow to help choosing the more suitable method for RNA pairwise alignment. We tested the performances of six algorithms, based on different approaches, on datasets created by merging publicly available datasets of known or curated RNA secondary structure annotations with datasets of curated RNA alignments. Then, we simulated the frequent case where the secondary structure is unknown by using the same alignment datasets but ignoring the known structure and instead predicting it. In conclusion, the proposed workflow for pairwise RNA alignment depends on the input RNA primary sequence identity and the availability of reliable secondary structures.