Evaluating DCA-based method performances for RNA contact prediction by a well-curated dataset

RNA molecules play many pivotal roles in the cellular functioning that are still not fully understood. Any detailed understanding of RNA function requires knowledge of its three-dimensional structure, yet experimental RNA structure resolution remains demanding. Recent advances in sequencing provide unprecedented amounts of sequence data that can be statistically analysed by methods such as Direct Coupling Analysis (DCA) to determine spatial proximity or contacts of specific nucleic acid pairs, which improve the quality of structure prediction. To quantify this structure prediction improvement, we here present a well curated dataset of about seventy RNA structures with high resolution and compare different nucleotide-nucleotide contact prediction methods available in the literature. We observe only minor difference between the performances of the different methods. Moreover, we discuss how these predictions are robust for different contact definitions and how strongly depend on procedures used to curate and align the families of homologous RNA sequences.