Accurate classification of RNA structures using topological fingerprints

While RNAs are well known to possess complex structures, functionally similar RNAs often have little sequence similarity. While the exact size and spacing of base-paired regions vary, functionally similar RNAs have pronounced similarity in the arrangement, or topology, of base-paired stems. Furthermore, predicted RNA structures often lack pseudoknots (a crucial aspect of biological activity), and are only partially correct, or incomplete. A topological approach addresses all of these difficulties. In this work we describe each RNA structure as a graph that can be converted to a topological spectrum (RNA fingerprint). The set of subgraphs in an RNA structure, its RNA fingerprint, can be compared with the fingerprints of other RNA structures to identify and correctly classify functionally related RNAs. Topologically similar RNAs can be identified even when a large fraction, up to 30%, of the stems are omitted, indicating that highly accurate structures are not necessary. We investigate the performance of the RNA fingerprint approach on a set of eight highly curated RNA families, with diverse sizes and functions, containing pseudoknots, and with little sequence similarity–an especially difficult test set. In spite of the difficult test set, the RNA fingerprint approach is very successful (ROC AUC \u3e 0.95). Due to the inclusion of pseudoknots, the RNA fingerprint approach both covers a wider range of possible structures than methods based only on secondary structure, and its tolerance for incomplete structures suggests that it can be applied even to predicted structures. Source code is freely available at https://github.rcac.purdue.edu/mgribsko/XIOS_RNA_fingerprint

Graphical methods in RNA structure matching

Eukaryotic genomes are pervasively transcribed; almost every base can be found in an RNA transcript. This is a surprising observation since most of the genome does not encode proteins. This RNA must serve an important regulatory function – important because producing non-coding RNA is an energy intensive process, and in the absence of strong selection one would expect it to disappear. RNA families with common functions have specifically conserved structural motifs, which are directly related to the functional roles of RNA in catalysis and regulation. Because the conserved structures depend on base-pairing, similar RNA structures may have little or no detectable sequence similarity, making the identification of conserved RNAs difficult. This is a particularly serious problem when studying regulatory structures in RNA. In many cases, such as that of cellular internal ribosome entry sites, although we can identify RNAs that have similar regulatory responses, it is difficult to tell whether the RNAs have common structural features using current methods. Available tools for identifying common structures based on RNA sequence suffer from one or more of the following problems: they do not consider pseudoknots, which are important in many catalytic and regulatory structures; they do not consider near minimum free energy structures, which is important as many RNAs exist as an ensemble of structures of nearly equal energy; they require many examples of known structures in order to train a computational model; they require impractical amounts of computational time, precluding their use on long sequences or genomic scale; or they use a similarity function that cannot identify RNAs as having similar structure, even when they are from one of the well characterized known classes. The approach presented here has the potential to address all of these issues, allowing novel RNA structures that are shared between RNAs with little or no sequence similarity to be discovered. This provides a powerful tool to investigate and explain the pervasive transcription observed in eukaryotic genomes