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

Sequence recombination and conservation of Varroa destructor virus-1 and deformed wing virus in field collected honey bees (Apis mellifera)

We sequenced small (s)RNAs from field collected honeybees (Apis mellifera) and bumblebees (Bombus pascuorum) using the Illumina technology. The sRNA reads were assembled and resulting contigs were used to search for virus homologues in GenBank. Matches with Varroa destructor virus-1 (VDV1) and Deformed wing virus (DWV) genomic sequences were obtained for A. mellifera but not B. pascuorum. Further analyses suggested that the prevalent virus population was composed of VDV-1 and a chimera of 5’-DWV-VDV1-DWV-3’. The recombination junctions in the chimera genomes were confirmed by using RT-PCR, cDNA cloning and Sanger sequencing. We then focused on conserved short fragments (CSF, size > 25 nt) in the virus genomes by using GenBank sequences and the deep sequencing data obtained in this study. The majority of CSF sites confirmed conservation at both between-species (GenBank sequences) and within-population (dataset of this study) levels. However, conserved nucleotide positions in the GenBank sequences might be variable at the within-population level. High mutation rates (Pi>10%) were observed at a number of sites using the deep sequencing data, suggesting that sequence conservation might not always be maintained at the population level. Virus-host interactions and strategies for developing RNAi treatments against VDV1/DWV infections are discussed

Model-Free RNA Sequence and Structure Alignment Informed by SHAPE Probing Reveals a Conserved Alternate Secondary Structure for 16S rRNA

<div><p>Discovery and characterization of functional RNA structures remains challenging due to deficiencies in <i>de novo</i> secondary structure modeling. Here we describe a dynamic programming approach for model-free sequence comparison that incorporates high-throughput chemical probing data. Based on SHAPE probing data alone, ribosomal RNAs (rRNAs) from three diverse organisms – the eubacteria <i>E</i>. <i>coli</i> and <i>C</i>. <i>difficile</i> and the archeon <i>H</i>. <i>volcanii</i> – could be aligned with accuracies comparable to alignments based on actual sequence identity. When both base sequence identity and chemical probing reactivities were considered together, accuracies improved further. Derived sequence alignments and chemical probing data from protein-free RNAs were then used as pseudo-free energy constraints to model consensus secondary structures for the 16S and 23S rRNAs. There are critical differences between these experimentally-informed models and currently accepted models, including in the functionally important neck and decoding regions of the 16S rRNA. We infer that the 16S rRNA has evolved to undergo large-scale changes in base pairing as part of ribosome function. As high-quality RNA probing data become widely available, structurally-informed sequence alignment will become broadly useful for <i>de novo</i> motif and function discovery.</p></div