Abstract
Discovery and characterization of functional RNA structures remains challenging due to deficiencies in de novo 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 E. coli and C. difficile and the archeon H. volcanii – 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 de novo motif and function discovery.
Highlights
RNA is a central participant in gene expression and regulation [1]
Optimization and benchmarking of current structure prediction approaches are confined to known RNA structure motifs, themselves limited to structures that are amenable to high-resolution structure characterization or comparative sequence analysis
Ribosomal RNA was used for development and evaluation of SHAPE-dependent RNA structure alignment
Summary
RNA is a central participant in gene expression and regulation [1]. for a vast majority of RNA transcripts, the positions and roles of higher-order structure are unknown. Sequence comparison approaches can be powerful tools in the discovery and annotation of functional RNA motifs. In related functional RNAs, critical structural elements are conserved despite changes in primary sequence. As RNA structure appears to be more conserved than primary sequence [2, 3], functional RNA discovery and transcriptome annotation can be improved by taking into account RNA structure. Structure-guided RNA sequence comparison approaches perform poorly and are limited by the pervasive difficulty of predicting RNA structures from sequence alone [4,5,6]. Optimization and benchmarking of current structure prediction approaches are confined to known RNA structure motifs, themselves limited to structures that are amenable to high-resolution structure characterization or comparative sequence analysis. RNA structure modeling is biased by a small number of well-characterized elements
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