Four RNA families with functional transient structures.

Protein-coding and non-coding RNA transcripts perform a wide variety of cellular functions in diverse organisms. Several of their functional roles are expressed and modulated via RNA structure. A given transcript, however, can have more than a single functional RNA structure throughout its life, a fact which has been previously overlooked. Transient RNA structures, for example, are only present during specific time intervals and cellular conditions. We here introduce four RNA families with transient RNA structures that play distinct and diverse functional roles. Moreover, we show that these transient RNA structures are structurally well-defined and evolutionarily conserved. Since Rfam annotates one structure for each family, there is either no annotation for these transient structures or no such family. Thus, our alignments either significantly update and extend the existing Rfam families or introduce a new RNA family to Rfam. For each of the four RNA families, we compile a multiple-sequence alignment based on experimentally verified transient and dominant (dominant in terms of either the thermodynamic stability and/or attention received so far) RNA secondary structures using a combination of automated search via covariance model and manual curation. The first alignment is the Trp operon leader which regulates the operon transcription in response to tryptophan abundance through alternative structures. The second alignment is the HDV ribozyme which we extend to the 5' flanking sequence. This flanking sequence is involved in the regulation of the transcript's self-cleavage activity. The third alignment is the 5' UTR of the maturation protein from Levivirus which contains a transient structure that temporarily postpones the formation of the final inhibitory structure to allow translation of maturation protein. The fourth and last alignment is the SAM riboswitch which regulates the downstream gene expression by assuming alternative structures upon binding of SAM. All transient and dominant structures are mapped to our new alignments introduced here.

Transient RNA structure features are evolutionarily conserved and can be computationally predicted.

Functional RNA structures tend to be conserved during evolution. This finding is, for example, exploited by comparative methods for RNA secondary structure prediction that currently provide the state-of-art in terms of prediction accuracy. We here provide strong evidence that homologous RNA genes not only fold into similar final RNA structures, but that their folding pathways also share common transient structural features that have been evolutionarily conserved. For this, we compile and investigate a non-redundant data set of 32 sequences with known transient and final RNA secondary structures and devise a dedicated computational analysis pipeline.

R-CHIE: a web server and R package for visualizing RNA secondary structures.

Visually examining RNA structures can greatly aid in understanding their potential functional roles and in evaluating the performance of structure prediction algorithms. As many functional roles of RNA structures can already be studied given the secondary structure of the RNA, various methods have been devised for visualizing RNA secondary structures. Most of these methods depict a given RNA secondary structure as a planar graph consisting of base-paired stems interconnected by roundish loops. In this article, we present an alternative method of depicting RNA secondary structure as arc diagrams. This is well suited for structures that are difficult or impossible to represent as planar stem-loop diagrams. Arc diagrams can intuitively display pseudo-knotted structures, as well as transient and alternative structural features. In addition, they facilitate the comparison of known and predicted RNA secondary structures. An added benefit is that structure information can be displayed in conjunction with a corresponding multiple sequence alignments, thereby highlighting structure and primary sequence conservation and variation. We have implemented the visualization algorithm as a web server R-chie as well as a corresponding R package called R4RNA, which allows users to run the software locally and across a range of common operating systems.