Many apparently noncoding RNA transcripts are observed for which we don't know the purpose of their existence. Identifying an RNA transcript with a conserved structure is important support for a possible structural RNA function for that transcript. This question is different from structure prediction because any RNA folds into some structure, regardless of whether that structure has any conserved biological function. We have produced a statistical test to assess when an RNA alignment presents evidence of a conserved RNA structure. The program named R-scape (RNA structural covariations above phylogenetic expectation) is computationally lightweight (which is unusual for structural RNA applications). And here is R-scape's webserver.
As more noncoding RNA transcripts are investigated, a picture emerges in which a substantial number of transcripts appear to perform roles where neither the actual RNA sequence nor any RNA structure are relevant, instead depending just on the fact that they are transcribed. Others appear to encode some small previously unidentified peptides. In this context, identifying a (possibly scarce) subset of transcripts with a conserved RNA structure becomes a special pursuit that could unveil new RNA functions in a background of other noncoding RNA transcripts.
This laboratory develops computational probabilistic models to understand RNA structure. We also work with models of sequence evolution in order to bring phylogenetic power to the question of remote homology recognition. Lately, we are also very interested in asking causal questions to experimentally determined data.
RNA Structural Covariations Above Phylogenetic Expectation.
Current version: 1.2.3 (February 2019).
A generator of RNA structure prediction models. Parameters are trained from data.