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.
RNA Structural Covariations Above Phylogenetic Expectation/Cascade variation/covariation Constrained Folding algorithm
Current version (now working on Apple Silicon):
An automated pipeline to search and align RNA homologs with secondary structure assessment
Design and test different RNA 2D structure architectures. Any modality: SCFGs, CRFs, or thermodynamically-determined parameters.
Current version:
Learning the rules of RNA base pairing doesn't require extensive knowledge or complex models. Here we demonstrate that simple probabilistic model with a small number of parameters can effectively learn the Watson-Crick-Franklin base pairing rules (A:U, G:C, and G:U) from RNA sequences alone. This learning process doesn't even need to be aware of secondary structures or alignments.
Git repository:
Supplemental materials: