RNA Informatics Research Lab at UGA is focused on developing computational tools and online servers for the prediction and annotation of non-coding RNA (ncRNA) genes and the prediction of protein tertiary structures. We are particularly interested in statistical models for profiling and searching for RNA secondary and tertiary structures in genomes. Our work also includes developing novel methods for multiple structural alignment and building repositories of pseudoknot structures and structural alignments including pseudoknots for a large spectrum of ncRNA families.
ncRNAs are RNAs that are not translated into proteins, which have been shown to be involved in many biological processes including gene regulation, chromosome replication, and RNA modification. The number of ncRNAs found has grown fast. It is a topic of great interest in bioinformatics to predict ncRNA genes using computational methods. Unlike protein coding genes, ncRNAs have not been found to carry strong statistical signals except the structures into which their products can fold. Therefore, using RNA structure as an indicator is becoming the standard approach to ncRNA prediction. In partcular, methods that can accurately model and efficiently search for RNA structures are highly desirable and are research objectives in the RNA-Informatics Group as well.
Large, complex RNA structures, however, pose difficult issues in bioinformatics solutions to ncRNA (structure and) gene prediction. The potential crossing pattern of base pairings formed by pseudoknots in RNA molecules makes it impossible to model their structures with the Covariance model (CM) that is ideal for pseudoknot-free structures. Other proposed models for RNA pseudoknots usually lead to unrealistic resource (i.e., computer time and memory) requirements for computing structural alignment and structure search. The research in the RNA-Informatics Group has addressed these issues in RNA pseudoknots through graph-theoretic modeling of RNA structure as a conformational graph. We have developed very efficient structure-sequence alignment algorithm based on tree decomposition of the conformational graph. The structure model and the alignment algorithm are now being applied to our various research projects in the prediction of ncRNA genes, RNA multiple structural alignment tools, and RNA pseudoknot database development.The RNA-Informatics Research Lab is co-directed by Professors Liming Cai of Computer Science Department and Russell Malmberg of Plant Biology Department and sponsored by NSF and NIH.