It is rapidly becoming clear that many genes do not code for proteins, but rather RNAs. Indeed, given recent biochemical work describing large numbers of completely novel RNAs, including two families of snoRNAs, tmRNA, micro (mi)RNAs, small interfering (si)RNAs, and RNA-dependent editing mechanisms, it is likely that there are many more RNAs carrying out a broad range of functions in the cell than was previously thought. Thus a comprehensive understanding of the biology of a cell will ultimately require a knowledge of the identity of all encoded RNAs, the molecules with which they interact, and the molecular structures of these complexes.

For these reasons, the computational biology of RNA is playing an increasingly important role within functional genomics. Here at UEA we develop mathematical and computational tools for the identification of RNA genes and structural elements within genomes, the prediction of RNA structure using evolutionary and physical principles, and the analysis of RNA structure and its application to topical problems in molecular and cell biology.

An important feature of RNA evolution is that structure tends to be conserved rather than primary sequence, and indeed, we have shown in studies of RNase P and RNase MRP that secondary structure can contain phylogenetic information. However, a major difficulty is that, compared with proteins, there are fewer signals for RNAs in genome sequences; RNA genes are not specified by open reading frames as they are not translated, and, moreover, sequence conservation between members of the same family are too low for standard genome search strategies such as BLAST to detect. Therefore, the search for RNA genes and structures presents special computational problems since it is essential to take into account secondary (and ultimately tertiary) structure. We have devised methods to include such structural information into the search for H/ACA-sno RNAs, that have been incorporated into succesful computational tools.

To derive relevant biological information from RNA structures it is necessary to have useful analytical tools. We have developed metrics for quantifying the differences between suboptimal structures. These metrics can be computed efficiently and we showed that they could be used to predict conformal switching for RNA structure. We have also incorporated phylogenetic tools into secondary structure analysis to study the evolution of various RNAs. We continue to develop tools for analysing RNA structure. For example, using new RNA data bases, we are investigating structural properties of the space of foldings for a single RNA sequence.

Non-coding RNA biology has received growing attention in recent years due to the discovery of short (s)RNAs such as short-interfering (si) and micro (mi)RNAs in plants, animals and fungi. These classes of short (20-30nt) non-coding RNAs are involved in a broad spectrum of biological pathways including regulation of gene expression, genome maintenance and defence against pathogens. Originally, individual sRNAs were identified by traditional cloning and Sanger sequencing but recent developments in high-throughput short-read sequencing technologies now make it possible to obtain hundreds of thousands of sRNA sequences in a single experiment.

We collaborate with the laboratories of Dr Tamas Dalmay (UEA) and Prof David Baulcombe (University of Cambridge) to develop tools for the analysis of high-throughput sRNA sequencing data from plant and animal species. Main goals of our current research include: identification of novel miRNAs, development of databases for high-throughput sRNA sequencing experiments, identification of miRNA targets from whole-genome gene expression data and comparison of sRNA expression profiles in different samples.

Since lack of access to bioinformatics support is a major bottleneck for many laboratories working on sRNA biology, we are committed to making our tools available to the research community in an easy to use form. We have released a web-server with tools for the analysis of large scale plant sRNA datasets and the stand-alone perl implentation is available. Our database of sRNA-produing genomic loci and novel miRNAs in the single-celled green alga Chlamydomonas reinhardtii is also available online (http://cresirna.cmp.uea.ac.uk). 

Key Publications

1. miRNAs control gene expression in the single-cell alga Chlamydomonas reinhardtii, A. Molnár, F. Schwach,D. Studholme,E. Thuenemann,D. Baulcombe, Nature, 447(7148),2007, 1126-9
2. PolIVb influences RNA-directed DNA methylation independently of its role in siRNA biogenesis, R. Mosher,F. Schwach, D. Studholme,D. Baulcombe, Proc Natl Acad Sci U S A, 105(8), 2008, 3145-50
3. Identification of novel short RNAs in tomato (Solanum lycopersicum), R.Rusholme, S.Moxon, N.Pakseresht, V.Moulton, K.Mannington, G.Seymour, T.Dalmay, Planta, 3, 2007, 709-717.
4. Boltzmann probability of RNA structural neighbors and riboswitch detection, E.Freyhult, V.Moulton, P.Clote, Bioinformatics, 23, 2007, 2054-2062.
5. A search for noncoding RNAs using predicted MFE secondary structures, S.Edvardsson, P.P. Gardner, A.M.Poole, M.D.Hendy, D.Penny, V.Moulton, Bioinformatics, 19, 2003, 865-873.
6. Metrics on RNA secondary structures, V.Moulton, M.Zuker, M.Steel, R.Pointon, D.Penny, Journal of Computational Biology, 7, 2000, 277-292.
7. Use of RNA secondary structure for studying the evolution of RNase P and RNase MRP, L.Collins, V.Moulton, D.Penny, Journal of Molecular Evolution, 51, 2000, 194-204.
8. Funding for the sRNA work is provided by the Biotechnology and Biological Sciences Research Council (grant BB/E004091/1).
   

 

Research Team: Prof. Vincent Moulton, Dr. Martin Lott, Dr. Daniel Mapleson, Dr. Matt Stocks