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<strong>EMBL</strong> Research at a Glance 2009<br />
Functional genomics and analysis of small RNA<br />
function<br />
Anton Enright<br />
PhD 2003, Cambridge<br />
University.<br />
Postdoctoral work at<br />
Memorial Sloan-Kettering<br />
Cancer Center, New York.<br />
Junior Faculty member at the<br />
Wellcome Trust Sanger<br />
Institute.<br />
Group leader at <strong>EMBL</strong>-EBI<br />
since 2008.<br />
Previous and current research<br />
Complete genome sequencing projects are generating enormous amounts of data. Although<br />
progress has been rapid, a large proportion of genes in any given genome are either un-annotated<br />
or possess a poorly characterised function. Our lab is entirely computational and our work involves<br />
the development of algorithms, protocols and datasets for functional genomics. Our goal<br />
is to predict and describe the functions of genes, proteins and in particular, regulatory RNAs and<br />
their interactions in living organisms.<br />
Decoding microRNA function and regulation: The recent discovery of widespread translational<br />
regulation by microRNAs (miRNAs) highlights the enormous diversity and complexity of gene<br />
regulation in living systems and the need for computational techniques to help understand these<br />
systems. We developed the miRanda algorithm (www.microrna.org) for miRNA target detection<br />
in collaboration with the Computational Biology Center, at Memorial Sloan-Kettering Cancer<br />
Center in New York. Recently, we have predicted large-scale miRNA–target networks for mammalian,<br />
fish and insect genomes using the miRanda algorithm and cross-species sequence analysis<br />
as part of the miRBase database. The lab will continue to develop and improve methods for<br />
computational detection of miRNA target sites to investigate other possible aspects of miRNA target<br />
specificity, including sequence and structural motifs.<br />
Analysing small RNA function: We work closely with experimental labs interested in the function of small RNAs in their system of interest<br />
by developing novel algorithms and techniques for analysis of primary data from such experiments (e.g. microarray). One example of this is<br />
the Sylamer algorithm (www.ebi.ac.uk/enright/sylamer) for associating miRNA or siRNA effects with gene expression data.<br />
Studying regulatory RNAs in model systems: Through our experimental collaborations we work on understanding the role of RNA regulation<br />
in multiple diverse biological systems. These include: zebrafish development, mouse knock-out models, neuronal development, disease<br />
and cancer models and embryonic stem cells. Typically these experiments involve identification of miRNAs through profiling techniques followed<br />
by experimental perturbation of miRNAs of interest. High-throughput techniques such as microarrays and new technology sequencing<br />
are used to determine the effect of individual miRNAs in the system of interest.<br />
Visualisation and network analysis: We retain an interest in both the analysis and clustering of biological networks (e.g. using Markov Clustering)<br />
and also in the visualisation of biological data. We continue to improve and maintain the BioL<strong>ayout</strong> software for biological network<br />
visualisation and analysis.<br />
Future projects and goals<br />
We are interested in the evolution of regulatory RNAs and in developing phylogenetic techniques appropriate for short non-coding RNA. Our<br />
long-term goal is to combine regulatory RNA target prediction, secondary effects and upstream regulation into complex regulatory networks<br />
that may help us better understand the context of RNA in complex cellular networks.<br />
Sylamer Results for the miR-430 microRNA in<br />
zebrafish. A clear signal is observed in geneexpression<br />
data for the seed-region of the miR-430<br />
miRNA in wild-type versus mutant samples. The<br />
three panels show 6, 7 and 8nt motifs respectively.<br />
Selected references<br />
Van Dongen, S., Abreu-Goodger, C. & Enright, A.J. (2008). Detection<br />
of microRNA binding and siRNA off-targets from expression data.<br />
Nature Methods, 5, 1023-1025<br />
Saini, H.K., Griffiths-Jones, S. & Enright, A.J. (2007). Genomic<br />
analysis of human microRNA transcripts. Proc. Natl. Acad. Sci.<br />
USA,10, 17719-1772<br />
66<br />
Giraldez, A.J. et al. (2006). Zebrafish MiR-30 promotes deadenylation<br />
and clearance of maternal mRNAs. Science, 312, 75-79<br />
Enright, A.J., Van Dongen, S. & Ouzounis, C.A. (2002). An efficient<br />
algorithm for large-scale detection of protein families. Nucleic Acids<br />
Res., 30, 1575-158<br />
Enright, A.J. et al. (1999). Protein interaction maps for complete<br />
genomes based on gene fusion events. Nature, 02, 86-90
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