Improving miRNA target prediction in animals

Gene expression is a complex process and can be regulated at several levels. One of the most recently discovered regulatory layers, called RNA silencing, involves microRNAs (miRNAs) a class of small noncoding RNA of around 22 nucleotides in length. MiRNAs interact with messenger RNAs (mRNAs) by binding to complementary regions called target sites. This interaction, which can be predicted by computer algorithms, typically leads to translational repression and/or mRNA degradation in animals, thus repressing gene expression post-transcriptionally. MiRNAs are important regulators playing key roles in development, and misregulation of these tiny molecules can cause many diseases such as neurodegeneration and cancer in humans.

Since the advent of next-generation sequencing thousands of miRNA sequences have been identified in plants, animals and viruses. However determining the targets (and therefore function) of these molecules is still a major challenge in the field as currently purely computational prediction methods are prone to false positive predictions.

This project aims to use a combined computational and experimental approach to better understand and improve miRNA target prediction in animals. The student will develop and apply novel bioinformatics techniques to further our knowledge of miRNA targeting and improve the accuracy of computational target prediction by integrating multiple sequencing data types (e.g. RNA-Seq & small RNA-Seq) from individual tissues under the supervision of Dr. Simon Moxon at The Genome Analysis Centre (TGAC). They will then go on to experimentally validate the predicted targets with Prof. Tamas Dalmay at the University of East Anglia.