Novel methods and tools for genome-wide characterisation of enhancer elements

MOXON_U22DTP

The correct spatiotemporal regulation of genetic programmes is critical for the development and function of complex organisms. The majority of vertebrate genome sequence is non-coding and includes many cis-regulatory elements (CREs) such as enhancers that control gene activity in a cell and tissue specific manner. The ability to accurately predict enhancer elements will provide a better understanding of the genetic control of cellular regulation as well as give insights into disease mechanisms.

This bioinformatics-based project will use next-generation sequencing data from gene expression and chromatin accessibility assays to develop a new computational tool to predict enhancer sequences across vertebrate genomes. The student will apply their method to new datasets generated at UEA in the Münsterberg and Wheeler labs and novel enhancer predictions will be experimentally validated.

The student will work closely with computational biologists and experimentalists in a collaborative, interdisciplinary environment. The project will be computational but there would be an opportunity to conduct some experimental validation work in the Münsterberg lab if the student wishes to do so.

This project would be suitable for graduates with a biology background who would like to specialise in bioinformatics or for computer science graduates with an interest in biology. The supervisory team and their research groups will provide appropriate support and training tailored to the background of the student.

References:

Mok, G.F., Folkes, L., Weldon, S.A., Maniou, E., Martinez-Heredia, V., Godden, A.M., Williams, R.M., Sauka-Spengler, T., Wheeler, G.N., Moxon, S. and Münsterberg, A.E., 2021. Characterising open chromatin in chick embryos identifies cis-regulatory elements important for paraxial mesoderm formation and axis extension. Nature Communications, 12(1), pp.1-15.

Bradley, T. and Moxon, S., 2020. FilTar: using RNA-Seq data to improve microRNA target prediction accuracy in animals. Bioinformatics, 36(8), pp.2410-2416.