The discovery of small RNAs (sRNAs) in plants and animals has revolutionised our knowledge of how genes are regulated. However, although much progress has been made on specific small RNAs and their targets (such as microRNAs), very little is known about the genome-wide structure of sRNA regulatory networks. In this project, the student will work together with a unique combination of world-class experts in bioinformatics, genomics and sRNAs with the aim of identifying, validating and visualising regulatory interaction networks of sRNAs and their target genes. This will build on BBSRC-funded work on genome-wide target sRNA target identification and validation using the "degradome".

Aim and Objectives:  The main aim is to produce software to construct, analyse and visualise sRNA interaction networks. Specific organisms of interest will include Arabidopsis thaliana and medicago, as well as important crops such grape and tomato. This will be achieved through the following objectives:

(A) Network Construction: The PhD student will develop algorithms for the identification and construction of sRNA mediated interaction networks using local and publicly available high-throughput PARE sequence data, as well as secondary data such as sRNA, gene and gene degradation expression profiles. This will involve processing thousands of sRNA-mediated interactions and using PARE degradation products and their relative signal strength to validate interactions. A by-product the analysis will be to investigate and update current sRNA targeting rules (especially for microRNAs), as well as to potentially discover new classes of sRNAs. Networks will be constructed in which nodes will comprise genes and sRNAs; edges linking the nodes will be determined by sRNA interactions. Edge weights, indicating e.g. statistical reliability of interactions, will also be computed from quantities such as sRNA and gene expression levels. Networks will be annotated with tertiary information, linking them to genome data as well as public repositories such as miRBase, UniProt and GO.

(B) Network Analysis and Visualisation: To gain insight into the function of millions of sRNAs through PARE-based network construction, the student will look for common patterns within networks and also develop ways to compare computed networks. This will involve data-mining techniques to investigate sub-networks, feedback-loops and cascades of interactions involving sRNAs such as microRNAs and ta-si RNAs. Network visualisation will also comprise an important part of this analysis. Novel methods for viewing and analysing sRNA networks will be investigated, including using 3D techniques to explore networks using contemporary graphics hardware (such as GPU's) and software. Patterns found in networks will be investigated with UEA biologists and also compared with those given in the biological literature. This will enable us to make predictions about sRNA functions, and to understand how common certain pathways and subnetworks are in the plant cell.

The student will work together with Prof. Vincent Moulton (CMP, UEA), Prof. Tamas Dalamy (BIO,UEA) and Dr. Mario Caccamo (TGAC). A background in computer science or a related discipline is required, but a prior knowledge of biology is not necessary.

Folkes L, Moxon S, Woolfenden H, Stocks M, Szittya G, Dalmay T, Moulton V (2012) PAREsnip: a tool for rapid genome-wide discovery of small RNA/target interactions evidenced through degradome sequencing. Nucleic Acids Research 40(13): e103.

Stocks M, Moxon S, Mapleson D, Woolfenden H, Mohorianu I, Folkes L, Schwach F, Dalmay T, Moulton V (2012) The UEA sRNA Workbench: A Suite of Tools for Analysing and Visualising Next Generation Sequencing microRNA and Small RNA Datasets. Bioinformatics 28(15): 2059-2061.