Developing bioinformatics approaches for the anaylsis of influenza virus whole genome sequence data.

Influenza viruses have a key impact on human and animal health worldwide, infecting humans and livestock and seeding pandemics. Sequencing technologies have enabled us to monitor and understand the genetic variation of these viruses at unprecedented scale. However, since it is now possible to routinely sequence hundreds or thousands of virus whole genomes, new bioinformatics approaches are required for analysis and processing of data at this scale. Methodological development in this area has become increasingly important for the purposes of understanding how influenza viruses evolve and spread.

The influenza virus genome is composed of 8 segments which continuously mutate and reassort, which result in antigenic drift and antigenic shift. Many standard bioinformatics approaches in microbial genomics were designed for data derived from bacteria that evolve at a much slower rate. As such, many approaches, from classification to mapping, can perform sub-optimally. Simultaneously, modern phylodynamic methods can provide important information, but many are not designed for big data applications.

This project was focused on the development of cutting-edge bioinformatics methods and modelling for the processing and analysis of influenza virus whole genome sequencing data, working with world-class researchers from University of Cardiff and collaborators across the UK. The student designed bioinformatics algorithms and modelling approaches for several parts of the whole genome sequencing pipeline, including sequence assembly, classification, and phylodynamic modelling.