The project represents an ambitious attempt to delve into this universe of uncharacterised biodiversity through a series of sequential investigations. The first step will be to provide a ‘natural classification’ for the ~30,000 phage high-quality genomes sequenced to date, through the definition of natural groupings of related genomic entities or Operational Taxonomic Units (OTUs). In short, this will be achieved by calculating pairwise genetic distances between all available phage genomes by using alignment free algorithms.
The edges of this large network will be pruned to retain only the lowest genetic distances (e.g. 95% genetic identity) to define cliques (complete subgraphs) that can be interpreted as natural species. We have successfully pioneered this approach for animal viruses and bacterial plasmids.
Those ~30,000 genomes represent only a minuscule fraction of the entire extant diversity of phages. Though, classification and characterization of the known diversity will inform key features to identify additional phages by sifting through large numbers of available metagenomic sequence libraries from a variety of sources, such as saliva or stool samples from animals, soil or water. This effort will greatly increase the catalog of known phages. Beyond cataloging phage diversity, the student will develop dedicated tools to identify novel phage genes and identify evidence for alternative codon usage.
The final step of the PhD project will be to characterise the distribution of phages in the environment and the key drivers behind the apportionment of their diversity. This includes their co-occurrence with bacterial hosts, geographical and physico-chemical features, such as temperature and humidity of their sampling location. This will be achieved by defining the occurrence and co-occurrence of OTUs, which reflects their flow between environmental settings.
Subject to a competition to identify the strongest applicants, this studentship would be fully funded by the AI-INTERVENE NERC Doctoral Focal Award. Please note that this PhD will be hosted at University College London*
Apply here https://ai-intervene-dfa.grantplatform.com/
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