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PhD Studentship: Meta-analyses and Analytical Platform Design for Biological Inference of Bacterial Metabarcoding Data

University of Exeter - College of Life and Environmental Sciences

Qualification Type: PhD
Location: Exeter
Funding for: UK Students, EU Students, International Students
Funding amount: From £15,609
Hours: Full Time
Placed On: 22nd November 2021
Closes: 7th January 2022
Reference: 4380

PCR amplicon datasets (metabarcoding) of bacterial communities form the basis of studies of microbial communities in terrestrial and marine environments, and represent a Big Data resource for understanding the roles of microbes in many biological and ecological processes. However, because most bacteria remain uncharacterised, interpretation of these sequence datasets is limited, and most studies are forced to report results at high taxonomic levels, providing only high level and often superficial insight into key microbial taxa involved and their significance. 

The vast amount of sequence data and associated metadata contained within multiple metabarcoding datasets can be used to collate information about individual bacterial taxonomic units (OTUs and/or ASVs). By defining these units consistently across datasets, information about geographical distributions, habitat(s), host-associations, pathogenicity, ecological preferences and responses, etc. can be inferred for OTUs/ASVs of interest, at a much higher resolution (genus to species level) than previously possible. This information can then be used to greatly enhance interpretation of bacterial diversity in any microbial barcoding study, based on a sequence/big data-driven synthesis of available information derived from across multiple compatible datasets. 

An improved ability to infer characteristics of microbial taxa is particularly important for host-associated bacteria, which strongly influence host health and functioning of a host-symbiont system, both as primary symbionts and members of the micro/pathobiome. The occurrence and dynamics of such lineages can be identified by sequence-based statistical analyses, but in most cases other information about those lineages is lacking. 

The aims of this project are 1) to use existing bacterial 16S rRNA gene V3/4 region metabarcoding data (e.g. those in the MGnify resource), with associated metadata, to undertake proof-of-concept studies demonstrating the power of meta-analytical approaches for improved biological inference of bacterial diversity; 2) to work with collaborators at the European Bioinformatics Institute (EMBL-EBI) in designing an online, freely available platform for carrying out such analyses, and encouraging standards of data collection and curation, and 3) apply this approach to microbiome studies carried out in aquaculture systems worldwide to advance interpretation of host-associated bacteria and their importance/roles in these systems. 

The student will work within a cross-disciplinary team, visiting partner institutions as necessary. The project will integrate molecular biology, bioinformatics and computational biology, ecology, linking to animal and environmental health, particularly relating to aquaculture. 

Anticipated outputs are 1) primary analytical, positional, and methodological publications based on the approaches outlined above, 2) dissemination and demonstration of the analytical approach to the wider community, including Defra’s DNA Centre of Excellence, 3) an openly accessible online analytical platform to which the student’s work has significantly contributed, and 4) application of the approach within the OneHealth Aquaculture framework. 

Funding:

This award provides annual funding to cover Home tuition fees and a tax-free stipend.  For students who pay Home tuition fees the award will cover the tuition fees in full, plus at least £15,609 per year tax-free stipend.  Students who pay international tuition fees are eligible to apply, but should note that the award will only provide payment for part of the international tuition fee and no stipend. 

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