Meta-analyses and analytical platform design for biological inference of bacterial metabarcoding data [Biosciences, Environmental Science, Ecology, Marine biology, Evolution, Zoology] – PhD (Funded) Ref: 4380
About the award
Dr Ryan Ames - College of Life and Environmental Sciences, Streatham Campus, University of Exeter.
Dr Diana Minardi - Centre for Environment, Fisheries & Aquaculture Science
Dr Daniel Read - UK Centre for Ecology and Hydrology
Dr Rob Finn - (EMBL-EBI)
Biosciences, Streatham Campus, Exeter
The Centre for Environment, Fisheries & Aquaculture Science (Cefas) and the University of Exeter have a Strategic Alliance that aims to combine the complementary capabilities and perspectives of both organisations.
This Alliance was further strengthened in 2018 via establishment of the Centre for Sustainable Aquaculture Futures (see: https://www.exeter.ac.uk/research/saf/).
A major component of the Alliance is to support joint PhD studentships. In accordance, we are pleased to announce the intention to fund two new PhD projects to start in September 2022. This project is one of four projects that are in competition for funding from the University of Exeter and Cefas.
For eligible students the studentship will cover Home tuition fees plus an annual tax-free stipend of at least £15,609 for 3.5 years full-time, or pro rata for part-time study. The student would be based in Biosciences in the College of Life and Environmental Sciences at the Streatham Campus in Exeter.
Background and proposed project: 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.
Ryan Ames (Exeter University EPSRC/BBSRC Innovation Fellow) has extensive experience with the bioinformatic analysis of a variety of sequencing technologies including microbiome, genomic, transcriptomic and small RNA sequencing.
Robert Finn (EMBL-EBI, Team Leader Microbiome Informatics) has extensive experience building and maintaining bioinformatics databases that are widely used by the scientific community. His team produces the MGnify database, one of the world’s largest resources for analyses of microbiome-derived sequence data (metabarcoding, metagenomic and metatranscriptomic).
Daniel Read (UKCEH, Group Lead – Molecular Ecology) conducts research on the application of molecular approaches to understand the structure, function and dynamics of biological communities, from microbes to multicellular eukaryotes.
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.
The conditions for eligibility of home fees status are complex and you will need to seek advice if you have moved to or from the UK (or Republic of Ireland) within the past 3 years or have applied for settled status under the EU Settlement Scheme.
International applicants need to be aware that you will have to cover the cost of your student visa, healthcare surcharge and other costs of moving to the UK to do a PhD.
Applicants for this studentship must have obtained, or be about to obtain, a First or Upper Second Class UK Honours degree, or the equivalent qualifications gained outside the UK, in an appropriate area of science or technology.
If English is not your first language you will need to have achieved at least 6.5 in IELTS and no less than 6.0 in any section by the start of the project.
Alternative tests may be acceptable (see http://www.exeter.ac.uk/postgraduate/apply/english/).
How to apply
In the application process you will be asked to upload several documents.
• Letter of application (outlining your academic interests, prior research experience and reasons for wishing to undertake the project).
• Transcript(s) giving full details of subjects studied and grades/marks obtained (this should be an interim transcript if you are still studying)
• Names of two referees familiar with your academic work. You are not required to obtain references yourself. We will request references directly from your referees if you are shortlisted.
• If you are not a national of a majority English-speaking country you will need to submit evidence of your proficiency in English.
The closing date for applications is midnight on 7th January 2022. Interviews will be held in early February 2022.
If you have any general enquiries about the application process please email email@example.com or phone 0300 555 60 60 (UK callers) +44 (0) 1392 723044 (EU/International callers).
Project-specific queries should be directed to the main supervisor.
|Application deadline:||7th January 2022|
|Value:||3.5 year studentship: Home tuition fees and an annual maintenance allowance at current Research Council rate. Current rate of £15,609 per year.|
|Duration of award:||per year|
|Contact: PGR Admissions Teamfirstname.lastname@example.org|