A big data and bioinformatics study to predict and prevent Saprolegniosis outbreaks in Scottish salmon farms

This research project is a prestigious industry fully funded 42 month PhD bioinformatic and modelling studentship that also will provide essential networking in the aquaculture industry. The project builds upon a previous investigation that collected molecular data across salmon farming sites in Scotland, this data can now be used to explore reasons behind the increasing incidences of saprolegniosis outbreaks in Scottish fish farms and determine why some farms suffered significant losses, while others remained relatively disease-free. 

The incidences of saprolegniosis outbreaks in Scottish farms have significantly increased over the last few years. Indeed, some sites have had very high losses (50% or more) due to saprolegniosis, whereas other farms have stayed largely disease free. Why some farms are affected and others seem to avoid disease outbreaks was investigated in a previous project RIFE1 (BBSRC-LINK: RIFE-SOS, BB/P020224/1). This previous research project aimed to discover, map, model and understand the main drivers and risk factors that are related to saprolegniosis outbreaks. The big data study collected and analysed thousands of samples, producing environmental and molecular information from across 15 hatcheries and freshwater salmon farming sites in Scotland. The study of informatic processes in biotic systems is central to molecular life science research and we propose to use the most appropriate modelling and bioinformatic techniques to analyse, in particular, sequencing data from different phylotypes of S. parasitica isolated from fish farms. Analysing this data together with the environmental data will be used as a model to predict outbreaks. We will also employ deep learning for sequence analysis, structure prediction and reconstruction, biomolecular property and function prediction. Thus, the current SAIC- industry supported studentship will follow on from the main findings of the initial work to enable the salmon aquaculture sector to pre-empt any future outbreaks, which will result in improved fish health, fewer losses and reduced production and treatment costs.

Research Project: The potential risk factors pertaining to fish, pathogen and the environment that play an important role in saprolegniosis outbreaks as identified in RIFE1 will be exploited further in the current PhD studentship which will specifically look at:

1. Bioinformatic genomic comparison of dominant infectious S. parasitica phylotype (as identified in RIFE1) with other phylotypes.
2. Bioinformatic gene expression analysis
3. Site-specific prediction tool: one of main outcomes of RIFE1 was the development of a general prediction tool, this will now be extended to the development of site-specific prediction tool to more accurately predict when intervention is and is not required.

 Preferred Candidate Background:

• Honours Degree (1st or 2.1) or Masters in Bioinformatics, Data Science, Computing Science, Biotechnology or related topics
• Good knowledge of big data handling
• Good appreciation of the importance of precision and consistency when dealing with data and metadata
• Knowledge of molecular biology desirable or willingness to learn
• They should be a highly motivated with a keen interest in the research topic
• Possess strong organisational, practical and interpersonal skills
• The candidate should be able to work independently as well as part of a team
• Ability to drive and organise travel for fish trials

We encourage applications from all backgrounds and communities, and are committed to having a diverse, inclusive team. Informal enquiries are encouraged.

Please contact Pieter Van West ([Email Address Removed]) or Dr Debbie McLaggan ([Email Address Removed]) for further information.

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APPLICATION PROCEDURE

• Formal applications can be completed online: https://www.abdn.ac.uk/pgap/login.php
• You should apply for Medical Sciences (PhD) to ensure your application is passed to the correct team for processing.
• Please clearly note the name of the supervisor and project title on the application form. If you do not mention the project title and the supervisor on your application, it will not be considered for the studentship.
• Your application must include: A personal statement, an up-to-date copy of your academic CV, and clear copies of your educational certificates and transcripts.
• Please note: you DO NOT need to provide a research proposal with this application
• If you require any additional assistance in submitting your application or have any queries about the application process, please don't hesitate to contact us at [Email Address Removed]