Application of Computational Methods and Multiomic Techniques to the Analysis of Marine Life

Lead Research Organisation: University of Manchester
Department Name: Earth Atmospheric and Env Sciences


It is well known that the marine environment remains poorly understood. This is partially due to the large, complex nature of marine ecosystems and organisms; there are around 15,000 species of fish in the world, and many thousands more from other phyla. Computational approaches can therefore be essential in gaining key insights into marine biological processes, as they can help us integrate and understand vast amounts of data, which would otherwise be impossible or infeasible to interpret. The application of these methods combined with multiomics data from cutting edge research can have significant impacts in a number of ways.

The sustainability of fish within Europe is becoming an increasing concern, with overfishing creating a depletion of fish stocks below maintainable limits. Numerous fish are caught as bycatch that cannot be used for human consumption, but many are currently discarded at sea so that they do not impact on the vessels' total allowable catches. The reformed common fisheries policy, agreed in 2013, which states how, where and who can exploit fish to enable sustainable fishing, includes provisions for a landings obligation which will effectively ban the discarding at sea of TAC fish species. The catch will therefore need to be sorted to species level, separated into the wanted catch destined for the human consumption market, the unwanted catch of quota species that has to be retained onboard and landed, and the unwanted catch of non-quota species that can be discarded at sea. When the unwanted catch is lumped together as bulk it often decomposes, especially on long fishing trips, making them difficult to identify. Given that all catches of species under quota will be subtracted from the quota of those species there is need to develop multiomic methods to identify/verify the species in the catch when the catch is spoiled.

The new CFP also includes the application of legal minimum conservation reference sizes in order to prevent the smallest fish entering the human consumption market. If caught they will have to be utilized in some other way, such as for energy production, composting, silage and fish meal. In each case storage bins will be required at the quay to collect the discards and transport them for processing. Most of the commercial outlets who would convert the discards into usable products, e.g. fish meal, have indicated that they would need to know the source of the discards since they do not accept illegal, unreported and unregulated fish.

The proposed project will explore the applications of advanced computational methods, such as machine learning, along with data derived from 'omics' methodologies such as proteomics and genomics for high-throughput species identification well as biological age estimation. Ideally these methods would prove to be useful to trace the species from the processed product (fish meal) to cross check with skipper's records and therefore monitoring/enforce the landing obligation, as well as to monitor the impacts on overfishing on population demography.

There are many other biological factors affecting global fish stocks, especially with an ever increasing reliance on aquaculture. This is particularly true in the UK, with aquaculture Salmon playing a huge part in the Scottish economy and providing a large fish consumed in the UK. It is therefore highly important to be able to study the biotic challenges facing aquaculture, such as Salmon lice or microbe-harboring organisms, to a level of detail which can only be gained through the use of a combination of computational methods being applied to multiomics data. This research can be further applied to fisheries by analyzing the sources and reserves of marine microbiota which contribute to the spoilage of caught fish, with a view to reducing exposure and therefore reducing the potential for wastage.


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Studentship Projects

Project Reference Relationship Related To Start End Student Name
BB/M011208/1 01/10/2015 31/03/2024
2282275 Studentship BB/M011208/1 01/10/2019 31/03/2024