MetaPlat · Development of an Easy-to-use Metagenomics Platform for Agricultural Science

Development of an Easy-to-use Metagenomics Platform for Agricultural Science

The MetaPlat project is focussed on creating an easy-to-use integrated hardware and software platform to enable the rapid analysis of large metagenomic datasets. It addresses the subject in a holistic way by creating a platform that handles large metagenomics data and produces in-depth analyses and comparisons thereby allowing researchers to make full use of the data generated for each sample. This requires many disciplines and skills to be brought together in order to achieve success. Furthermore it requires a mixture of creative research, focused application and commercial awareness, which together will lead to the development of such a platform. Dairy livestock have the ability to convert tough plant material such as grass into quality, high-protein products for human nutrition through fermentation by microbes in their digestive tracts, but a by-product of this action is substantial methane production. Methane is a greenhouse gas that has more heat trapping capacity than CO2 and is produced in vast quantities by livestock world-wide. The dairy and beef industry, has significant economic, nutritional, and cultural value so it is not feasible to demand that everybody stop drinking milk and eating meat. Any strategy that aims to mitigate greenhouse gas emission in agriculture also needs to maintain the efficiency of cattle in food production by investigating the action of microbes in livestock. With this in mind we are creating an easy-to-use high performance machine learning platform with the objective of enabling the rapid analysis of large metagenomic datasets, in order to better understand the microbial mechanisms behind efficient food production, better meat quality and methane production. The project goal has been broken down into the following core objectives: • Sample collection preparation, and sequencing • Curation of the reference databases (phylogeny-aware new classification and previously unclassified sequences using machine learning) • Development of accurate classification algorithms • Real-time or time-efficient comparison analyses • Production of statistical and visual representations conveying more useful information. • Platform Integration • Provide insights into probiotic supplement usage, methane production and feed conversion efficiency in cattle

Data: CORDIS, © European Union