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- Create Date May 14, 2024
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GLOMICAVE project addresses the need for building systems that allow scaling analytical processes of primary data and supporting downstream large-scale omics experiments, by maximizing the utility of pre-existing massive omics datasets to increase the understanding of biological systems as a whole. In this context, the main objective of GLOMICAVE is to develop a cloud-based genotype to phenotype platform – relying on Big Data Analytics (BDA) and Artificial Intelligence (AI) techniques. The main outcome will be a multi-omics data analysis open platform consisting of multiple tools to assist experts and non-experts in identifying and understanding new links between genotype and phenotype. Consequently, GLOMICAVE aims to exploit the information hidden in the existent scientific literature and large-scale omics datasets for a better understanding of genotype-phenotype relationships.
GLOMICAVE integrative approach will be validated in 3 different industrial sectors addressing specific challenges in 6 business cases. This deliverable is focused on the Meat Quality Business Case. In this use case, beef cattle are reared to produce meat which implicates a range of complex traits including live phenotypes (e.g. birth weight, average daily gain), carcass traits (e.g. carcass weight, conformation score, fat weight,), meat quality traits (e.g. marbling score, meat weight, meat colour). The information available is mostly at the genomic level and is obtainable in QTLdb database. Below the genomic level, complexity increases with a higher number of transcripts, proteins and metabolites. In this use case, GLOMICAVE is focused on finding relationship between relevant phenotypes included in beef production and QTL, genome (SNP and WGS), transcriptome (RNA-seq and non-coding RNAseq) and methylome (Reduced Representation Bisulfite Sequencing). The objective was to improve production efficiency and meat quality investigating the relationship between relevant phenotypes for the beef industry (including traits at two levels - carcass and meat- through the characterisation of signatures from different molecular features (genome, transcriptome, epigenome and metabolome) in muscle originated from grass versus grain-fed steers. The outcomes were directly translated into increased profits for the beef sector, while responding consumers' preferences, with further implications for animal welfare and exploitation of sustainable food sources.
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