Mining Meadows: Biosynthesis and Bioactivity of Phytochemicals from UK Plants

驯化过程必然导致遗传多样性的丧失和种群规模的减少，使动物更容易发生突变。随着这些种群的杂交和突变的积累，它们可能导致疾病、发育障碍和不育--这使它们成为罕见人类疾病的理想模型。大多数研究集中在基因组编码蛋白质的~2%，主要是由于难以识别功能性非编码元件。这就留下了非编码区--有时被称为“暗基因组”--它含有功能性调控序列。大多数与疾病或性状相关的变异都存在于这些非编码的、通常是保守的序列中，该项目的目的是利用由252个哺乳动物基因组组成的独特资源，沿着人口数据，评估人口规模减少和有害突变积累的程度。机器学习的最新发展将用于将模型和信息从高度研究的生物体（人类，小鼠）转移到资源稀缺的基因组，识别功能元件并预测其中突变的影响。

The process of domestication invariably leads to a loss of genetic diversity and a reduction in population size, leaving the animals more vulnerable to mutations. As these populations interbreed and mutations accumulate, they can cause disease, developmental disorders and infertility - making them ideal models for rare human disorders. Most studies focus on the ~2% of the genome encoding proteins, mainly due to the difficulty in identifying functional non-coding elements. This leaves aside the non-coding regions - sometimes called 'the dark genome' - which harbours functional regulatory sequences. The majority of disease- or trait-associated variants are present in these non-coding, often conserved, sequences.The aim of the project is to use a unique resource consisting of 252 mammal genomes, along with population data, to assess the extent by which population sizes have been reduced and deleterious mutations accumulated. The latest developments in machine learning will be applied to transfer models and information from highly-studied organisms (human, mouse) to genomes with scarce resources, identifying functional elements and predicting the impact of mutations within them.