Understanding molecular variations of cattle genome by machine learning

通过机器学习了解牛基因组的分子变异

• 批准号: 2868882
• 金额: --
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2868882
• 关键词: Understanding; molecular; variations; cattle; genome

With large-scale omics data available, this PhD project will train a PhD researcher with innovative ideas of integrating informatics into animal genetic and genomic research in the new era. The student will integrate genetics and machine learning methods to identify molecular variations of the cattle genome and assess the association of molecular variants with complex phenotypes (e.g., health, production, efficiency, fitness). The successful candidate will have the opportunity to work with large-scale cattle omics data including whole-genome sequence, RNA-sequence, complex phenotypes of UK cattle population. In addition to the data, the student will have access to methodology, software, and high-performance computational resources (e.g., Nvidia DGX Workstation, clusters, Jupyter Notebook) that have been well-established by us to address the research questions. The data will be jointly analyzed to identify molecular variations of cattle genome (e.g., eQTL) using sequence data and machine/deep learning methods. The identified variants with functional significance will be associated with cattle complex phenotypes to investigate the links between functional variants and cattle health, fitness, production, and efficiency. The PhD project offers interdisciplinary research and the project has strong collaborative efforts between SRUC and University of Edinburgh through cross-supervision. The student will obtain fruitful research training from both SRUC and University of Edinburgh on quantitative genetics, statistical modelling, and machine learning. The student will also obtain valuable training in scientific writing, project management skills, and career development skills. Students are part of the EASTBIO training programme and will undertake enhanced subject-specific, core bioscience and generic skills training and a 3-month professional internship (PIPS) outwith academia. The students are expected to submit a PhD thesis in 4 years.

有了大规模的OMIC数据，该博士学位项目将培训一名博士研究人员，其创新思想将信息学整合到新时代的动物遗传和基因组研究中。学生将整合遗传学和机器学习方法，以鉴定牛基因组的分子变异，并评估分子变异与复杂表型（例如健康，生产，效率，健身）的关联。成功的候选人将有机会使用大规模的牛绝语数据，包括全基因组序列，RNA序列，英国牛群的复杂表型。除了数据外，学生还可以访问我们已经良好建立的研究问题的方法论，软件和高性能计算资源（例如NVIDIA DGX工作站，簇，Jupyter Notebook）。将共同分析数据，以鉴定使用序列数据和机器/深度学习方法的牛基因组（例如EQTL）的分子变异。具有功能显着性的确定变体将与牛复杂表型有关，以研究功能变异与牛健康，健身，生产和效率之间的联系。博士学位项目提供跨学科研究，该项目通过跨司法院在SRUC和爱丁堡大学之间进行了强有力的合作努力。该学生将从SRUC和爱丁堡大学获得有关定量遗传学，统计建模和机器学习的富有成果的研究培训。该学生还将获得科学写作，项目管理技能和职业发展技能的宝贵培训。学生是EASTBIO培训计划的一部分，将在学术界接受增强的特定学科，核心生物科学和通用技能培训以及3个月的专业实习（PIPS）。预计学生将在4年内提交博士学位论文。