University of Oxford Big Data Institute: Development & dissemination of efficient analysis methods for large, complex, heterogeneous clinical datasets

牛津大学大数据研究所：发展

• 批准号: MR/L016265/1
• 负责人: Peter Donnelly
• 金额: $ 891.94万
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2014
• 资助国家: 英国
• 起止时间: 2014 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FL016265%2F1
• 关键词: University; Oxford; Big; Data; Institute

Background: Understanding the determinants of common life-threatening and disabling disease is challenging. Such conditions are typically caused by many different factors (genetic, physiological, behavioural, infectious and environmental), which may be occur at different times of life (including developmental factors as well as those that may occur much later; e.g. cigarette smoking, alcohol, co-morbidity, and treatment). Furthermore the effects may manifest over a range of timescales (immediate to many decades) and time courses (acute, chronic stable, progressive, relapsing/remitting). Vision: The University of Oxford Big Data Institute (BDI) sets out to be an international centre of excellence for the analysis of very large and complex biomedical data sets. The Institute will combine world-class academic leadership with a core infrastructure of cutting-edge technology and high calibre scientists. Built upon Oxford's internationally leading expertise in epidemiology, genomics, imaging, computer science, and infectious disease surveillance, the BDI will be a national resource for the development of new analytical methods to facilitate the generation, storage, analysis and sharing of data in very large clinical studies. This work will provide transformative advances in the speed and range of research into the causes and consequences, prevention and treatment of disease, and will be particularly relevant to understanding the role of genetic and environmental influences on common life-threatening and disabling diseases such as cancer, cardiac disease, stroke, and dementia.Knowledge Transfer: In addition to primary research, the BDI will play a major role in capacity building through training, dissemination of research methods, and stakeholder engagement:- A new MRC Big Data Training Academy will deliver an extensive, flexible and outward-looking portfolio of training and career development opportunities accessible to researchers of all levels within and outside Oxford, including a new doctoral training programme, support for post-doctoral training fellows, visitor and exchange programmes, and a broad range of short courses suitable for graduate training and continuing professional development.- Effective collaboration with local, national and international experts (including partnerships with academia, healthcare, and pharmaceutical and IT industries) will enhance the development, evaluation and adoption of new research methods and tools.- The Institute will work with regulators and other key stakeholders to develop standards for data storage, sharing and analysis; and to promote appropriate and proportionate regulatory and governance approaches that are fit-for-purpose in areas such as privacy, consent, information security, data access and sharing, intellectual property, and the intersection between research and routine practice.- Public engagement activities will promote understanding, address concerns, and develop trust in "Big Data" approaches to biomedical research.

背景：了解常见危及生命和致残疾病的决定因素具有挑战性。这种情况通常是由许多不同的因素（遗传、生理、行为、感染和环境）引起的，这些因素可能发生在生命的不同时期（包括发育因素以及可能发生在较晚的因素，例如吸烟、饮酒、合并症和治疗）。此外，这种影响可能在一定的时间尺度（即刻到几十年）和时间过程（急性、慢性稳定、进行性、复发/缓解）上表现出来。愿景：牛津大学大数据研究所（BDI）旨在成为一个卓越的国际中心，用于分析非常庞大和复杂的生物医学数据集。该研究所将把世界一流的学术领导与尖端技术和高素质科学家的核心基础设施结合起来。基于牛津大学在流行病学、基因组学、成像、计算机科学和传染病监测方面的国际领先专业知识，BDI将成为开发新分析方法的国家资源，以促进超大型临床研究数据的生成、存储、分析和共享。这项工作将在疾病的起因和后果、预防和治疗的研究速度和范围方面提供变革性的进展，并将特别与了解遗传和环境影响对癌症、心脏病、中风和痴呆等常见危及生命和致残疾病的作用有关。知识转移:除了初级研究，BDI还将通过培训、传播研究方法和利益相关者参与，在能力建设方面发挥重要作用：-新的MRC大数据培训学院将为牛津大学内外各级研究人员提供广泛、灵活和外向型的培训和职业发展机会，包括新的博士培训计划，支持博士后培训研究员。访客和交流计划，以及一系列适合毕业生培训和持续专业发展的短期课程。-与本地、国内及国际专家有效合作（包括与学术界、医疗保健、制药业及资讯科技业建立伙伴关系），有助发展、评估及采用新的研究方法和工具。-该研究所将与监管机构和其他主要利益相关者合作，制定数据存储、共享和分析标准；促进在隐私、同意、信息安全、数据访问和共享、知识产权以及研究与日常实践之间的交叉等领域采用适当和相称的监管和治理方法。-公众参与活动将促进对生物医学研究“大数据”方法的理解，解决问题，并建立信任。

项目成果

A general framework for predicting the transcriptomic consequences of non-coding variation

预测非编码变异转录组后果的通用框架

• DOI: 10.1101/279323
• 发表时间: 2018
• 作者: Abdalla M

Best Practices for Ethical Sharing of Individual-Level Health Research Data From Low- and Middle-Income Settings.

• DOI: 10.1177/1556264615594606
• 发表时间: 2015-07
• 期刊: Journal of empirical research on human research ethics : JERHRE
• 作者: Bull S;Cheah PY;Denny S;Jao I;Marsh V;Merson L;Shah More N;Nhan le NT;Osrin D;Tangseefa D;Wassenaar D;Parker M
• 通讯作者: Parker M

Clinical whole-genome sequencing in severe early-onset epilepsy reveals new genes and improves molecular diagnosis.

• DOI: 10.1093/hmg/ddu030
• 发表时间: 2014-06-15
• 期刊: Human molecular genetics
• 影响因子: 3.5
• 作者: Martin HC;Kim GE;Pagnamenta AT;Murakami Y;Carvill GL;Meyer E;Copley RR;Rimmer A;Barcia G;Fleming MR;Kronengold J;Brown MR;Hudspith KA;Broxholme J;Kanapin A;Cazier JB;Kinoshita T;Nabbout R;WGS500 Consortium;Bentley D;McVean G;Heavin S;Zaiwalla Z;McShane T;Mefford HC;Shears D;Stewart H;Kurian MA;Scheffer IE;Blair E;Donnelly P;Kaczmarek LK;Taylor JC
• 通讯作者: Taylor JC

Large Variations in HIV-1 Viral Load Explained by Shifting-Mosaic Metapopulation Dynamics.

• DOI: 10.1371/journal.pbio.1002567
• 发表时间: 2016-10
• 期刊: PLoS biology
• 影响因子: 9.8
• 作者: Lythgoe KA;Blanquart F;Pellis L;Fraser C
• 通讯作者: Fraser C

Associations of device-measured physical activity across adolescence with metabolic traits: Prospective cohort study.

• DOI: 10.1371/journal.pmed.1002649
• 发表时间: 2018-09
• 期刊: PLoS medicine
• 影响因子: 15.8
• 作者: Bell JA;Hamer M;Richmond RC;Timpson NJ;Carslake D;Davey Smith G
• 通讯作者: Davey Smith G