Bioinformatics strategies for early life microbiomics

描述(由申请人提供)： 背景资料。最近的证据表明，人体最初在微生物中定居的重要性--微生物群的建立--是一生健康结果的决定因素。可用于在多个时间点描述大规模人群中的人类微生物组的元基因组技术进步，正在迅速超过澄清微生物组组装模式并调查观察到的模式与健康和疾病之间的联系所需的计算工具。基于网络的方法在填补这一关键空白方面有着巨大的希望，但可用的网络分析工具系列存在重大限制。识别生物网络中的临床相关模式，特别是时间进化的网络，迫切需要新的方法学方法。 候选人。霍恩博士是一名计算流行病学家，由定量生物医学研究所(IQBS)和达特茅斯学院盖泽尔医学院内的一个多学科团队指导。凭借她在耶鲁大学的传染病流行病学博士学位和在儿童医院波士顿信息学项目/哈佛医学院的博士后培训，她非常有资格成功完成她提出的研究和职业发展计划，并在该奖项的支持下过渡到作为生物医学信息学科学家的独立。霍恩博士拥有成熟而富有成效的研究背景，她的跨学科研究已发表了26篇同行评议的论文。 环境IQBS和盖泽尔医学院为拟议的职业发展和研究提供了理想的环境。以霍恩博士的模范导师和顾问团队为核心的大学学术环境得到了众多最先进的研究中心和核心设施的支持，其中包括世界级的超级计算设施。定期的研究研讨会涉及生物医学信息学、基因组学和生物统计学的最新进展，信息学、基因组学和医学的频繁系列讲座吸引了来自世界各地的主要研究人员。 职业发展计划。这项建议的目标是获得有指导的研究经验和职业发展活动，以便将霍恩博士的职业生涯推进到生物医学信息学的独立研究人员的职业生涯。霍恩博士之前的研究侧重于对传染病风险的分布和决定因素进行计算研究。她在微生物基因组数据分析(博士后培训)和公共卫生信息学(博士后培训)方面有经验。拟议的职业发展活动和指导研究经验将以两种不同的方式将Hoen博士的专业知识提升到一个新的水平：(1)提供她在生物医学信息学研究新工具的开发、评估和应用方面的指导研究经验；(2)通过为临床和翻译研究提供生物医学信息学方面的培训。Hoen博士建议参加课程作业、研讨会、系列研讨会和研究会议，重点是职业道德、转化研究方法、计算生物学(包括基因组学、元基因组学、网络分析和系统生物学)。霍恩博士组建了一个模范的指导团队，由杰森·摩尔博士领导，他在生物医学信息学方面有着长期的指导、领导和资助研究的历史。她的导师和顾问团队包括国际知名的信息学方法开发、网络分析和复杂系统、元基因组数据的计算分析、临床和翻译科学、流行病学和生物医学信息学领导力方面的专家。这个团队所体现的集体专业知识将为Hoen博士的职业发展和她在生物医学信息学方面的多学科、转化性研究的成功实施提供杰出的资源。霍恩博士的团队致力于确保她获得成功的指导研究和职业发展经验，因为她正在努力在该奖项的时间框架内实现独立。 研究计划。拟议研究的目标是开发、评估和应用计算工具，以捕捉微生物群落组成的大型纵向数据集的复杂性，以及来自正在进行的独特大分子流行病学研究的丰富临床信息，使用系统方法，重点关注人类宿主、组成微生物组、暴露、健康和疾病的细菌分类群落之间的复杂相互作用。这项研究将建立在一系列网络的基础上，根据早期生命暴露和与健康相关的结果，这些网络代表了婴儿和微生物定居模式之间的复杂相互作用。建议的分析将解决这些网络捕获的复杂模式，包括多尺度关联、网络组件之间的多路交互以及不断变化的网络中的时间动态。这项研究的首要假设是，这些创新的方法可以揭示微生物组组成与早期生命中的健康和疾病之间的关键联系。这项研究的翻译潜力在于通过操纵和保护健康微生物群的治疗来降低疾病风险的新机会。

DESCRIPTION (provided by applicant): Background. Recent evidence points to the importance of the initial colonization of the human body with microorganisms-the establishment of the microbiome-as a determinant of health outcomes throughout life. The metagenomic technological advances available for characterizing the human microbiome across large cohorts at multiple time points are rapidly out-pacing the computational tools needed to clarify patterns of assembly of the microbiome and investigate connections between observed patterns and health and disease. Network-based methods have immense promise for filling this critical gap, but there are major limitations in the available suie of network analytic tools. The identification of clinically relevant patterns in biological network, and in particular, temporally evolving networks, urgently requires novel methodological approaches. Candidate. Dr. Hoen is a computational epidemiologist mentored by a multidisciplinary team within the Institute for Quantitative Biomedical Sciences (iQBS) and the Geisel School of Medicine at Dartmouth College. With her PhD in infectious disease epidemiology at Yale University and her postdoctoral training in the Children's Hospital Boston Informatics Program/Harvard Medical School, she is extremely well qualified to successfully accomplish her proposed research and career development plan and transition to independence as a scientist in biomedical informatics with the support of this award. Dr. Hoen has an established, productive research background that has resulted in 26 peer-reviewed publications on her interdisciplinary research. Environment. iQBS and the Geisel School of Medicine offer the ideal environment for the proposed career development and research. A collegial intellectual environment, at the core of which is Dr. Hoen's exemplary team of mentors and advisors is bolstered by numerous state-of-the-art research centers and core facilities, including a world-class supercomputing facility. Regular research seminars address current advances in biomedical informatics, genomics and biostatistics, and frequent lecture series in informatics, genomics and medicine attract leading investigators from around the world. Career Development Plan. The goal of this proposal is to gain mentored research experience and career development activities in order to advance Dr. Hoen' career into that of an independent researcher in biomedical informatics. Dr. Hoen's previous research has focused on computational studies of the distribution and determinants of infectious disease risk. She has experience in the analysis of microbial genomic data (doctoral training) and in public health informatics (postdoctoral training). The proposed career development activities and mentored research experience would take Dr. Hoen's expertise to a new level in two distinct ways: (1) by providing her mentored research experience in the development, evaluation and application of novel tools for biomedical informatics research; and (2) by providing her training in biomedical informatics for clinical and translational research. Dr. Hoen proposes to participate in coursework, workshops, seminar series, and research meetings that focus on professional ethics, translational research methods, computational biology including genomics, metagenomics, network analysis, and systems biology. Dr. Hoen has assembled an exemplary mentoring team, led by Dr. Jason Moore, who has a longstanding history of mentorship, leadership, and funded research in biomedical informatics. Her team of mentors and advisors include internationally known experts on informatics methods development, network analysis and complex systems, computational analysis of metagenomic data, clinical and translational science, epidemiology and biomedical informatics leadership. The collective expertise embodied by this team will provide an outstanding resource for the development of Dr. Hoen's career and the successful execution of her multidisciplinary, translational research in biomedical informatics. Dr. Hoen's team is committed to ensuring that she achieves a successful mentored research and career development experience as she works toward independence within the timeframe of this award. Research Plan. The goal of the proposed research is to develop, evaluate and apply computational tools that capture the complexity of large, longitudinal datasets of microbial community composition in conjunction with rich clinical information from a unique ongoing large molecular epidemiologic study using a systems approach that focuses on the complex interactions between human hosts, the communities of bacterial taxa that comprise the microbiome, exposures, health and disease. The framework upon which this research will be built is a series of networks that represent the complex interactions between infants and microbial colonization patterns in light of early life exposures and health-related outcomes. The analyses proposed will address complex patterns captured by these networks including multi-scale associations, multi-way interactions between network components, and temporal dynamics in changing networks. The overarching hypothesis of this research is that these innovative approaches can reveal critical associations between microbiome composition and health and disease in early life. The translational potential of this research lies in novel opportunities to reduce disease risk through therapies that manipulate and preserve a healthy microbiome.