Bioinformatics strategies for early life microbiomics

描述（由申请人提供）： 背景最近的证据表明，微生物最初在人体内定植的重要性，即微生物组的建立，是整个生命健康结果的决定因素。可用于在多个时间点表征大型队列中人类微生物组的宏基因组技术进步正在迅速超越阐明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具有巨大的潜力，但网络分析工具的可用性存在重大限制。在生物网络，特别是时间演变的网络，临床相关模式的识别，迫切需要新的方法。 候选人Hoen博士是一位计算流行病学家，由定量生物医学科学研究所（iQBS）和达特茅斯学院Geisel医学院的多学科团队指导。凭借她在耶鲁大学的传染病流行病学博士学位和她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独立的生物医学信息学科学家。Hoen博士有一个成熟的，富有成效的研究背景，导致了26个同行评议的出版物对她的跨学科研究。 环境iQBS和Geisel医学院为拟议的职业发展和研究提供了理想的环境。学院的智力环境，在其核心是霍恩博士的导师和顾问的模范团队是由众多国家的最先进的研究中心和核心设施，包括世界一流的超级计算设施支持。定期的研究研讨会讨论生物医学信息学，基因组学和生物统计学的当前进展，信息学，基因组学和医学的频繁讲座系列吸引了来自世界各地的主要研究人员。 职业发展计划。该提案的目标是获得指导性的研究经验和职业发展活动，以推进Hoen博士的职业生涯成为生物医学信息学的独立研究人员。Hoen博士以前的研究主要集中在传染病风险分布和决定因素的计算研究。她在微生物基因组数据分析（博士培训）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的研究经验将采取博士霍恩的专业知识，以两种不同的方式到一个新的水平：（1）通过提供她的指导研究经验，在开发，评估和应用新的工具，生物医学信息学研究;和（2）通过提供她在生物医学信息学的临床和转化研究的培训。Hoen博士建议参加课程，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，宏基因组学，网络分析和系统生物学。Hoen博士组建了一个由Jason摩尔博士领导的模范指导团队，他在生物医学信息学领域拥有长期的指导、领导和资助研究历史。她的导师和顾问团队包括信息学方法开发，网络分析和复杂系统，宏基因组数据的计算分析，临床和转化科学，流行病学和生物医学信息学领导方面的国际知名专家。该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领域的多学科、转化研究的成功执行提供杰出的资源。Hoen博士的团队致力于确保她在此奖项的时间范围内实现独立工作时获得成功的指导研究和职业发展经验。 研究计划。拟议研究的目标是开发，评估和应用计算工具，这些工具可以捕获微生物群落组成的大型纵向数据集的复杂性，并结合来自一项独特的正在进行的大型分子流行病学研究的丰富临床信息，该研究采用系统方法，重点关注人类宿主之间的复杂相互作用，包括微生物组，暴露，健康和疾病的细菌类群社区。本研究将建立的框架是一系列网络，这些网络代表了婴儿与微生物定植模式之间的复杂相互作用，以及早期生活暴露和健康相关结果。所提出的分析将解决这些网络捕获的复杂模式，包括多尺度关联，网络组件之间的多方式交互，以及不断变化的网络中的时间动态。这项研究的总体假设是，这些创新方法可以揭示微生物组组成与早期生活中的健康和疾病之间的关键关联。这项研究的转化潜力在于通过操纵和保护健康微生物组的疗法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.