A Short Course in Computation, Statistical Analyses, and Interpretation of Microbial Metagenome Data

微生物宏基因组数据的计算、统计分析和解释短期课程

• 批准号: 9119846
• 负责人: Ashley Shade
• 金额: $ 6.2万
• 依托单位: MICHIGAN STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-03 至 2018-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9119846
• 关键词: Address; Algorithms; Animals; Area; Bioinformatics; Biomedical Research; Cloud Computing; Computer software; Creativeness; Data; Data Analyses; Data Set; Databases; Development; Disease; Ecology; Ecosystem; Educational process of instructing; Environment; Equilibrium; Evaluation; Experimental Designs; Faculty; Feedback; Fostering; Foundations; Genbank; Generations; Health; High-Throughput Nucleotide Sequencing; Human; Human Microbiome; Immunity; Language; Lead; Learning; Mental Health; Metagenomics; Michigan; Participant; Pattern; Personal Computers; Plants; Postdoctoral Fellow; Preventive Medicine; Process; Protocols documentation; Pythons; Research; Research Personnel; Role; Running; Science; Shotguns; Statistical Computing; Statistical Data Interpretation; Technology; Testing; Time; Training; Vocabulary; Work; application programming interface; cluster computing; computing resources; data visualization; graduate student; improved; insight; learning materials; lectures; lens; literacy; meetings; metagenome; microbial; microbiome; microbiota; repository; skills; sound; statistics; tool; web site

 DESCRIPTION (provided by applicant): Recently, two important advances have fostered a new era in biomedical research. First, we now recognize that humans, other animals, and plants are important ecosystems for microbial consortia, and that these consortia underpin their hosts' wellness. For example, we are just beginning to understand the role of human microbiota in mental health, immunity, and development. Second, advances in high-throughput sequencing technologies have provided cutting-edge experimental tools for observing the diversity and functions of microbial consortia. For the first time, researchers can grapple with the sheer diversity of microbial consortia associated with hosts, and also can begin to untangle how this diversity contributes to host wellness. Thus, a many biomedical researchers have generated immense, information-rich metagenomic datasets, hoping to realize the promise of these datasets to understand the intricate relationships between microbiota and host. Despite this promise, analyses of metagenomic data present a major challenge. Most biomedical researchers lack the computational, bioinformatic, and statistical training required for appropriate analysis, and also lack a working vocabulary to communicate their analysis needs to statisticians. This is especially concerning in human microbiome research because inaccurate or incomplete analyses can lead to erroneous interpretations that have implications for our approaches to preventative medicine and disease treatment. It also leads to generation of data that ultimately cannot be used to answer research questions because of inadequate statistical power or depth of sequencing in experimental design. We plan to address this need by offering an economical, one-week intensive course to train advanced graduate students, post-docs, and faculty in how to analyze microbial metagenomic data, from raw sequence handling to statistical analyses. Our integrated educational strategy addresses two related training needs. First, we offer general training in the fundamentals of effective computing so that participants will build computing skills needed to execute their analyses independently. We also offer specific training to overcome hurdles particular to microbial metagenome analyses. Participants will develop these skills via practical, hands-on tutorials motivated with real microbial metagenome datasets, and will enrich their learning by engaging in lectures and panel discussions with key leaders in the field. All of our course materials are continually improved and freely available on our course website (edamame-course.org) and disseminated on our GitHub repository. Participant learning will be assessed each year and materials iteratively adapted to best meet course objectives. We successfully ran this course in 2014 and received overwhelmingly positive feedback. Our course evaluation data shows that our educational strategy was effective at increasing skill level, confidence, and analysis sophistication among our participants.

 描述（由适用提供）：最近，两个重要的进步促进了生物医学研究的新时代。首先，我们现在认识到人类，其他动物和植物是微生物财团的重要生态系统，并且这些财团在其宿主的健康下。例如，我们刚刚开始了解人类微生物群在心理健康，免疫学和发展中的作用。其次，高通量测序技术的进步为观察微生物联盟的多样性和功能提供了尖端的实验工具。研究人员首次可以努力应对与宿主相关的微生物财团的多样性，并且也可以开始解开这种多样性如何有助于寄宿健康。这是许多生物医学研究人员产生了巨大的，信息丰富的宏基因组数据集，希望实现这些数据集的承诺，尽管有希望，但对元基因组数据的分析也带来了重大挑战。大多数生物医学研究人员都缺乏适当分析所需的计算，生物信息学和统计培训，并且缺乏与统计学家传达其分析需求的工作词汇。这在人类微生物组研究中尤其关注，因为不准确或不完整的分析可能会导致错误的解释，这对我们的预防医学和疾病治疗的方法具有影响。它还导致生成数据，这些数据最终无法用于回答研究问题，因为实验设计中的统计能力不足或测序深度。我们计划通过提供经济的一周密集课程来满足这一需求，以培训高级研究生，培训后和教职员工如何分析从原始序列处理到统计分析的微生物元基因组数据。我们综合的教育策略满足了两个相关的培训需求。首先，我们提供有效计算基础知识的一般培训，以便参与者建立独立执行分析所需的计算技能。我们还提供特定的培训，以克服微生物元基因组分析的特定障碍。参与者将通过以真实的微生物元素数据集进行的实用，动手教程来发展这些技能，并通过与该领域的主要领导者进行讲座和小组讨论来丰富他们的学习。我们的所有课程材料都可以在我们的课程网站（Edamame-course.org）上不断提高和免费提供，并在我们的GitHub存储库中分发。参与者学习将每年评估，并迭代地适应最佳的课程目标。我们在2014年成功运行了这门课程，并获得了压倒性的积极反馈。我们的课程评估数据表明，我们的教育策略可有效提高技能水平，信心和分析参与者的社交。