Reproducible and FAIR Bioinformatics Analysis of Omics Data

项目摘要/摘要 现代生物医学研究日益量化。下一代研究人员将需要一个 一套全新的量化技能，以充分利用他们创建的数据。为了响应这一需求， 当前R25提案的目标是将现有的为期5天的生物信息学技术课程转变为 新的，为期两周的短期课程，可重复和公平的Omics数据的生物信息学分析，以提供 为受训中的生物医学研究科学家开始开发核心提供独特的教育机会 具备生物信息学和大数据集生物统计分析的能力。新课程还将解决 NIH的优先事项包括严谨和可重复性以及可找到、可访问、可互操作和可重复使用(公平) 数据原理。在过去的五年里，担任首席调查员的达特茅斯学院 在MDI生物实验室教授为期5天的生物信息学和生物统计学课程 实习生/年(总计202人)。基于压倒性的积极反馈，目前的项目将延长这5天 在MDI生物实验室进行为期两周的较长时间的短期课程，该课程的特点是学生对 讲师比例(5：1)，更多的亲身体验学习，以及经验丰富的优秀教员 在教学和执行大数据分析方面。新课程设计为每个学员提供约35名学员。 年(在五年期R25项目中总共175个)。它将包括生物统计、科学严谨性和 可再现性和公平数据原则。课程设计还将包括一个简短的概念陈述， 然后是一个练习，学生们将通过应用一项新技能来获得信心。每一次主动学习 课程将有三个难度级别(初学者、中级和高级)，以允许每个学生 按照自己的步调前进。较低的学生与教师比例将使课程主持人能够指导学员 通过现实的挑战，而不会造成挫折感。拟议课程的具体目标包括： 具体目标1.主要为博士后研究员和研究生开发一个为期两周的短期课程， 提高他们设计和分析组学实验的能力，如RNA-Seq、16S(微生物组)、 元基因组学，和sc-RNA-seq数据；具体目标2.通过生物医学增强研究的影响 通过教授科学家负责任的研究行为，以及安全和合乎道德的数据使用 作为严谨、可重复性和公平数据原则；具体目标3.将培训课程传播给 广泛的受众和具体的目标4.评估课程对学生的短期和长期影响， 包括一项长期跟踪，以确定学生对生物信息学的信心和实际整合， 生物统计学和公平数据原理融入他们的研究，以及这门课程对他们职业生涯的影响 就业市场的轨迹和竞争力。总而言之，拟议的课程将提供一个独特的交叉- 培训，为受训中的生物医学研究科学家提供开始开发核心的教育机会 具备生物信息学和大数据集生物统计分析的能力。

PROJECT SUMMARY/ABSTRACT Modern biomedical research is increasingly quantitative. The next generation of researchers will need an entirely new set of quantitative skills to fully take advantage of the data they create. In response to this need, the goal of the current R25 proposal is to transform an existing, 5-day bioinformatics techniques course into a new, two-week short course, Reproducible and FAIR Bioinformatics Analysis of Omics Data, to provide a unique educational opportunity for biomedical research scientists-in-training to begin to develop core competencies in bioinformatics and biostatistical analyses of large datasets. The new course will also address NIH priorities including rigor and reproducibility and Findable, Accessible, Interoperable and Reusable (FAIR) data principles. During the last five years, the Dartmouth faculty who are serving as Principal Investigators have taught a 5-day course at the MDI Biological Laboratory on bioinformatics and biostatistics to ~40 trainees/year (202 total). Based on overwhelmingly positive feedback, the current project will extend this 5-day course into a longer, two-week short course at the MDI Biological Laboratory that will feature a low student-to- instructor ratio (5:1), more hands-on experiential learning, and exceptional faculty who are highly experienced in teaching and performing big-data analyses. The new course is designed to accommodate ~35 trainees per year (175 total over the five-year R25 project). It will incorporate modules on biostatistics, scientific rigor and reproducibility, and FAIR data principles. The course design will also involve a short conceptual presentation, followed by an exercise in which students will gain confidence by applying a new skill. Each active-learning session will have three levels of difficulty (beginner, intermediate, and advanced) to allow each student to progress at their own pace. The low student-to-faculty ratio will allow course facilitators to guide participants through realistic challenges without causing frustration. The specific aims of the proposed course include: Specific Aim 1. Develop a two-week short course primarily for postdoctoral fellows and graduate students that improves their ability to design and analyze omics experiments such as RNA-seq, 16S (microbiome), metagenomics, and sc-RNA-seq data; Specific Aim 2. Enhance the impact of research by biomedical scientists by teaching them the Responsible Conduct of Research, the secure and ethical use of data, as well as rigor and reproducibility and FAIR data principles; Specific Aim 3. Disseminate the training curriculum to a broad audience; and Specific Aim 4. Evaluate the short- and long-term impacts of the course on students, including a long-term follow-up to determine students’ confidence in and actual integration of bioinformatics, biostatistics, and FAIR data principles into their research, and the reported impact of this course on their career trajectory and competitiveness in the job market. In summary, the proposed course will provide a unique cross- training, educational opportunity for biomedical research scientists-in-training to begin to develop core competency in bioinformatics and biostatistical analyses of large data sets.