CF Bioinformatics and Biostatistics Core

该应用程序的首要目标是继续发展达特茅斯囊性纤维化研究 中心(DartCF)，以我们在CF相关研究方面的强大历史为基础，并将我们的能力扩展到 研究肠道内宿主与微生物的相互作用及其对肠道生态失调和整体系统健康的影响 与CF相关的糖尿病患者的比例。囊性纤维化生物信息学的主要目标和 生物统计学核心(CF-BBC)是提供专家级生物信息学和生物统计学的支持和培训。 最先进的生物信息学和生物统计学在一般的生物医学研究中已经变得必不可少，并且 Cf尤其是研究，因为日益复杂的实验设计和庞大的数据集 产生于基因组、转录组、蛋白质组和相关的高维技术。我们发现，CF 受过生物信息学和生物统计学培训的研究人员比工作人员更有能力分析自己的数据 在通用数据分析核心设施中，CF研究人员在他们的 菲尔德。因此，投资生物信息学和生物统计培训是有效和高效地应对 与CF相关的科学数据数量迅速增加。我们已经成功地培养了四名CF科学家 现在作为教师参加了我们的教育计划，还有数十人报告说他们 与CF研究人员非正式地分享他们在生物信息学和生物统计学方面的培训。重要的是 我们举办了两个强化课程，提供应用程序的技术和统计基础方面的培训 生物信息学，我们的教学系统包括每周会议(R Club)和研讨会，在这些研讨会上，学生 教职员工使用统计、图表和信息学工具展示他们的经验。这些每周的会议 部分是由CF研究社区的参与者设计的，使我们的教育议程保持同步 满足不断变化的研究需求。我们提供生物信息学支持的模式也是创新的，因为 BBC生物信息学和生物统计学专家都是训练有素的CF研究人员。这大大改善了 交流，使核心在实验设计、理解生物学等领域更有价值 结果的意义，建议后续实验，撰写稿件和与审稿人打交道 评论。最后，CF-BBC在其对开放科学的承诺方面是创新的。我们的核心帮助DartCF 社区格式化和注释数据，并将其存储在适当的数据库中，使我们的数据更容易找到， 其他人可访问、可互操作和可重用(公平)。我们还帮助DartCF研究人员查找、下载和 重新分析与他们的研究相关的公共数据集，使他们能够生成新的假设并确保 这一初步发现是可以重现的。总而言之，CF-BBC的使命是提供专家级的 为DartCF教职员工和学员以及我们的合作伙伴提供生物信息学和生物统计学支持和培训 机构，并支持和加强开放科学。

The overarching goal of this application is to continue to develop the Dartmouth Cystic Fibrosis Research Center (DartCF), building on our strong history of CF-related research and expanding our capability to investigate host-microbe interactions in the gut, and their impact on gut dysbiosis and overall systemic health of patients with CF-related diabetes. The primary objective of the Cystic Fibrosis Bioinformatics and Biostatistics Core (CF-BBC) is to provide expert-level bioinformatics and biostatistical support and training. State-of-the-art bioinformatics and biostatistics have become essential in biomedical research in general, and CF research in particular, because of increasingly complex experimental designs and the large data sets that arise from genomic, transcriptomic, proteomic and related high-dimension techniques. We have found that CF researchers trained in bioinformatics and biostatistics are better positioned to analyze their own data than staff at generic data analysis Core facilities, and that CF researchers share what they’ve learned with others in their field. Therefore, investing in bioinformatics and biostatistics training is an effective and efficient response to rapidly increasing amounts of CF related scientific data. We have successfully trained four CF scientists who are now participating as instructors in our educational programs, and dozens more who report that they are sharing their training in bioinformatics and biostatistics with CF researchers on an informal basis. Importantly, we host two intensive courses to provide training in the technical and statistical foundations of applied bioinformatics, and our instructional system includes weekly meetings (R Club) and seminars at which students and faculty present their experience using statistical, graphical, and informatics tools. These weekly meetings are designed in part by participants from the CF research community, keeping our educational agenda in sync with evolving research needs. Our model of providing bioinformatics support is also innovative because CF- BBC bioinformatics and biostatistics specialists are trained CF researchers. This greatly improves communication and makes the Core more valuable in areas like experimental design, understanding biological meaning of results, suggesting follow up experiments, writing manuscripts and dealing with reviewer comments. Finally, the CF-BBC is innovative in its commitment to Open Science. Our Core helps the DartCF community format and annotate data, and deposit it in appropriate databases, making our data more findable, accessible, interoperable and reusable (FAIR) by others. We also help DartCF researchers find, download and reanalyze public data sets relevant to their research, allowing them to generate new hypotheses and ensure that preliminary findings are reproducible. In summary, the mission of the CF-BBC is to provide expert-level bioinformatics and biostatistical support and training to DartCF faculty and trainees, and our partnering institutions, and to support and enhance Open Science.