CSHL Statistical Methods for Functional Genomics Course

项目摘要/摘要 拟议的冷泉港实验室(CSHL)关于统计方法的夏季课程 功能基因组学将于2021-2024年每年举行一次。本课程的主要目标是建立 具备分析基因组学和分子生物学高通量数据的统计方法的能力。 在过去的二十年里，高通量分析已经在生物学研究中变得普遍，因为 既有快速的技术进步，又有总成本的下降。许多标准的基因组测量方法如 由于甲基化、拷贝数变异和染色质免疫沉淀在最近几年中已被采用 几年到高通量格式，这产生了基因组规模的数据从 有机体。现在需要的是在相关统计方法方面有扎实培训的调查人员 分析这些数据。CSHL建议通过以下方式满足对这种专门的跨学科培训的需求 每年夏天继续提供为期两周的高级课程，题为泛函的统计方法 基因组学。本课程将提供强化的实际操作培训，帮助学员做好启动 分析大型和复杂的生物数据集。此外，课程还将涉及一些问题。 对于所有高通量技术都是通用的，例如识别和补偿系统误差， 在全基因组范围内的统计学意义，并将生物信息学数据纳入统计学 程序。课堂练习和演示将使用R环境进行统计 计算以及生物导体，这是R的一个用于生物信息学研究的开源项目。这个 课程讲师将是充分活跃于并取得重大进展的知名研究人员 对复杂生物数据集的分析做出贡献，讲师将补充 一系列受邀演讲者，他们将介绍各自专业领域的最新研究，以说明 这门课所教授的原则。该课程每年将培训约24名学生，范围从 高级研究生到高级调查员。预计科学家将提出申请，他们将拥有 各种科学背景，包括分子进化、发育、神经科学、癌症、 植物生物学和免疫学。与其他CSHL研究生课程一样，总体目标是 功能基因组学的统计方法是提供高级方法学方面的住院医师培训 参与者可以立即应用于他们自己的研究。

PROJECT SUMMARY/ABSTRACT The proposed Cold Spring Harbor Laboratory (CSHL) summer course on Statistical Methods for Functional Genomics is to be held annually in 2021-2024. The primary objective of the course is to build competence in statistical methods for analyzing high‐throughput data in genomics and molecular biology. Over the past two decades, high‐throughput assays have become pervasive in biological research due to both rapid technological advances and decreases in overall cost. Many standard genomic measures such as methylation, copy-number variation, and chromatin immunoprecipitation have been adapted in recent years to high-throughput formats, and this has produced an explosion of genome-scale data from multiple organisms. Investigators are now needed who have robust training in relevant statistical methods for analyzing such data. CSHL proposes to meet the need for this specialized, interdisciplinary training by continuing to offer an advanced two-week course each summer entitled Statistical Methods for Functional Genomics. This course will provide intensive, hands-on training that will prepare participants to initiate analyses of large and complex biological data sets. In addition, the curriculum will address issues common to all high-throughput technologies, such as identifying and compensating for systematic errors, statistical significance on a genome-wide scale, and incorporating bioinformatics data into statistical procedures. In-class exercises and demonstrations will be done using the R environment for statistical computing as well as Bioconductor, an open‐source project in R for use in bioinformatics research. The course instructors will be established researchers who are fully active in and have made significant contributions to the analysis of complex biological data sets, and the instructors will be supplemented by a series of invited speakers who will present current research in their fields of expertise to illustrate principles taught in the course. The course will train approximately 24 students per year, ranging from advanced graduate students to senior investigators. Applications are anticipated from scientists with a variety of scientific backgrounds, including molecular evolution, development, neuroscience, cancer, plant biology, and immunology. As with other CSHL postgraduate courses, the overarching goal of Statistical Methods for Functional Genomics is to provide residential training in advanced methodologies that participants can apply immediately to their own research.