CSHL Statistical Methods for Functional Genomics Course

项目概要/摘要 拟议中的冷泉港实验室（CSHL）夏季统计方法课程， Functional Genomics将于2021-2024年每年举办一次。本课程的主要目标是建立 在基因组学和分子生物学的高通量数据分析的统计方法的能力。 在过去的二十年中，高通量测定在生物学研究中已经变得普遍，这是由于 技术的快速进步和整体成本的下降。许多标准的基因组测量， 由于甲基化、拷贝数变异和染色质免疫沉淀在最近已经被采用， 多年来，高通量格式，这已经产生了爆炸性的基因组规模的数据，从多个 有机体现在需要在相关统计方法方面接受过严格培训的研究人员， 分析这些数据。CSHL建议通过以下方式满足对这种专业化、跨学科培训的需求： 继续提供为期两周的高级课程，每年夏天题为统计方法的功能 基因组学。本课程将提供强化的实践培训，使参与者能够开始 分析大型且复杂的生物数据集。此外，课程将解决问题， 这是所有高通量技术所共有的，例如识别和补偿系统误差， 在全基因组范围内的统计意义，并将生物信息学数据纳入统计 程序.课堂练习和演示将使用R环境进行统计 Bioconductor是一个R语言的开源项目，用于生物信息学研究。的 课程讲师将建立研究人员谁是充分积极参与，并取得了显着 对复杂生物数据集分析的贡献，教师将得到以下补充： 一系列受邀演讲者将介绍其专业领域的最新研究， 在课程中讲授的原则。该课程每年将培训约24名学生， 高级研究生到高级研究员。预计科学家们的应用程序具有 各种科学背景，包括分子进化，发展，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， 功能基因组学的统计方法是提供先进方法的住宿培训 参与者可以立即应用于自己的研究。

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.