CSHL Statistical Methods for Functional Genomics Course

CSHL 功能基因组学统计方法课程

• 批准号: 10482328
• 负责人: Charla A Lambert
• 金额: $ 9.21万
• 依托单位: COLD SPRING HARBOR LABORATORY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-07 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10482328
• 关键词: Address; Behavior; Bioconductor; Bioinformatics; Biological; Biological Assay; Biology; Cells; Collaborations; Competence; Complex; Copy Number Polymorphism; Course Content; Data; Data Analyses; Data Set; Development; Educational Curriculum; Ensure; Environment; Exercise; Explosion; Future; Genomics; Goals; Immunology; Inherited; Laboratories; Learning; Malignant Neoplasms; Measurement; Measures; Methodology; Methods; Methylation; Molecular Biology; Molecular Evolution; Mutation; Neurosciences; Organism; Participant; Plants; Postdoctoral Fellow; Procedures; Research; Research Personnel; Scientist; Series; Statistical Computing; Statistical Data Interpretation; Statistical Methods; Students; Talents; Techniques; Tissues; Training; Update; behavioral study; biological research; career networking; chromatin immunoprecipitation; cost; education research; educational atmosphere; forging; formal learning; functional genomics; genome-wide; genomic data; graduate student; high throughput analysis; high throughput screening; high throughput technology; instructor; laboratory curriculum; large datasets; open source; programs; trend; work-study

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.

项目概要/摘要 拟议的冷泉港实验室（CSHL）统计方法暑期课程 功能基因组学将于 2021-2024 年每年举办一次。该课程的主要目标是建立。 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。 在过去的二十年中，由于以下原因，高通量测定在生物学研究中变得普遍： 技术的快速进步和总体成本的降低，许多标准基因组测量方法都如此。 由于甲基化、拷贝数变异和染色质免疫沉淀最近已被采用 数年的时间转变为高通量格式，这导致了来自多个国家的基因组规模数据的爆炸式增长。 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。 CSHL 建议通过分析此类数据来满足这种专业化、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“函数统计方法” 基因组学本课程将提供密集的实践培训，为入门做好准备。 此外，课程还将解决大型和复杂的生物数据集的分析问题。 所有高通量技术共有的，例如识别和补偿系统错误， 全基因组范围内的统计显着性，并将生物信息学数据纳入统计 课堂练习和演示将使用 R 环境进行统计。 计算以及 Bioconductor（一个用于生物信息学研究的 R 开源项目）。 课程讲师将是充分活跃并取得重大成果的研究人员 对复杂生物数据集分析的贡献，讲师将得到补充 一系列受邀演讲者将介绍其专业领域的当前研究成果以说明 该课程每年将培训大约 24 名学生，范围从 高级研究生到高级研究人员的申请预计来自具有博士学位的科学家。 各种科学背景，包括分子进化、发育、神经科学、癌症、 与其他 CSHL 研究生课程一样，其总体目标是植物生物学和免疫学。 功能基因组学统计方法旨在提供高级方法学的住院培训 参与者可以立即应用于自己的研究。