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.

项目摘要/摘要 拟议的冷春港实验室（CSHL）夏季课程有关统计方法 功能基因组学应每年在2021 - 2024年举行。该课程的主要目的是建造 用于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 快速技术进步和总体成本下降。许多标准基因组测量 作为甲基化，拷贝数变化和染色质免疫沉淀已在最近改编 高通量格式的年份，这产生了来自多个基因组规模数据的爆炸 有机体。现在需要调查人员在相关的统计方法中接受强大的培训 分析此类数据。 CSHL提议满足对这种专业的跨学科培训的需求 每年夏天继续提供一门高级两周的课程，标题为功能的统计方法 基因组学。本课程将提供深入的动手培训，将使参与者启动 大而复杂的生物学数据集的分析。此外，课程将解决问题 所有高通量技术的共同点，例如识别和补偿系统错误， 在全基因组量表上的静态意义，并将生物信息学数据编码为统计 程序。课堂练习和演示将使用统计的R环境进行 计算和生物通用器，这是一个用于生物信息学研究的R开放源项目。 课程讲师将成为充分活跃并做出重大活跃的研究人员 对复杂生物数据集的分析的贡献，教师将补充 一系列受邀演讲者将在其专业领域介绍当前的研究以说明 课程中教授的原则。该课程每年将培训大约24名学生 高级研究生到高级调查员。申请是从具有的科学家那里 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， 功能基因组学的统计方法是为居民提供高级方法的培训 参与者可以立即申请自己的研究。