CSHL Statistical Methods for Functional Genomics

功能基因组学的 CSHL 统计方法

• 批准号: 8551253
• 负责人: DAVID J. STEWART
• 金额: $ 10.57万
• 依托单位: COLD SPRING HARBOR LABORATORY
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-08-05 至 2017-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8551253
• 关键词: Address; Behavior; Bioconductor; Bioinformatics; Biological; Biological Assay; Biology; Cells; Competence; Complex; Computers; Copy Number Polymorphism; Data; Data Set; Development; Education; Educational Curriculum; Educational process of instructing; Ensure; Environment; Exercise; Explosion; Genome; 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; Techniques; Tissues; Training; Update; Work; biological research; chromatin immunoprecipitation; cost; functional genomics; genome-wide; graduate student; high throughput analysis; high throughput screening; high throughput technology; instructor; lectures; meetings; open source; programs; public health relevance; trend; Address; Behavior; Bioconductor; Bioinformatics; Biological; Biological Assay; Biology; Cells; Competence; Complex; Computers; Copy Number Polymorphism; Data; Data Set; Development; Education; Educational Curriculum; Educational process of instructing; Ensure; Environment; Exercise; Explosion; Genome; 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; Techniques; Tissues; Training; Update; Work; biological research; chromatin immunoprecipitation; cost; functional genomics; genome-wide; graduate student; high throughput analysis; high throughput screening; high throughput technology; instructor; lectures; meetings; open source; programs; public health relevance; trend

DESCRIPTION (provided by applicant): The proposed Cold Spring Harbor Laboratory (CSHL) summer course on Statistical Methods for Functional Genomics is to be held annually in 2013-2017. The primary objective of the course is to build competence in statistical methods for analyzing high-throughput data in genomics and molecular biology. In the past decade, high-throughput assays have become pervasive in biological research due to both rapid technological advances and decreases in overall cost. For example, many standard genomic measures such as methylation, copy-number variation, and chromatin immunoprecipitation have been adapted in recent years to high-throughput formats. This has produced an explosion of genome-scale data from multiple organisms, and investigators are now needed who have robust training in relevant statistical methods for analyzing such data. To make meaningful biological inferences from genome-wide data sets, it is essential that both experimental and computational biologists understand the fundamental statistical principles underlying analysis methods. 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 will involve detailed lectures and invited presentations as well as hands-on computer tutorials. 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.

描述（由申请人提供）：拟议的Cold Spring Harbour实验室（CSHL）关于功能基因组学统计方法的夏季课程将于2013 - 2017年每年举行。该课程的主要目的是在统计方法中建立能力，以分析基因组学和分子生物学中的高通量数据。在过去的十年中，由于技术进步和总体成本降低，高通量测定在生物学研究中已普遍存在。例如，近年来，许多标准的基因组措施，例如甲基化，拷贝数变异和染色质免疫沉淀已被调整为高通量格式。这导致了来自多个生物体的基因组规模数据的爆炸，现在需要研究人员在相关的统计方法中进行了强有力的培训以分析此类数据。为了根据全基因组数据集做出有意义的生物学推论，实验生物学家和计算生物学家都必须了解基本统计原理的基本分析方法，这一点至关重要。 CSHL建议通过继续提供每年夏天的高级两周课程，标题为功能基因组学统计方法，以满足对这种专业的跨学科培训的需求。本课程将提供深入的动手培训，使参与者为启动大型生物学数据集的分析做好准备。此外，该课程将解决所有高通量技术常见的问题，例如识别和补偿系统错误，全基因组规模上的统计显着性，并将生物信息学数据纳入统计程序。将使用R环境进行统计计算以及BioConductor进行课堂练习和演示，这是一个用于生物信息学研究的R中的开源项目。该课程将涉及详细的讲座和邀请的演讲以及动手的计算机教程。该课程讲师将成为完全活跃并为复杂生物数据集的分析做出重大贡献的研究人员，讲师将 由一系列受邀演讲者补充，他们将在其专业知识领域提出当前的研究，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，从高级研究生到高级调查员。具有多种科学背景的科学家预期的应用，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，功能基因组学统计方法的总体目标是提供高级方法论的住宅培训，参与者可以立即将其应用于自己的研究。