Statistical Genomics and Systems Biology Workshop

统计基因组学和系统生物学研讨会

• 批准号: 8414735
• 负责人: Steve Horvath
• 金额: $ 18.08万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-05-01 至 2017-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8414735
• 关键词: Address; Bioinformatics; Biological; Biology; Biomedical Research; California; Clinical Research; Collaborations; Complex; Computational Biology; Computer software; Computers; Data; Data Analyses; Data Set; Diagnosis; Disease; Disease model; Distance Learning; Educational process of instructing; Educational workshop; Environment; Exercise; Experimental Genetics; Feedback; Funding; Gene Expression; Generations; Genes; Genetic; Genetic Variation; Genomics; Goals; Grant; Human Genome Project; Imagery; Instruction; Interdisciplinary Study; Internet; Knowledge; Learning; Life; Los Angeles; Mathematics; Meta-Analysis; Methods; Mining; Modeling; Monitor; Network-based; Neurosciences; Ontology; Participant; Pathway Analysis; Pathway interactions; Phenotype; Physics; Proteins; Public Health; Research Personnel; Resources; SNP genotyping; Scientist; Simulate; Slide; Social Sciences; Software Tools; Solutions; Students; Surveys; System; Systems Biology; Teaching Method; Testing; Time; Training; Transcript; Translational Research; United States National Institutes of Health; Universities; Variant; Vocabulary; Weight; analytical method; base; computer based statistical methods; computer science; data sharing; deep sequencing; density; design; experience; gene discovery; genetic analysis; graduate student; human disease; innovation; instructor; interest; lectures; neuropsychiatry; next generation; programs; public health relevance; satisfaction; statistics; success; theories; tool; trait; transcriptome sequencing

DESCRIPTION (provided by applicant): The human genome project and its offshoots have dramatically increased the amount of genetic data available to researchers. Interdisciplinary studies that bring together biologists and quantitative scientists are critical to mine the vast amount of public data and understand the genetic basis of human disease and variation. New quantitative methods have revolutionized the analysis of high dimensional genomic data sets. We have over 18 years of NIH-funded experience developing these leading-edge methods, and over ten years experience teaching these methods to genetic researchers in intensive summer workshops. Our continuing goal is to create tools and workshops to train the next generation of genomic scientists to mine, analyze, and share data, which will advance the understanding, diagnosis, and treatment of disease, and promote public health. As part of this educational endeavor, we propose to offer an intensive week-long workshop in the theory and practice of statistical genomics and systems biology (with an emphasis on network methods). The participants will spend much of their time performing hands-on exercises with widely used, state-of-the-art applications our group has developed for discovering genes that influence disease traits. We will use real and simulated data sets that will journey from classic linkage and association tests to the analysis of high-density SNP genotypes, RNA-seq transcript counts, rare variants from deep sequencing, and network analysis methods,. The participants will range from graduate students to senior researchers, both quantitative and non-quantitative, and from many fields, including biology, genetics, clinical and biomedical research, interdisciplinary and translational sciences, bioinformatics, statistics, computational biology, computer science, applied mathematics, physics, and social science. We plan to bring together quantitative, biological, and interdisciplinary researchers and give them an understanding of the issues and the vocabulary of quantitative genomics so that they can communicate and collaborate effectively and productively. Without understanding and close collaboration between quantitative and qualitative scientists, solutions to the challenges of modern genomics will elude us. The workshops will be offered on the campus of the University of California, Los Angeles, and all materials will also be offered remotely, through our Distance Learning Initiative. In successive years the emphasis will alternate between statistical genomics and systems biology, but each year both topics will be addressed. The focal topic each year will be covered in an intensive five-day course, with computer exercises. The secondary topic will be covered in an optional two-day module either before or after the primary topic. All course materials, plus additional resources, will be available throughout the year to course alumni and any interested researcher. Feedback and interaction with the course instructors will also be available throughout the year in the Distance Learning environment.

描述（由申请人提供）：人类基因组计划及其分支极大地增加了研究人员可用的遗传数据量。将生物学家和定量科学家聚集在一起的跨学科研究对于挖掘大量公共数据和了解人类疾病和变异的遗传基础至关重要。新的定量方法彻底改变了高维基因组数据集的分析。我们在 NIH 资助的开发这些前沿方法方面拥有超过 18 年的经验，并且在夏季密集研讨会上向遗传研究人员教授这些方法也有超过 10 年的经验。我们的持续目标是创建工具和研讨会来培训下一代基因组科学家挖掘、分析和共享数据，这将促进对疾病的理解、诊断和治疗，并促进公共卫生。作为这项教育工作的一部分，我们建议提供为期一周的密集研讨会，内容涉及统计基因组学和系统生物学的理论和实践（重点是网络方法）。参与者将花费大量时间使用我们小组开发的广泛使用的最先进的应用程序进行实践练习，以发现影响疾病特征的基因。我们将使用真实和模拟的数据集，这些数据集将从经典的链接和 关联测试用于分析高密度 SNP 基因型、RNA-seq 转录本计数、深度测序的罕见变异和网络分析方法。参与者范围从研究生到高级研究人员，包括定量和非定量的，并且来自许多领域，包括生物学、遗传学、临床和生物医学研究、跨学科和转化科学、生物信息学、统计学、计算生物学、计算机科学、应用数学、物理学和社会科学。我们计划将定量、生物学和跨学科研究人员聚集在一起，让他们了解定量基因组学的问题和词汇，以便他们能够有效且富有成效地进行交流和协作。如果定量和定性科学家之间没有理解和密切合作，现代基因组学挑战的解决方案将无法解决。研讨会将在加州大学洛杉矶分校校园内举行，所有材料也将通过我们的远程学习计划远程提供。在接下来的几年中，重点将在统计基因组学和系统生物学之间交替，但每年都会讨论这两个主题。每年的焦点主题将通过为期五天的强化课程进行涵盖，并配有计算机练习。次要主题将在主要主题之前或之后在可选的为期两天的模块中进行介绍。所有课程材料以及其他资源将全年提供给课程校友和任何感兴趣的研究人员。全年都可以在远程学习环境中获得反馈以及与课程讲师的互动。