Statistical Approaches to the Analysis of Genome-wide Measurements: Significance of Periodic Gene Expression and Correlations of Gene Content in Completely Sequenced Genomes

全基因组测量分析的统计方法：完全测序的基因组中周期性基因表达的意义和基因内容的相关性

• 批准号: 0426148
• 负责人: Jie Chen
• 金额: $ 9.05万
• 依托单位: University of Missouri-Kansas City
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-12-15 至 2006-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0426148&HistoricalAwards=false
• 关键词: Statistical; Approaches; Analysis; Genome; wide

The goal of this project is to advance interdisciplinary research between mathematical sciences and modern biological sciences. The specific aims of this project are to: (1) expand the PI's skills and knowledge in molecular biology and bioinformatics, (2) pursue projects at the intersection of molecular biology and statistics, and (3) develop new statistical modeling techniques or improve the existing statistical methods for analyzing genetic data sets. Research activities will include: developing statistical methods to identify statistically significant periodically expressed genes in the cell cycle expression data sets with controllable false discovery rate (FDR); establishing an optimal clustering strategy that produces biologically meaningful clusters for gene expression data sets; and investigating the canonical correlation between gene content vectors so that optimal relations between genes or protein vectors can be revealed. The Bioinformatics Center at the Stowers Institute for Medical Research in Kansas City, Missouri will host the PI for this project. Outcomes of the project may have profound impact in promoting teaching, training, and learning at the intersection of statistics, molecular biology, and bioinformatics. Bringing together knowledge and expertise across disciplines will generate new knowledge and understanding in statistical bioinformatics. Moreover, such newly acquired knowledge will enrich the educational experiences and enhance career options for students at the PI's home institution. Thus, this project will leverage cross-discipline research collaboration and advanced scientific educational opportunity to advance scientific knowledge.This IGMS project is jointly supported by the MPS Office of Multidisciplinary Activities (OMA) and the Division of Mathematical Sciences (DMS).

该项目的目标是促进数学科学与现代生物科学之间的跨学科研究。该项目的具体目标是：(1)扩展PI在分子生物学和生物信息学方面的技能和知识；(2)追求分子生物学和统计学交叉的项目；(3)开发新的统计建模技术或改进现有的遗传数据集分析统计方法。研究活动将包括：开发统计方法，以可控的错误发现率（FDR）识别细胞周期表达数据集中具有统计意义的周期性表达基因；建立最优聚类策略，为基因表达数据集产生具有生物学意义的聚类；研究基因含量载体之间的典型相关性，从而揭示基因或蛋白质载体之间的最佳关系。密苏里州堪萨斯城斯托斯医学研究所的生物信息学中心将主持这个项目的项目负责人。该项目的成果可能对促进统计学、分子生物学和生物信息学交叉学科的教学、培训和学习产生深远影响。汇集跨学科的知识和专业知识将产生统计生物信息学的新知识和理解。此外，这些新获得的知识将丰富学生在PI所在机构的教育经历，并为学生提供更多的职业选择。因此，该项目将利用跨学科的研究合作和先进的科学教育机会来推进科学知识。该IGMS项目由MPS多学科活动办公室（OMA）和数学科学司（DMS）联合支持。