Computational Methods for Functional Genomics

功能基因组学的计算方法

基本信息

批准号：
6906407
负责人：
Simon Tavare
金额：
$ 40.63万
依托单位：
UNIVERSITY OF SOUTHERN CALIFORNIA
依托单位国家：
美国
项目类别：
财政年份：
2002
资助国家：
美国
起止时间：
2002-07-15 至 2007-06-30
项目状态：
已结题

项目摘要

DESCRIPTION (provided by applicant): This proposal focuses on the development of statistical and computational methods for the analysis of data coming from high-throughput functional genomics technology such as microarrays. The research involves the extraction of meaningful relative expression levels from such experiments, and the subsequent identification of DNA sequence features such as promoters and transcription factor binding sites shared by genes identified from the arrays. This research involves the close collaboration of computational scientists and laboratory scientists. The projects involve studies of aging (in nematode, fruit fly and man), RNA structure prediction, identification of features of DNA replication in yeast, sensitivity to radiation therapy in human cancer patients, hybrid vigor in the development of oysters, and the evolution of E. coll. The common themes are the acquisition, analysis and interpretation of large amounts of data generated through the use of various microarray technologies. New approaches to research and training are needed for studying problems in genomics and molecular biology. The traditional one-person or student-advisor study is unlikely to be successful. Instead a multidisciplinary approach must be devised, with mathematical scientists involved with experimentalists to understand the motivations for the experiments and the limitations of the studies. Post-doctoral and graduate students from mathematics and statistics must learn the skills of cross-disciplinary communication and collaboration. This research program is specifically structured to provide practical and continuing training in this process.

描述（由申请人提供）：该提案侧重于开发统计和计算方法，用于分析来自高通量功能基因组技术（例如微阵列）的数据。该研究涉及从此类实验中提取有意义的相对表达水平，以及随后识别 DNA 序列特征，例如从阵列中识别的基因共享的启动子和转录因子结合位点。这项研究涉及计算科学家和实验室科学家的密切合作。这些项目涉及衰老研究（线虫、果蝇和人类）、RNA 结构预测、酵母 DNA 复制特征的识别、人类癌症患者对放射治疗的敏感性、牡蛎发育中的杂交活力以及E.科尔。共同的主题是采集、分析和解释通过使用各种微阵列技术产生的大量数据。研究基因组学和分子生物学问题需要新的研究和培训方法。传统的一人或学生顾问研究不太可能成功。相反，必须设计一种多学科方法，让数学科学家与实验学家一起了解实验的动机和研究的局限性。数学和统计学的博士后和研究生必须学习跨学科沟通和协作的技能。该研究计划经过专门设计，旨在提供此过程中的实用和持续培训。