Information Retrieval for Large-scale Genomic Analysis

描述：(申请人提供)本项目支持继续 PI1的S跨学科培训于1999年9月开始，由斯隆/能源部 计算分子生物学院士。PI1的S眼前的职业生涯 目标是通过以下方式完成向计算分子生物学的过渡 在这个激动人心的领域打造一条独立的研究轨道。 华盛顿大学是一个独一无二的合适的地方 过渡：它有几个强大的生物程序和一台强大的计算机 科学系，他们之间通过积极的合作 合作、联合课程和研讨会。少年派是一名计算机科学家 培训，并根据初步调查确信她的专业知识 在设计中，可以利用有效的算法来解决 全球紧急需求引发的挑战性计算问题 分析大量的生物数据集，例如人类基因组。她 然而，对这一领域的长期兴趣在很大程度上是由诱人的 为真正的生物学知识做出贡献的愿景。为了实现这一愿景， 她计划获得实验技术方面的专业知识，这将使她 为了测试由计算分析产生的生物学假说，通过 广泛的实践养生法，密集的实验课程。具体研究 外研院建议探索的问题是将信息检索应用于 技术(已被广泛应用并在开发中获得巨大成功 用于万维网的搜索引擎)用于大规模基因组分析 序列和基因表达数据。她计划建立一个计算模型 共表达真核基因上游的未翻译启动子区域，通过 确定决定基因特异性的复合调控基序 表达模式。-这种复合基序可能由结合位点组成 协调控制表达的几个调控因子。这些 然后使用模型对未注释基因的启动子区域进行分类， 从而为它们的功能提供了一个假说。这项研究具有 有可能导致新的计算方法用于分类 特别是真核启动子区域，并用于功能基因组学 将军。通过计划的训练提供的强化训练 活动和拟议的研究将使PI1能够成功 完成向计算领域独立研究人员的过渡 分子生物学。

DESCRIPTION: (provided by applicant) This project supports the continuation of the PI1's interdisciplinary training started in September 1999 with a Sloan/DOE fellowship in Computational Molecular Biology. The PI1's immediate career objective is to complete a transition into computational molecular biology by forging an independent research trajectory within this exciting field. University of Washington is a uniquely suitable place for making such a transition: it has several strong biology programs as well as a strong computer science department, with considerable synergy among them through active collaborations, joint courses, and seminars. The PI its a computer scientist by training, and based on initial investigations is convinced that her expertise in designing efficient algorithms can be gainfully employed to address challenging computational problems arising from the emergent need for global analysis of massive biological datasets, for example, the human genome. Her long term interest in this field, however, is fueled largely by the tantalizing vision of contributing to real biological knowledge. To realize this vision, she plans to acquire expertise in experimental techniques that will allow her to test biological hypotheses arising from computational analyses, through an extensive regimen of hands-on, lab-intensive coursework. The specific research problem the FLPI proposes to explore is to apply information retrieval techniques (which have been widely applied with great success in developing search engines for the world-wide web) for large-scale analysis of genomic sequence and gene expression data. She plans to build computational models of the untranslated promoter regions upstream of coexpressed eukaryotic genes, by identifying composite regulatory motifs that determine the genes' specific expression pattern. -Such composite motifs may be composed of the binding sites of several regulatory factors that coordinately control expression. These models are then to be used to classify promoter regions of unannotated genes, thereby providing a hypothesis for their function. This research has the potential to lead to novel computational methods for classification of eukaryotic promoter regions in particular, and for functional genomics in general. The intensive training afforded through the planned training activities and the proposed research will enable the PI1 to successfully complete the transition into being an independent investigator in computational molecular biology.