KDI: Intelligent Computational Genomic Analysis

KDI：智能计算基因组分析

• 批准号: 9980088
• 负责人: Simon Kasif
• 金额: $ 170万
• 依托单位: University of Illinois at Chicago
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1999
• 资助国家: 美国
• 起止时间: 1999-09-15 至 2001-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9980088&HistoricalAwards=false
• 关键词: KDI; Intelligent; Computational; Genomic; Analysis

In the twenty-first century, biology will increasingly become an information science. Recent startling advances in DNA sequencing technology have spurred dozens of major sequencing projects, ranging from important bacterial genomes to the Human Genome Project. In between are projects to sequence the genomes of laboratory animals such as mice, plants such as the flowering plant Arabidopsis and various important food crops. Enormous increases in the amount of available sequence data have produced a strong demand for computational high throughput analysis. Genomic sequence data will provide the basis for many important discoveries as we move into the next century, and new algorithms are vital to maximizing the scientific knowledge gleaned from this data. The fact that biological data is distributed all over the world presents additional problems for algorithms that need to be able to use all available knowledge sources to construct consistent theories. The computer scientists working on this project have expertise in machine learning, computational biology, probabilistic reasoning, scientific databases and high performance computing. The biologists include several key investigators who have sequenced and analyzed eight completed bacterial genomes. Their expertise covers DNA sequencing technology, sequence databases, molecular biology, genetics and biophysics. This project will develop novel computer systems to support scientific discovery in large-scale genomic sequence databases. These systems will make sophisticated computational analysis an integral part of the process of developing, integrating and disseminating new scientific knowledge. The results will enable new discoveries from the rich source of information embodied in genomic databases.

在21世纪的世纪，生物学将日益成为一门信息科学。 DNA测序技术的最新惊人进展刺激了数十个重大测序项目，从重要的细菌基因组到人类基因组计划。 介于两者之间的项目是对实验室动物（如小鼠）、植物（如开花植物拟南芥）和各种重要粮食作物的基因组进行测序。 可用序列数据量的巨大增加产生了对计算高通量分析的强烈需求。 当我们进入下一个世纪时，基因组序列数据将为许多重要发现提供基础，而新的算法对于最大限度地利用从这些数据中收集的科学知识至关重要。生物数据分布在世界各地的事实给算法带来了额外的问题，这些算法需要能够使用所有可用的知识源来构建一致的理论。 参与该项目的计算机科学家拥有机器学习、计算生物学、概率推理、科学数据库和高性能计算方面的专业知识。 这些生物学家包括几位关键的研究人员，他们已经对八个完整的细菌基因组进行了测序和分析。 他们的专业知识涵盖DNA测序技术、序列数据库、分子生物学、遗传学和生物物理学。 该项目将开发新的计算机系统，以支持大规模基因组序列数据库中的科学发现。这些系统将使复杂的计算分析成为发展、综合和传播新科学知识过程的一个组成部分。 这些结果将使人们能够从基因组数据库中包含的丰富信息源中发现新的发现。