Collaborative Research: Interpolated Markov Models for DNA Sequence Analysis

合作研究：用于 DNA 序列分析的插值马尔可夫模型

• 批准号: 9902923
• 负责人: Steven Salzberg
• 金额: $ 33万
• 依托单位: Institute for Genomic Research
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1999
• 资助国家: 美国
• 起止时间: 1999-09-01 至 2002-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9902923&HistoricalAwards=false
• 关键词: Collaborative; Research; Interpolated; Markov; Models

This is a collaborative research project of Arthur Delcher at Loyola College at Maryland (RUI award IIS-9820497) and Steven Salzberg at the Institute for Genomic Research (award IIS-9902923). The quantity of DNA sequence data is exploding, and with this growth comes a parallel growth in the demand for computational methods for sequence analysis. This abundance of data demands new and highly accurate computational analysis tools to explore these sequences and maximize the scientific knowledge gained from them. This interdisciplinary project combines the latest techniques in data modeling and data mining with knowledge of molecular biology to find genes and other scientifically significant patterns in DNA sequence databases. The Glimmer system uses Interpolated Markov Models (IMMs) to finding genes in microbial genomes. This tremendously successful system has found over 98% of the genes in the bacteria that cause Lyme disease, syphilis, tuberculosis, and others. This project is developing new systems to find genes in the much more complex DNA of higher organisms, and at the same time advancing the state of the art for the underlying IMM technology. Specific foci of the project include: developing improved probabilistic models for gene finding; developing algorithmic methods for identifying laterally transferred genes and other unusual DNA sequences; and developing IMMs and other probabilistic techniques for analyzing DNA in higher organisms including plants and mammals. This project will dramatically improve our ability to discover genes and other biologically significant features in a wide range of organisms, leading to the development of new antibiotics and vaccines and to a better understanding of how DNA directs the functions of a living cell. The software will be freely disseminated in the genomics community.http://www.tigr.org/softlab/glimmer/glimmer.html

这是马里兰州洛约拉学院的Arthur Delcher （RUI奖IIS-9820497）和基因组研究所的Steven Salzberg（奖IIS-9902923）的合作研究项目。DNA序列数据的数量呈爆炸式增长，随着这种增长，对序列分析计算方法的需求也在平行增长。这些丰富的数据需要新的和高度精确的计算分析工具来探索这些序列，并最大限度地从中获得科学知识。这个跨学科项目将数据建模和数据挖掘的最新技术与分子生物学知识结合起来，在DNA序列数据库中寻找基因和其他科学上重要的模式。Glimmer系统使用插值马尔可夫模型（IMMs）在微生物基因组中寻找基因。这个非常成功的系统已经在导致莱姆病、梅毒、结核病和其他疾病的细菌中发现了98%以上的基因。该项目正在开发新的系统，以在更复杂的高等生物DNA中寻找基因，同时推进基础IMM技术的最新发展。该项目的具体重点包括：开发改进的基因发现概率模型；开发识别横向转移基因和其他不寻常DNA序列的算法方法；发展imm和其他概率技术来分析包括植物和哺乳动物在内的高等生物的DNA。这个项目将极大地提高我们在广泛的生物体中发现基因和其他重要生物学特征的能力，从而开发新的抗生素和疫苗，并更好地了解DNA如何指导活细胞的功能。该软件将在基因组学社区免费传播。http://www.tigr.org/softlab/glimmer/glimmer.html