Computational Analysis of Short Repetitive Motifs in DNA Sequences

DNA 序列中短重复基序的计算分析

• 批准号: 7196374
• 负责人: Nikola Stojanovic
• 金额: $ 7.4万
• 依托单位: UNIVERSITY OF TEXAS ARLINGTON
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-05-01 至 2009-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7196374
• 关键词: Address; Adopted; Algorithms; Attention; Binding; Binding Sites; Bioinformatics; Biology; Chemistry; Chromosomes; Communities; Computer Analysis; Computer software; Condition; Consensus Sequence; DNA; DNA Sequence; Data; Databases; Detection; Disease; Elements; Exhibits; Family; Gene Expression Regulation; Genes; Genome; Genomic Segment; Goals; Human; Imagery; Individual; Length; Measures; Methods; Modeling; Numbers; Phylogenetic Analysis; Play; Process; Promoter Regions; Public Domains; Publishing; Purpose; Relative (related person); Research Personnel; Seeds; Sequence Homologs; Signal Transduction; Site; Software Tools; Staging; Statistically Significant; base; concept; improved; interest; mammalian genome; novel; programs; promoter; software development; therapy development; tool; transcription factor

DESCRIPTION (provided by applicant): Computational analysis of various aspects of gene regulation, transcription factor binding in particular, is an important and well known problem. Adequately addressed, it would greatly improve our understanding of diseases and help with the development of treatments. However, despite the intensive efforts and the application of sophisticated models, the identification of the binding motifs in DMA sequences remains elusive. The raw sequence likely carries only a part of the regulatory signal, and it is often too short and subtle to be detected even by the most sensitive algorithms. Many software tools developed for this purpose exploit the clustering and over-representation of motifs in promoter regions, sometimes combining this method with other experimental or phylogenetic information. However, the fact that many short sequences appear to be over-represented in any segment of DNA, at least in comparison with completely random model, impedes the reliable discovery. This proposal seeks support to develop new software and apply it to the identification, visualization and analysis of repeated short (approximately 5-25 bases) degenerate motifs, in short (a few hundred bases) and long (entire chromosomes) DNA sequences. We intend to use this software on the human and other genomes, as well as on sequences of interest to our collaborators in biology and chemistry, in an attempt to systematically characterize short over-represented sequences. We shall determine which of these motifs correspond to the experimentally confirmed transcription factor binding consensuses, study their phylogenetic conservation and investigate their possible association with repeat families. Special attention will be paid to the upstream sequences of genes, and tools will be developed for a genome-wide search for related motif layouts. Our software will be based on an adaptation of classic string processing algorithms to address the inexact matches in a novel way, by combining the seed elements into statistically significant degenerate motifs. In addition to performing analysis with our collaborators, we will place the programs in the public domain, along with the other tools which we have already developed and published, inviting other investigators to use them on their own data.

描述(由申请人提供)： 计算分析基因调控的各个方面，特别是转录因子结合，是一个重要的和众所周知的问题。如果处理得当，它将极大地提高我们对疾病的了解，并有助于开发治疗方法。然而，尽管进行了大量的努力并应用了复杂的模型，但对DMA序列中的结合基序的识别仍然难以捉摸。原始序列可能只携带部分调控信号，而且它往往太短太微妙，即使是最敏感的算法也无法检测到。为此目的开发的许多软件工具利用启动子区域中基序的聚集和过度表达，有时将这种方法与其他实验或系统发育信息结合起来。然而，至少与完全随机的模型相比，许多短序列似乎在DNA的任何片段中过度表达的事实阻碍了可靠的发现。 这项提议寻求支持开发新的软件，并将其应用于识别、可视化和分析重复的短(约5-25个碱基)简并基序，即短(数百个碱基)和长(整个染色体)DNA序列。我们打算在人类和其他基因组以及我们的生物和化学合作者感兴趣的序列上使用这个软件，试图系统地描述短的过度表达的序列。我们将确定这些基序中的哪些与实验确认的转录因子结合共识相对应，研究它们的系统发育保守性，并调查它们与重复家族的可能联系。将特别关注基因的上游序列，并将开发工具，在全基因组范围内搜索相关的基序布局。 我们的软件将基于经典字符串处理算法的改编，通过将种子元素组合成统计上显著的退化主题，以一种新颖的方式解决不精确匹配问题。除了与我们的合作者一起进行分析外，我们还将把这些程序与我们已经开发和发布的其他工具一起放在公共领域，邀请其他调查人员在他们自己的数据上使用它们。