Computational Problems in DNA Sequencing, and Regulatory and Sequence Analysis

DNA 测序、调控和序列分析中的计算问题

• 批准号: 9974498
• 负责人: Martin Tompa
• 金额: $ 129.25万
• 依托单位: University of Washington
• 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 2003-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9974498&HistoricalAwards=false
• 关键词: Computational; Problems; DNA; Sequencing; Regulatory

Genome Projects and similar efforts are generating vast amounts of data, but many challenges still remain in sequencing and mappng of large genomes. Analysis of these data is also still in its infancy, and refined tools and methodologies are sorely needed. This project aims to provide mathematical and computational improvements in response to several key challenges in the assembly and analysis of genomic data, looselycategorized into mapping and sequencing, regulatory analysis, and discovery of sequence motifs. Although algorithms for physical mapping and sequencing have been studied for years, there are still no fully satisfactory practical solutions to support current mapping and sequencing efforts as they move to newer technologies, longer and more repeat-rich targets, and dramatically higher throughputs. This project will algorithms and software for assembly of physical maps, with particular emphasis on increasing robustness and decreasing the need for human intervention. It also will investigate algorithms and analysis for novel sequencing strategies that have the potential to reduce cost and increase throughput, accuracy, and speed of sequencing and resequencing.One of the central goals in molecular biology and genetics is to elucidate the function of genes. This goal is going to become more dominant as more raw genomic information accumulates. By observing the level of expression of a gene in different phases in the organism life cycle, different tissues, or different disease stages, one can get important clues to understanding the gene's role, with significant potential diagnostic and therapeutic applications. This project will provide analytical and algorithmic tools for several problems arising in gene expression analysis. In many cases, one cannot elucidate the functions of individual genes without attacking the more general problem of understanding regulatory pathways--systems of interacting genes and proteins controlling fundamental cellular processes. Again, the technology for gathering relevant data currently far outstrips the capabilities of computational tools for their analysis. This project will study algorithms for designing and interpreting expression array experiments involving gene knockouts and other perturbations that, in concert with a priori biological knowledge, allow putative pathways to be verified (or refuted) and perhaps even inferred.Another area where data gathering capabilities outstrip those of our analytical tools is sequence analysis. A recurrent approach in sequence analysis is identification of sequence motifs--approximately repeated patterns in DNA or protein sequence data. Such similarities are often the key to identifying functionally important features such as protein binding sites on DNA sequences. As one example of the potential utility of such tools, they might help bridge an important gap in regulatory studies such as those outlined above. When analysis of expression array data reveals large sets of coregulated genes, a natural next step is to look for common motifs in regions near these genes, potentially binding sites for regulatory proteins. Identification of these proteins would then suggest dependencies in the regulatory pathways for the genes under study. This project will study key problems related to finding motifs and assessing their statistical significance, including subtle sequence signals involving long patterns with inserted and deleted residues.

基因组计划和类似的工作正在产生大量数据，但大型基因组的测序和绘图仍然存在许多挑战。对这些数据的分析也仍处于起步阶段，迫切需要完善的工具和方法。该项目旨在提供数学和计算方面的改进，以应对基因组数据组装和分析中的几个关键挑战，这些数据大致分为绘图和测序、调控分析和序列基序的发现。尽管物理作图和测序的算法已经研究了多年，但随着当前的作图和测序工作转向更新的技术、更长、重复次数更多的目标以及显着更高的通量，仍然没有完全令人满意的实用解决方案来支持它们。该项目将使用用于组装物理地图的算法和软件，特别强调提高鲁棒性并减少人工干预的需要。它还将研究新测序策略的算法和分析，这些策略有可能降低成本并提高测序和重测序的通量、准确性和速度。分子生物学和遗传学的中心目标之一是阐明基因的功能。随着更多原始基因组信息的积累，这一目标将变得更加重要。通过观察基因在生物体生命周期不同阶段、不同组织或不同疾病阶段的表达水平，可以获得了解该基因作用的重要线索，具有重要的潜在诊断和治疗应用价值。该项目将为基因表达分析中出现的几个问题提供分析和算法工具。在许多情况下，如果不解决理解调控途径（控制基本细胞过程的相互作用的基因和蛋白质的系统）这一更普遍的问题，就无法阐明单个基因的功能。同样，目前收集相关数据的技术远远超过了计算工具的分析能力。该项目将研究设计和解释涉及基因敲除和其他扰动的表达阵列实验的算法，这些实验与先验的生物学知识相结合，允许验证（或反驳）甚至推断推定的路径。数据收集能力超过我们分析工具的另一个领域是序列分析。序列分析中的一种常见方法是识别序列基序——DNA 或蛋白质序列数据中的近似重复模式。这种相似性通常是识别重要功能特征（例如 DNA 序列上的蛋白质结合位点）的关键。作为此类工具潜在效用的一个例子，它们可能有助于弥合上述监管研究中的一个重要差距。当表达阵列数据分析揭示大量共调控基因时，自然的下一步是寻找这些基因附近区域的共同基序，即调控蛋白的潜在结合位点。这些蛋白质的鉴定将表明所研究基因的调控途径的依赖性。该项目将研究与寻找基序并评估其统计显着性相关的关键问题，包括涉及插入和删除残基的长模式的微妙序列信号。