CIF:Small:Next Generation DNA Sequencing: Signal Processing Perspectives

CIF:Small:下一代 DNA 测序：信号处理视角

• 批准号: 1018235
• 负责人: Haris Vikalo
• 金额: $ 12.25万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2011-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1018235&HistoricalAwards=false
• 关键词: CIF; Small; Next; Generation; DNA

An individual's genetic makeup, inferred by means of DNA sequencing, will help determine the individual's susceptibility to a broad range of chronic and acute diseases or disorders, enable the discovery and clinical testing of new pharmaceutical products, and generally personalize and improve the delivery of health care. Before the promised benefits of personalized medicine come to fruition, DNA sequencing technology must become fast, affordable, and reliable. High cost and labor intensive nature of conventional sequencing technology render it unfit for routine sequencing tasks. Recently developed sequencing-by-synthesis is a novel high-throughput technique addressing these obstacles -- currently, it achieves a cost reduction of two orders of magnitude as compared to the conventional method. However, fidelity and sequence read-lengths of sequencing-by-synthesis are inferior to those of the costly conventional technology, and its overall performance is insufficient for most medical studies.The goal of this research is to develop signal processing techniques which enable accurate and reliable DNA sequence detection in sequencing-by-synthesis systems. The investigator specifically aims to: (1) Develop mathematical models of sequencing-by-synthesis process and derive techniques for inferring parameters of such models. (2) Design computationally efficient algorithms for optimal DNA sequence detection in sequencing-by-synthesis systems. (3) Validate the obtained theoretical results on experimental data. The results of the outlined work are expected to have a major impact on the development and applications of high-performance affordable DNA sequencing.

通过 DNA 测序推断个体的基因构成，将有助于确定个体对各种慢性和急性疾病或病症的易感性，实现新医药产品的发现和临床测试，并普遍个性化和改善健康的提供关心。在个性化医疗所承诺的好处实现之前，DNA 测序技术必须变得快速、经济且可靠。传统测序技术的高成本和劳动力密集性质使其不适合常规测序任务。最近开发的边合成边测序是一种解决这些障碍的新型高通量技术——目前，与传统方法相比，它实现了两个数量级的成本降低。然而，合成测序的保真度和序列读长均不如昂贵的传统技术，其整体性能不足以满足大多数医学研究的要求。本研究的目标是开发信号处理技术，使准确和可靠的信号处理技术成为可能。合成测序系统中可靠的 DNA 序列检测。研究者的具体目标是：（1）开发合成测序过程的数学模型，并推导推断此类模型参数的技术。 (2) 设计计算高效的算法，用于合成测序系统中的最佳 DNA 序列检测。 (3)用实验数据验证所得到的理论结果。上述工作的结果预计将对高性能、经济实惠的 DNA 测序的开发和应用产生重大影响。