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测序推断的个人基因构成将有助于确定个人对一系列慢性和急性疾病或紊乱的易感性，使新药物的发现和临床测试成为可能，并总体上使卫生保健的提供得到个性化和改善。在个性化医疗的承诺效益实现之前，DNA测序技术必须变得快速、负担得起和可靠。传统测序技术的高成本和劳动密集型特性使其不适合常规测序任务。最近开发的合成测序技术是一种新的高通量技术，解决了这些障碍--目前，它实现了比传统方法低两个数量级的成本。然而，合成测序的保真度和序列读取长度都低于昂贵的传统技术，对于大多数医学研究来说，它的整体性能是不够的。本研究的目标是开发能够在合成测序系统中准确可靠地检测DNA序列的信号处理技术。研究人员的具体目标是：(1)建立合成测序过程的数学模型，并推导出推断这些模型的参数的技术。(2)设计了综合测序系统中最优DNA序列检测的高效算法。(3)用实验数据验证理论结果。上述工作的结果预计将对高性能负担得起的DNA测序的开发和应用产生重大影响。