Mathematical Models for DNA Sequencing Quality Assurance

DNA 测序质量保证的数学模型

• 批准号: 9460346
• 负责人: Jeffrey Sachs
• 金额: $ 7.48万
• 依托单位: Daniel H Wagner Associates Inc (Sunnyvale Office)
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1995
• 资助国家: 美国
• 起止时间: 1995-06-01 至 1996-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9460346&HistoricalAwards=false
• 关键词: Mathematical; Models; DNA; Sequencing; Quality

This Small Business Innovation Research Phase I project will: determine variables important to quality control of DNA sequencing such as error dependency on sequence content; design optimal testing protocols and sampling plans for assessing precision and accuracy of DNA sequencing laboratories; and develop methods for predicting insertion and deletion errors (frameshifts) in sequence data. The power of statistical tests will be studied using results for sequencing combinatorially designed, synthetic DNA fragments. This investigation will be based on theoretical analysis, computer simulations, and on data provided through our collaboration with the Yeast Genome Center at Stanford University. The growing use of DNA sequence data in research, databases, diagnostic and therapeutic biotechnology, and even litigation requires that the quality of data being used by objectively assessable. The proposed research addresses the following problems associated with sequence data: the inability to associate consistent, quantitative error rate estimates with sequence data; the concomitant lack of quality control on the sequence data; and the inability to make informed decisions balancing cost per base of different methods (or laboratories) against the relative stringency of quality requirements imposed on different sequencing tasks.

这个小企业创新研究第一阶段项目将：确定对DNA测序质量控制重要的变量，如对序列内容的误差依赖；设计最佳测试方案和采样计划，以评估DNA测序实验室的精度和准确性；并提出预测序列数据中插入和删除错误（帧移）的方法。统计测试的力量将使用测序组合设计的结果来研究，合成DNA片段。这项研究将基于理论分析、计算机模拟以及我们与斯坦福大学酵母基因组中心合作提供的数据。DNA序列数据在研究、数据库、诊断和治疗生物技术、甚至诉讼中越来越多地使用，这要求数据的质量被客观地评估。提出的研究解决了与序列数据相关的以下问题：无法将一致的定量错误率估计与序列数据联系起来；伴随而来的序列数据质量控制的缺失；以及无法做出明智的决定，平衡不同方法（或实验室）的每个基础的成本，反对强加于不同测序任务的相对严格的质量要求。