SBIR Phase II: Mathematical Models for DNA Sequencing Quality Control

*** 9612376 Young这个小企业创新研究二期项目解决了将一致的、定量的错误率估计与DNA序列数据联系起来的问题，以及由此产生的对这些数据的质量控制的改进。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，这就要求所使用数据的质量是可以客观评估的。将确定对DNA测序质量控制很重要的变量，如序列内容和位置的误差依赖。将开发方法，将这些错误率信息纳入现有和正在开发的数据库条目，并根据对已知最佳设计DNA序列的测序结果，为常规向此类数据库提供DNA序列数据的实验室提供质量控制评分。该项目还将开发协议和用户友好软件，以定期评估给定测序机的当前性能状态。这项研究将基于理论统计分析、计算机模拟和使用专门设计的合成DNA片段进行测序实验的结果。＊＊＊

*** 9612376 Young This Small Business Innovation Research Phase II project addresses the problem of associating consistent, quantitative error rate estimates with DNA sequence data and the resulting improved quality control on such data. 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 be objectively assessable. Variables will be identified which are important to quality control of DNA sequencing such as error dependency on sequence content and position. Methods will be developed for including such error rate information into existing and developing database entries and, based on results from sequencing known optimally designed DNA sequences, provide a quality control score for laboratories which routinely contribute DNA sequence data to such databases. The project will also develop protocols and user-friendly software for periodically assessing the current state of performance for given sequencing machines. This investigation will be based on theoretical statistical analyses, computer simulations, and on results from sequencing experiments using specially designed, synthetic DNA fragments. ***