Mathematical Models for DNA Sequencing Quality Assurance

The growing use of DNA sequence data in research, databases, dianostic and therapeutic biotechnology; and even litigation requires that the quality of data being used be 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. This work proposes to perform investigations to: determine variables important to quality control of DNA sequencing; design optimal testing protocols and sampling plans for assessing precision and accuracy of DNA sequencing laboratories; and develop methods for determining variables which influence error rates, including sequence content, sequencing method, and experimental protocol. 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.