IMAGE-ESTIMATION METHODS FOR AUTOMATED DNA SEQUENCING

拟议工作的广泛目标是提高DNA的效率 AUTOMATIC数据的二次开发与应用 电泳胶阅读器，我们最近开发的估计方法 限制片段大小调整的上下文。我们认为，这种方法 展示了扩大序列凝胶可读范围的前景 显著地提高了测序研究的产量 采用高分辨率的电泳法。我们的总体方法是 对电泳胶数据的分析是为了验证和利用 在适当的情况下，由其他人开发的模型，并开发和 根据需要评估新模型，以描述在 生成观测数据并将这些模型应用于估计 旨在确定最有可能引起兴趣的现象的方法 已经产生了这些数据。实现拟议目标的具体目标 工作内容为：1)开发和/或导入、改进和验证BAND模型 作为时间和局部序列的函数的移动性、幅度和形状， 以及基线随时间和空间的变化；2)开发和评估 使用从所开发的模型得到的先验的基地呼叫算法 在目标1中，并基于以下两种情况下数据的后验似然 荧光发射的高斯和泊松模型；3)后续 探索随机与符号相结合的方法的应用 为了结合更复杂的模型进行推论，例如 需要分析压缩等异常情况；4)开发和 应用多级别(例如，符号、配置文件和扫描强度)，带注释 用于量化算法性能的评估数据库 考虑错误类型及其频率；5)及早启动 传播(“测试版测试”)算法和评估 数据库；随后的全面传播将在 我们的实验室计划继续得到生物医学中心的支持 美国国立卫生研究院NCRR研究技术计划。因为人类的体型 基因组、全序列的确定将不仅仅取决于发展 低成本、可靠的技术，既可以自动化，也可以 提高这些技术的信息产出的战略。

The broad goal of the proposed work is to advance the efficiency of DNA sequencing by further developing and applying to the data from automatic electrophoretic-gel readers, estimation methods recently developed by us in the context of restriction-fragment sizing. We believe that such methods show promise for extending the readable range of sequence gels significantly, thereby improving the yield from sequencing studies employing high-resolution electrophoresis. Our general approach to the analysis of data from electrophoretic gels is to verify and capitalize on the models developed by others, where appropriate, and to develop and evluate new models as needed to describe the processes operative in generation of the observed data and apply those models to estimation methods designed to determine the phenomenon of interest most likely to have given rise to the data. Specific aims for accomplishing the proposed work are: 1) To develop and/or import, refine, and verify models of band mobility, amplitude, and shape as a function of time and local sequence, and of baseline variation with space and time; 2) To develop and evaluate base-calling algorithms employing priors derived from the models developed in aim 1 and based on the posterior likelihood of the data under both Gaussian and Poisson models for fluorescent emission; 3) To subsequently explore the application of methods for joint stochastic and symbolic inferences in order to incorporate more complex models such as would be needed to analyze aberrancies such as compressions; 4) To develop and apply a multilevel (e.g. symbolic, profile, and scan-intensity), annotated evaluation database for quantifying performance of the algorithms with respect to error types and their frequencies; and 5) To initiate early dissemination ("beta testing") of the algorithms and the evaluation database; subsequent full-scale dissemination would be under the auspices of planned continuation support of our laboratory from the Biomedical Research Technology Program of NCRR, NIH. Because of the size of the human genome, full sequence determination will depend not only on the development of low-cost-reliable technologies that can be automated but also on strategies to improve the information yield from those technologies.