IMAGE-ESTIMATION METHODS FOR AUTOMATED DNA SEQUENCING

自动 DNA 测序的图像估计方法

• 批准号: 2208734
• 负责人: LEWIS J THOMAS
• 金额: $ 25.57万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 1989
• 资助国家: 美国
• 起止时间: 1989-07-01 至 1996-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/2208734
• 关键词: biomedical automation; gel electrophoresis; human genetic material tag; imaging /visualization /scanning; information systems; mathematical model; model design /development; nucleic acid sequence

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.

拟议工作的广泛目标是提高DNA的效率 通过进一步开发并应用于自动数据的数据来进行测序 电泳凝胶读取器，美国最近开发的估计方法 限制性碎片大小的上下文。 我们相信这种方法 显示出扩展可读序列凝胶范围的希望 值得注意的是，从测序研究提高了产量 采用高分辨率电泳。 我们对 分析来自电泳凝胶的数据是验证和大写 他人在适当的情况下开发的模型，并开发和发展 根据需要进行新的模型来描述在 生成观察到的数据并将这些模型应用于估计 旨在确定最有可能的现象的方法 引起了数据。 完成提议的具体目标 工作是：1）开发和/或导入，完善和验证频段的模型 移动性，振幅和形状随时间和局部序列的函数， 和基线变化随空间和时间的变化； 2）开发和评估 采用从开发模型中得出的先验的基础呼叫算法 在AIM 1中，基于数据下数据的后验可能性 荧光发射的高斯和泊松模型； 3）随后 探索关节随机和符号的方法的应用 推论以合并更复杂的模型，例如 需要分析诸如压缩之类的劣质； 4）发展和 应用多级（例如符号，轮廓和扫描强度），注释 评估数据库，用于量化算法的性能 尊重错误类型及其频率； 5）提早发起 算法和评估的传播（“ beta测试”） 数据库;随后的全尺度传播将在主持下 来自生物医学的实验室的计划延续支持 NCRR的研究技术计划，NIH。 由于人类的大小 基因组，完整的序列确定不仅取决于发展 可以自动化的低成本可靠技术 提高这些技术信息产量的策略。