GENOME DATA ANALYSIS--THEORY AND SOFTWARE

基因组数据分析——理论和软件

• 批准号: 3333903
• 负责人: PHILIP P GREEN
• 金额: $ 25.05万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 1992
• 资助国家: 美国
• 起止时间: 1992-09-29 至 1995-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/3333903
• 关键词: artificial chromosomes; computer assisted sequence analysis; computer program /software; computer system design /evaluation; genetic mapping; genome; in situ hybridization; linkage mapping; mathematical model; pulsed field gel electrophoresis; restriction mapping; sequence tagged sites; statistics /biometry

I will develop improved methods for analyzing several types of genome mapping and sequencing data, which will then be incorporated into my existing software packages CRIMAP, SEGMAP and GENEFINDER and distributed to the genome analysis community. 1. I have begun work on a unified approach to analyzing, and integrating into a single physical map, the data from a variety of different mapping techniques, including STS-content mapping of YAC contigs, linkage mapping, radiation hybrid mapping, in situ hybridization, and pulsed field gel restriction mapping. This approach, called genomic segment analysis, is based on the observation that these different mapping methods can all be viewed as providing information about relationships between genomic segments of various types. A statistical approach to analysis of genomic segment data, inspired by linkage analysis methods, has been implemented in the program CRIMAP, and a combinatorial (deterministic) approach, inspired by methods for constructing STS content maps of YAC contigs, has been implemented in the program SEGMAP. These methods will be further developed, with the primary emphases being on allowing efficient joint analysis of different types (and large amounts) of data, on characterizing and representing ambiguities of map order and distance, and on detection of data errors. Simulation studies will be carried out to investigate the accuracy of maps constructed using these approaches, examining in particular the effects of data errors. 2. I will improve CRIMAP's ability to perform multilocus linkage analysis with disease loci, by extending its current efficient likelihood computation and maximization methods to handle incomplete pedigree information and more general disease locus models. 3. The program GENEFINDER uses a systematic statistical approach to identify and display probable exons in C. elegans genomic sequence. I will develop its ability to analyze other genomes, including the human. Other improvements will include the automated construction of candidate genes from their component exons, automatic identification of likely regions of sequencing errors, and extension of the display capabilities to include other types of genomic features, such as repeats, promoter sequences, and protein motifs. In addition, I will systematically compare the power of this approach with recent "neural net" approaches to gene identification.

我将开发改进的方法来分析几种类型的基因组 绘图和测序数据，然后将其纳入我的 现有软件包 CRIMAP、SEGMAP 和 GENEFINDER 并已分发 基因组分析社区。 1. 我已经开始研究一种统一的方法来分析和整合 将来自各种不同映射的数据整合到单个物理地图中 技术，包括 YAC 重叠群的 STS 内容映射、链接 测绘、辐射混合测绘、原位杂交和脉冲 场凝胶限制图谱。 这种方法称为基因组片段 分析是基于对这些不同映射的观察 方法都可以被视为提供有关关系的信息 各种类型的基因组片段之间。 统计方法 受到连锁分析方法的启发，对基因组片段数据进行分析， 已在 CRIMAP 程序中实施，并且组合 （确定性）方法，受到构建 STS 方法的启发 YAC重叠群的内容图谱已在程序SEGMAP中实现。 这些方法将得到进一步发展，主要重点是 允许对不同类型（以及大型 数据量，关于表征和表示地图的模糊性 顺序和距离，以及数据错误的检测。 模拟研究 将调查使用所构建的地图的准确性 这些方法，特别是检查数据错误的影响。 2.我将提高CRIMAP进行多位点连锁分析的能力 与疾病位点，通过扩展其当前的有效可能性 处理不完整谱系的计算和最大化方法 信息和更一般的疾病位点模型。 3. GENEFINDER 程序使用系统统计方法 识别并显示线虫基因组序列中可能的外显子。 我 将发展其分析其他基因组（包括人类基因组）的能力。 其他改进将包括自动构建候选 来自其组成外显子的基因，自动识别可能的 测序错误区域和显示能力的扩展 包括其他类型的基因组特征，例如重复序列、启动子 序列和蛋白质基序。 另外，我会系统地 将这种方法与最近的“神经网络”方法的威力进行比较 基因鉴定。