GENOME DATA ANALYSIS--THEORY AND SOFTWARE

基因组数据分析——理论和软件

• 批准号: 3333904
• 负责人: PHILIP P GREEN
• 金额: $ 14.15万
• 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 1994-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/3333904
• 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; nucleic acid sequence; 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.

我将开发分析几种类型的基因组的改进方法 映射和测序数据，然后将被纳入我的 现有软件包CRIMAP、SEGMAP和GENEFINDER， to the genome基因analysis分析community社区. 1.我已经开始研究一种统一的方法来分析和整合 将各种不同的数据映射成一张物理地图 技术，包括YAC重叠群的STS-内容作图， 定位、辐射杂交定位、原位杂交和脉冲 现场凝胶限制性酶切图谱。 这种方法叫做基因组片段 分析，是基于观察，这些不同的映射 方法都可以被看作是提供关于关系的信息 不同类型的基因组片段之间。 统计方法， 基因组片段数据的分析，受到连锁分析方法的启发， 已经在CRIMAP程序中实现，并且组合 （确定性）方法，灵感来自构建STS的方法 YAC重叠群的内容图谱已经在程序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（和大型） 数据量），描述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 将进行调查的准确性地图构建使用 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 疾病位点，通过扩展其当前有效的可能性， 处理不完全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程序采用系统的统计方法， 鉴定并显示C.线虫基因组序列。 我 将发展分析其他基因组的能力，包括人类。 其他改进将包括自动构建候选人 基因从其组成外显子，自动识别可能的 测序错误区域以及显示能力的扩展 为了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。 此外，我将系统地 将这种方法的能力与最近的“神经网络”方法进行比较， 基因鉴定