GENOME DATA ANALYSIS--THEORY AND SOFTWARE

我将开发改进的方法来分析几种类型的基因组 测绘和排序数据，然后将其合并到我的 现有的软件包CRIMAP、SEGMAP和GENEFINDER以及分布式 给基因组分析社区。 1.我已开始制定统一的分析和集成方法 将来自各种不同映射的数据转换成单个物理映射 技术，包括YAC重叠群的STS-内容作图，连锁 作图、辐射杂交作图、原位杂交和脉冲 野外凝胶限制图。这种方法被称为基因组片段 分析，是基于观察到的这些不同的映射 方法都可以被视为提供有关关系的信息 在不同类型的基因组片段之间。一种统计方法 基因组片段数据的分析，受连锁分析方法的启发， 已在CRIMAP程序中实现，并实现了一个组合 (确定性)方法，灵感来自构建STS的方法 YAC重叠群内容图，已在SEGMAP程序中实现。 这些方法将得到进一步发展，主要重点是 关于允许不同类型(和大型)的有效联合分析 数据量)，关于地图的模糊性的表征和表示 顺序和距离，以及数据错误的检测。模拟研究 将进行调查，以调查使用 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 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 信息和更一般的病源地模型。 3.程序GENEFINDER使用系统统计方法来 鉴定和展示线虫基因组序列中可能的外显子。我 将发展其分析包括人类在内的其他基因组的能力。 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 排序错误的区域和显示能力的扩展 包括其他类型的基因组特征，如重复序列、启动子 序列和蛋白质模体。此外，我还将系统地 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。

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.