Bioinformatic Tools in Cancer Research

人类和小鼠基因组遗传结构的知识对疾病基因定位、数量性状基因座（QTL）定位和人类癌症小鼠模型的实用性策略具有重要意义。我们开发了一个软件工具，SNPdetector，用于通过基于荧光的重测序自动识别突变和单核苷酸多态性（SNP）。SNPdetector的设计旨在最大限度地减少手动SNP分析的要求，这是目前大规模调查中的瓶颈。SNPdetector的错误率比当前“最先进”的软件工具polyphred 5低50%。华盛顿大学和贝勒医学院两个大型人类基因组测序中心已安装并应用SNPdetector，用于ENCODE项目和肿瘤突变检测的大规模SNP分析。SNPdetector也被应用于NHGRI的斑马鱼诱变研究中发现ENU诱导的突变。我们分析了SeattleSNPs项目测序的166个人类基因的连锁不平衡模式。我们发现，在最近的许多研究中报道的块状LD结构仅覆盖15-20%的基因组区域。其余区域具有重叠的LD或不与其他标记物处于LD中的单例SNP。我们分析了重叠LD模式的起源，并评估了目前的单倍型阻断方法在这些人类基因中捕获各种LD模式的效果。我们预计，重叠LD模式的发现将对关联研究的设计产生影响。在小鼠模型中，缺乏遗传多样性被认为是实验室近交系小鼠的主要缺点。我们对小鼠16号染色体的高分辨率、多品系单倍型结构的分析揭示了复杂的单倍型结构，表明实验室小鼠品系的受控复杂性为研究人类复杂疾病提供了巨大的实用性。该实验室还专注于开发工具功能分析。这些包括分析方法，计算过程和可视化工具，以评估mRNA表达数据，以及工具，以确定候选基因。人们认识到，途径分析比聚类或分类分析对观察到的微阵列数据提出了更高的要求。现有的工具不能区分高质量的探针和具有过量表达或空表达值的探针。此外，一个基因可能具有多个探针组，这些探针组给出相互冲突的表达信号。为了解决这些问题，我们开发了一种方法来建立一个统一的探针定义使用基因定位信息。我们在NCICB的合作者目前正在使用这种基于基因的探针定义来分析临床研究界伦勃朗项目中的表达数据。为了便于在疾病关联研究中识别候选基因，我们开发了一个动态和强大的搜索引擎，基因功能相似性搜索工具（GFSST），它允许我们在疾病关联研究和药物靶点发现中选择候选基因。对于基因本体（GO）术语中定义的给定基因或给定基因功能集，该工具可以在用户定义的相似性阈值内识别基因。为了方便这种搜索，我们已经定义了一个统计模型来衡量基因的功能相似性的基础上GO有向无环图（DAG）。可在http://gfsst.nci.nih.gov上获得人类和小鼠基因组的UniProt（通用蛋白质资源）上的GFSST实施。我们开发了用于同步Ciphergen MassSpec图谱的工具，以减少产生假阳性信号的实验变异。我们还在开发一种新的生物标志物发现算法。

Knowledge of genetic architecture of the human and mouse genome has important implications for strategies of disease gene mapping, quantitative trait loci (QTL) mapping, and the utility of mouse model for human cancer. We developed a software tool, SNPdetector, for automated identification of mutations and single nucleotide polymorphisms (SNPs) by fluorescence-based resequencing. SNPdetector was designed to minimize the requirement for manual SNP analysis, currently the bottleneck in a large-scale investigation. The error rate of SNPdetector is 50% lower than the current "state-of-art" software tool polyphred 5. Two large human genome sequencing centers, Washington University and Baylor College of Medicine, have installed and applied SNPdetector for large-scale SNP analysis for the ENCODE project and tumor mutation detection. SNPdetector has also been applied to discover ENU-induced mutations in a zebra fish mutagenesis study at NHGRI. We analyzed the patterns of Linkage Disequilibrium in 166 human genes sequenced by the SeattleSNPs project. We found that the block-like LD structure, reported in many recent studies, only covers 15-20% of the genomic regions. The remaining regions either have overlapping LD or singleton SNPs that are not in LD with the other markers. We analyzed the origin of the overlapping LD patterns and evaluated how well the current haplotype blocking methods captures the various LD patterns in the these human genes. We anticipate that the discovery of the overlapping LD pattern will have an impact on the design of association study. In mouse models, lack of genetic diversity has been considered as a major drawback of laboratory-inbred mouse. Our analysis of a high-resolution, multiple-strain haplotype structure of mouse chromosome 16 reveals a complex haplotype structure, indicating that the controlled complexity of laboratory mouse strains provides great utility for studying human complex diseases.The laboratory also has focused efforts on developing tools for functional analysis. These include analytical methods, computational processes and visualization tools to evaluate mRNA expression data, as well as tools to identify candidate genes. It is recognized that pathway analysis makes significantly greater demands on observed microarray data than cluster or classification analysis. Existing tools do not differentiate probes of good quality from those that have either excess expression or null expression values. In addition, one gene may have multiple probe sets that give conflicting expression signals. To resolve these issues, we developed a method to build a unified probe definition using gene mapping information. Our collaborators at NCICB are currently using this gene-based probe definition to analyze expression data in the Rembrandt project for the clinical research community. To facilitate the identification of candidate genes in a disease association study, we have developed a dynamic and robust search engine, the Gene Functional Similarity Search Tool (GFSST), which allows us to select candidate genes in disease association studies and drug target discoveries. For a given gene or a given set of gene functions defined in Gene Ontology (GO) terms, this tool can identify genes within a user defined similarity threshold. To facilitate this search, we have defined a statistical model to measure functional similarity of genes based on the GO directed acyclic graph (DAG). An implementation of GFSST on UniProt (Universal Protein Resource) for the human and mouse genomes is available at http://gfsst.nci.nih.gov. We have developed tools for synchronizing Ciphergen MassSpec profile to reduce experimental variations that give false positive signal. We are also developing a new algorithm for biomarker discovery.