Bioinformatics and Statistics

该计划项目的目标是整合三个不同的基因组学科，以促进我们的 临床肾移植工作体会。急性排斥和慢性同种异体移植患者 肾病将被比作没有排斥反应和长期、功能良好的移植的患者。 具体地说，我们建议同时使用肾移植活检和外周血淋巴细胞。基因 将使用基于Affymetrix寡核苷酸的微阵列进行表达谱分析(项目1)。 蛋白质组学将涉及使用液相色谱与线性离子陷阱质谱联用 (项目2)。由这两种技术生成的数据标识的代理候选对象将包括一组 500个基因，我们将对其进行复杂性状SNP遗传分析(项目3)。生物信息学和 统计核心将为所有项目提供生物信息学和统计支持。所有数据都将流向 用于高级分析和归档的核心。在第一级，这种支持将包括监测 统计完整性的实验设计，组织每个项目中生成的复杂数据集，以便 首席调查员更容易接触到它们以进行解释和发现并执行数据 使用生物信息学工具进行挖掘。输入斯克里普斯网站的临床数据也将被整合 以实时的方式进行这些努力。在第二级，核心将监督遴选和 基于基因表达和蛋白质组数据的500个候选基因集的统计验证 项目3中的复杂性状遗传学。我们将使用监督和非监督数据挖掘方法，如 集群和班级预测工具。我们还将确定差异表达的基因和蛋白质在 不同肾移植组的比较。在第三级，核心将整合所有 三个项目，以便能够识别、定义和验证连接、路径和机制 可能包括患者和捐赠者遗传学之间的新关系。

The objective of this program project is to integrate three different genomic disciplines to advance our working understanding of clinical kidney transplantation. Patients with acute rejection and chronic allograft nephropathy will be compared to patients without rejection and long term, well-functioning transplants. Specifically, we propose to use both kidney transplant biopsies and peripheral blood lymphocytes. Gene expression profiling will be done using Affymetrix oligonucleotide-based microarrays (Project 1). Proteomics will involve the use of liquid chromatography coupled with linear ion trap mass spectrometry (Project 2). Gent candidates identified by data generated with these two technologies will comprise a set of 500 genes for which we will perform complex trait SNP genetic analysis (Project 3). The Bioinformatics and Statistics Core will provide the bioinformatics and statistical support for all projects. All data will flow to the Core for both advanced analysis and archiving. At the first level this support will include monitoring experimental designs for statistical integrity, organizing complex data sets generated in each Project so that they are more accessible to the Principal Investigators for interpretation and discovery and perform data mining using bioinformatic tools. Clinical data entered into the web site at Scripps will also be integrated with these efforts in a real-time fashion. At the second level the Core will supervise the selection and statistical validation of the 500 gene candidate set based on gene expression and proteomic data intended for complex trait genetics in Project 3. We will use supervised and unsupervised data mining methods such as clustering and class prediction tools. We will also identify differentially expressed genes and proteins in the different kidney transplant group comparisons. At the third level the Core will integrate data generated in all three Projects so that connections, pathways and mechanisms can be recognized, defined and validated including possibly novel relationships between patient and donor genetics.