Bioinformatics and Statistics

生物信息学和统计学

• 批准号: 7979535
• 负责人: Steve Horvath
• 金额: $ 21.68万
• 依托单位: SCRIPPS RESEARCH INSTITUTE, THE
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-30 至 2011-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7979535
• 关键词: 3-Dimensional; Acute; Address; Affect; African American; Algorithms; Alleles; Archives; Atherosclerosis; Autoimmune Diseases; Biochemical Pathway; Bioinformatics; Biological; Biopsy; Calcineurin; Candidate Disease Gene; Case-Control Studies; Categories; Cells; Chi-Square Tests; Chronic; Chronic rejection of renal transplant; Classification; Clinical; Clinical Data; Complement; Complex; Complex Genetic Trait; Computer software; Computers; Coupled; Data; Data Set; Databases; Diagnostic; Discipline; Discriminant Analysis; Disease; Distant; Drug toxicity; Ethnic group; Experimental Designs; Frequencies; Future; Gene Expression; Gene Expression Profiling; Gene Frequency; Gene Proteins; Genes; Genetic; Genetic Polymorphism; Genetic Variation; Genomics; Genotype; Haplotypes; Head; Hispanics; Human; Human Genetics; Immune; Immune response; Immunosuppressive Agents; Indium; Individual; Informatics; Injury; Injury to Kidney; Internet; Joints; Kidney; Kidney Transplantation; Knowledge; Learning; Left; Link; Linkage Disequilibrium; Liquid Chromatography; Logistic Regressions; Machine Learning; Mass Fragmentography; Measurement; Metabolism; Methods; Microarray Analysis; Modeling; Molecular; Molecular Profiling; Monitor; Nomenclature; Oligonucleotides; Organ; Outcome; Paper; Pathway Analysis; Pathway interactions; Patients; Pattern; Peripheral Blood Lymphocyte; Pharmaceutical Preparations; Population; Principal Component Analysis; Principal Investigator; Proteins; Proteomics; Public Domains; Publishing; Recording of previous events; Regulation; Relative (related person); Renal function; Research Personnel; Sample Size; Sampling; San Francisco; Sex Characteristics; Specific qualifier value; Statistical Methods; Sum; System; Techniques; Technology; Testing; Time; Tissues; Transplantation; Validation; Voting; Work; Xenobiotic Metabolism; base; case control; clinical phenotype; comparison group; data mining; data sharing; design; forest; gene function; genetic analysis; genetic association; human disease; indexing; interest; male; meetings; nephrotoxicity; novel; open source; programs; protein expression; repaired; research study; response; simulation; statistics; success; tool; trait; trend; web site

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.

该项目的目标是整合三个不同的基因组学科，以推进我们的研究。 对临床肾移植的工作理解。急性排斥和慢性同种异体移植患者 肾病将与没有排斥反应和长期、功能良好的移植的患者进行比较。 具体来说，我们建议同时使用肾移植活检和外周血淋巴细胞。基因 表达谱分析将使用基于亲和素的微阵列（项目1）进行。 蛋白质组学将涉及液相色谱与线性离子阱质谱联用 （项目2）。通过这两种技术生成的数据识别的Gent候选人将包括一组 我们将对500个基因进行复杂性状SNP遗传分析（项目3）。生物信息学和 Statistics Core将为所有项目提供生物信息学和统计支持。所有数据将流向 用于高级分析和归档的核心。在第一级，这种支助将包括监测 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， 他们更容易被主要研究者解释和发现，并执行数据 使用生物信息学工具进行采矿。在斯克里普斯的网站上输入的临床数据也将被整合 用实时的方式来实现这些努力。在第二级，核心将监督甄选工作， 基于基因表达和蛋白质组学数据的500个基因候选集的统计学验证， 项目3中的复杂性状遗传学。我们将使用监督和非监督的数据挖掘方法，如 聚类和类别预测工具。我们还将确定差异表达的基因和蛋白质， 不同肾移植组的比较。在第三层，核心将整合所有 三个项目，以便认识、界定和验证各种联系、途径和机制 包括患者和捐赠者基因之间可能存在的新关系。