DNA & PROTEIN MICRO-ARRAY FACILITY (SHARED RESOURCE)

UCI DMA和蛋白质微阵列设施最初成立于1999年，是一个综合性的癌症 中心共享资源核心设施，旨在协助北校区和南校区的调查人员 进行微阵列表达分析。南校区内外约200名调查人员 在过去的五年里，他们提供了服务。我们主要利用Affyscore基因芯片阵列 用于监测基因表达和进行SNP/基因型作图的系统。我们的使命是提供RNA 以一致和及时的方式进行表达和基因组DNA分析， 我们设施的用户。除了提供进行基因表达的实验专业知识， SNP/基因型作图方案，工作人员提供有关实验设计的建议和咨询， 微阵列研究的数据分析方法和统计学。 目前，这项技术的主要用途是确定基因的差异表达模式， 无论是在细胞内还是在组织内。这项技术使研究人员能够比较表达模式， 病变和正常细胞、癌细胞和非癌细胞以及不同组织类型。的目标 这些类型的研究是为了更好地了解可能导致疾病状态的过程， 找到新的或更好的方法来控制或阻止疾病的发展。一个迅速 该技术的日益广泛的应用是分析基因型变异（SNP）以定位致病基因。 这些类型的方法将提供对多因素疾病状态的洞察。全基因组信息 与个体不同，基因信息可以用来理解由以下原因引起的疾病的更微妙的模式： 多个基因或环境和遗传因素的汇合。 由于全基因组阵列提供的大量详细信息， 微阵列将成为一个更重要的工具，不仅在了解疾病的过程中， 在疾病诊断和个性化治疗设计方面。

The UCI DMA & Protein MicroArray Facility was initially established in 1999 as a Comprehensive Cancer Center Shared Resource Core Facility, directed at assisting both North and South Campus investigators performing microarray expression analysis. About 200 investigators inside and outside the South Campus have been provided services in the last five year period. We primarily utilize the Affymetrix GeneChip array system to monitor gene expression and perform SNP/genotype mapping. Our mission is to provide RNA expression and genomic DMA analysis in a consistent and timely manner that is of the highest quality for all of our Facility's users. In addition to providing experimental expertise for performing gene expression and SNP/genotype mapping protocols, the staff provides advice and consultation on experimental design, and data analysis approaches and statistics for microarray based research. Currently, the primary use of this technology is to determine the differential expression patterns of genes, either within cells or tissues. This technology enables investigators to compare patterns of expression in diseased and normal cells, cancer cells and non-cancerous cells and in different tissue types. The goal of these types of studies is to better understand the processes that potentially lead to diseased states and to find new or better ways of either controlling or stopping the progression of diseased states. A rapidly increasing use of this technology is analysis of genotypic variants (SNPs) to map disease-causing genes. These types of approaches will provide insight into multifactorial disease states. Genomewide information unlike individual gene information can be used to understand the more subtle patterns of diseases caused by multiple genes or by the confluence of environmental and genetic factors. Because of the great amount of detailed information provided from genomewide arrays, it is likely that microarrays will become an even more important tool not only in understanding disease processes, but also in disease diagnosis and the design of individualized therapies.