Gene Expression

基因表达

• 批准号: 8709128
• 负责人: LESLEYANN HAWTHORN
• 金额: $ 4.37万
• 依托单位: ROSWELL PARK CANCER INSTITUTE CORP
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-30 至 2014-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8709128
• 关键词: Algorithms; Alleles; Alternative Splicing; Award; Bioinformatics; Budgets; Buffaloes; Cancer Center Support Grant; Categories; Cells; ChIP-on-chip; Chromosomal Loss; Chromosome abnormality; Chromosomes; Communities; Complex; Computer software; Custom; DNA; Data; Data Analyses; Data Set; Detection; Development; Disease; Doctor of Philosophy; Electrophoresis; Emerging Technologies; Ensure; Event; Exons; Funding; Gene Expression; Gene Expression Profiling; Genes; Genetic; Genome; Genomics; Genotype; Grant; Hawthorn plant; Individual; Karyotype determination procedure; Label; Loss of Heterozygosity; Maps; Methylation; Mutation; Organism; Peer Review; Pharmaceutical Preparations; Polymorphism Analysis; Population; Preparation; Procedures; Process; Publications; Quality Control; RNA; Research; Research Infrastructure; Research Personnel; Resolution; Resources; Roswell Park Cancer Institute; Running; Sampling; Scanning; Sequence Analysis; Services; Single Nucleotide Polymorphism Map; Solutions; Spectrophotometry; Staining method; Stains; Technology; Testing; Time; Tissues; Training; Transcript; Translating; Universities; Update; Validation; Work; base; cost; density; design; gel electrophoresis; genetic association; genetic linkage; genome wide association study; genome-wide; innovation; member; new technology; novel; programs; research study; response; success; tool

The Gene Expression Resource (GER) was established with the successful recruitment of Lesleyann Hawthorn, PhD in 2001. The Resource has flourished since that time with a broad base of satisfied users whose continued patronage is derived from a unique approach to microarray experimentation. Its success is largely due to a commitment to provide researchers with high-quality microarray data with the added advantage of data analysis so that the results provided to the individual researchers are in a usable format, unlike most array-based facilities. Dr Hawthorn and the GER staff keep abreast of emerging technologies to provide users with a broad range of expertise and options. This success is substantiated by a large user base, publications and awarded grants. The Resource is functionally divided into Gene Expression, Genotyping, and Copy Number Analysis at the genomics level. At the level of individual chromosomal regions or specific genes, it offers mutation/polymorphism analysis, SNP validation and discovery, expression level quantification and analysis of methylation status. Most importantly, the Resource offers data analysis for all the platforms that it supports and provides expertise, time and funding for the development of novel technologies. For example, Exon array analysis has been developed which allows simultaneous detection of gene expression and alternative splicing events. Furthermore, the Resource is now able to run Whole-Genome Tiling arrays, which permit the highest-resolution Chip-on-Chip analysis as well as the detection of novel transcripts in genomic DNA. During the last few years, the Resource has been working on innovative approaches to the analysis of genomics data. One of these centers on the high-density SNP arrays that offer the highest density genotyping, as well as simultaneous CGH analysis with accompanying LOH analysis. Gene Expression has been using these arrays to look for gains and losses of chromosomal regions (CGH). The ability of the SNP Mapping arrays to detect standard cytogenetic abnormalities represents a major advancement over conventional karyotyping. The GER has collaborated with Dr Cowell's group (GN) to develop statistical analysis tools that permit the overlay of gene expression data with aCGH data, thus enhancing the power of both these technologies. Members of the six CCSG programs utilized this Resource over the last project period. The Resource was instrumental in enhanced peer-reviewed funding, publications and recruitment efforts. It is anticipated that with the new recruitment into the CSBT Program (Dr Gudkov), MTET Program (Dr Adjei), and Til (Dr Lee) the utilization of the Resource will expand. The Resource is used by all six Programs and 98% of users are CCSG members. $80,355 in CCSG support is requested, representing 11% of the total operating budget.

基因表达资源（GER）是通过成功招募的 莱斯利安·霍索恩（Lesleyann Hawthorn），2001年。自那时以来，资源一直蓬勃发展 满意的用户继续摄影从微阵列的独特方法中得出 实验。它的成功很大程度上是由于致力于为研究人员提供高质量的承诺 微阵列数据具有数据分析的额外优势，因此提供给个人的结果 与大多数基于数组的设施不同，研究人员的形式是可用的。霍索恩博士和GER员工保留 并随着新兴技术的了解，为用户提供广泛的专业知识和选择。这个成功 由大型用户群，出版物和授予的赠款证实。资源在功能上分配 进入基因表达，基因分型和基因组学水平的拷贝数分析。在 单个染色体区域或特定基因，它提供突变/多态性分析，SNP验证 和发现，表达水平定量和甲基化状态的分析。最重要的是 资源为其支持并提供专业知识，时间和资金的所有平台提供数据分析 用于开发新技术。例如，已经开发了外显子阵列分析 允许同时检测基因表达和替代剪接事件。此外， 资源现在能够运行全基因组平铺阵列，该阵列允许最高分辨率的芯片 分析以及基因组DNA中新型转录本的检测。在过去的几年中， 资源一直在研究基因组学数据分析的创新方法。其中之一 以最高密度基因分型的高密度SNP阵列以及同时提供的中心 CGH分析和随附的LOH分析。基因表达一直在使用这些阵列来寻找 染色体区域（CGH）的收益和损失。 SNP映射阵列检测标准的能力 细胞遗传学异常代表了与常规核分型相比的重大进步。 Ger有 与Cowell博士小组（GN）合作开发统计分析工具，以允许基因覆盖 用ACGH数据表达数据，从而增强了这两种技术的能力。六个成员 CCSG程序在上一个项目期间使用了此资源。资源在 加强了同行评审的资金，出版物和招聘工作。可以预见到新的 招募CSBT计划（Gudkov博士），MTET计划（Adjei博士）和TIL（Lee博士）的利用 资源将扩展。所有六个程序都使用该资源，而98％的用户是CCSG 成员。请求$ 80,355的CCSG支持，占运营总预算的11％。