Statistical And Computational Methods For Gene Expression and Proteomic Analysis

基因表达和蛋白质组分析的统计和计算方法

• 批准号: 7966728
• 负责人: peter j munson
• 金额: $ 52.54万
• 依托单位: CENTER FOR INFORMATION TECHNOLOGY
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/7966728
• 关键词: Address; Affect; Age related macular degeneration; Antineoplastic Agents; Attention; Bioinformatics; Biological; Biological Assay; Biological Models; Biology; Biomedical Research; Blood; Caring; Cells; Clinical; Collaborations; Complementary DNA; Computer software; Computing Methodologies; Core Facility; Data; Data Analyses; Data Set; Databases; Defect; Detection; Development; Educational workshop; Event; Exons; Experimental Designs; Feasibility Studies; Gene Expression; Gene Expression Profile; Genes; Genetic Transcription; Genome; Genomics; Goals; Heart; Heart Diseases; Image; Immune; Individual; Inflammatory Response; Knockout Mice; Laboratories; Lead; Measurement; Measures; Medical; Methodology; Methods; Microarray Analysis; Modeling; Molecular Profiling; National Heart, Lung, and Blood Institute; National Human Genome Research Institute; National Institute of Allergy and Infectious Disease; National Institute of Child Health and Human Development; National Institute of Dental and Craniofacial Research; National Institute of Diabetes and Digestive and Kidney Diseases; National Institute of Drug Abuse; National Institute of Neurological Disorders and Stroke; Oligonucleotides; Pattern; Peripheral Blood Mononuclear Cell; Pineal gland; Process; Property; Proteins; Proteomics; Quality Control; RNA Splicing; Rattus; Reading; Reproducibility; Research; Research Personnel; Retrieval; Sampling; Source; Spliced Genes; Structure of retinal pigment epithelium; Surveys; Techniques; Technology; Testing; Tissues; Training; Tube; Uncertainty; United States National Institutes of Health; Update; Work; Writing; base; design; high throughput analysis; image processing; lymphoblastoid cell line; new technology; next generation; novel; open source; research study; response; tool

Gene expression measurement using cDNA and oligo arrays continues to be a popular and useful technology for genomic analysis. High throughput methods for measuring protein concentrations are also increasing in popularity. One of the more challenging problems results from the large volume of data generated in these experiments. Image capture, processing, interpretation and quantification remain important fundamental issues. Quality control and experimental design must be carefully addressed. Many problematic statistical, image processing and bioinformatics issues remain and are addressed in this project. We have focused our research attention on developing new methods for analysis of gene splicing, based on microarray platforms especially designed for the purpose. We have developed and enhanced an analysis strategy using statistical ANOVA for efficient detection of potential splice events, and applied this technique to major publicly available data sets. We have also recently applied these approaches to studies of a mouse knock-out model, to a model of the inflammatory response of immune cells, and to the response of cells to an anti-cancer agent. Two measurement platforms, the Affymetrix exon array and the ExonHit junction probe array are being studied. The entire Framiningham Heart Survey SABRe project has begun to use this new technology, which increases the available transcriptional information by roughly a factor of 10, compared to standard expression arrays. For almost a decade, our group has functioned as the "statistical analysis core" for a high-volume microarray laboratory in CCMD/CC. All microarray studies by this group now pass through our analysis pipeline. We now also perform as the analysis core for the microarray core facility for the NHLBI, more than tripling the throughput of microarray studies into our database and pipeline. This "core" facility has generated more than a dozen new collaborative projects per year, in which our staff are primarily responsible for statistical analysis and interpretation of microarray data. This year, our group has completed the analysis of the first component of the multi-year, Framingham Heart Survey SABRe project, in which ultimately about 5,000 biological samples will be analyzed for microarray expression profiles. In combination with clinical and other laboratory data, this dataset will no doubt lead to major advances in the understanding of expression signatures and heart disease. The first, feasibility study analyzed samples from 50 individuals, with four blood derived sample types per individual; PBMC, lymphoblastoid cell lines, PaxGene tubes and buffy coat. The technical goal is to chose the best, or at least usable sample types for analysis in the larger study. The result shows that PBMC and PaxGene tubes are roughly equally good in the quality of results. Affordable, high-quality software availability has been one of the bottlenecks in analysis of microarray data. We have continued development of the "MSCL Analyst's Toolbox" to address this need. Built upon the commercial statistical package JMP, this toolbox allows investigators to download Affymetrix microarray data from a central database, normalize and transform the data, inspect it for a variety of outliers or defects, perform a variety of statistical tests to select relevant genes affected in the experiment, and then visualize and classify various patterns of gene expression. Because our Toolbox is written in open source scripts, its statistical tests can be modified as needed to conform to novel or unique experimental designs. In collaboration with over forty investigators in CC, NHLBI, NIDCR and other ICs, this tool has been applied to several dozen microarray studies. One-day and two-day Toolbox training workshops are regularly presented on the NIH campus. In a major NIH-wide project, we maintain a database for storage, retrieval and analysis of Affymetrix microarrays, NIHLIMS. As part of this collaboration, we have created a data analysis pipeline and bioinformatics toolset, including both commercial and freely available software. The database currently stores information from over 4000 microarrays. Our downloadable tool set (MSCL Analyst's Toolbox) is now mature, widely tested and applied in numerous studies. Working with investigators in NCI, CC, NHLBI, NINDS, NIAID, NHGRI, NICHD, NIA, NIDDK, NIDA we have developed, customized and applied this software for the analysis of microarray based studies. We also maintain a quarterly-updated set of annotation files for use with Affymetrix data, in a format for convenient download and use by our collaborators. In another study with investigators in NEI, we are evaluating the utility of several biological models for age-related macular degeneration and for retinal pigment epithelium (RPE) tissue development, using microarray technology. Preliminary results show that RPE tissues from several sources can be clearly distinguished from non-RPE, by the increased expression of a number of RPE-specific genes. We are now investigating the properties of RNAseq, a method for more accurately assessing the transcriptome using next-generation sequencing technology. In one project, with investigators in NHGRI, we are assessing the reproducibility, both within subject, and within lane, of the methodology. In another, we have analyzed the transcriptome of rat pineal gland, both day and nightime. We have found a dramatic number of new unexpected differences as well as dozens of expression differences already known from microarray analysis. Indeed, about 50% of the "reads" generated in this study do not belong to well-document rat genes, and are presumably a result of novel transcription from portions of the genome not yet annotated.

使用 cDNA 和寡核苷酸阵列进行基因表达测量仍然是基因组分析中流行且有用的技术。 用于测量蛋白质浓度的高通量方法也越来越受欢迎。更具挑战性的问题之一来自这些实验中生成的大量数据。 图像捕获、处理、解释和量化仍然是重要的基本问题。必须仔细解决质量控制和实验设计。许多有问题的统计、图像处理和生物信息学问题仍然存在，并在该项目中得到解决。 我们的研究重点是开发基于专门为此目的设计的微阵列平台的基因剪接分析新方法。 我们开发并增强了一种使用统计方差分析来有效检测潜在剪接事件的分析策略，并将该技术应用于主要的公开数据集。 我们最近还将这些方法应用于小鼠基因敲除模型、免疫细胞炎症反应模型以及细胞对抗癌药物的反应的研究。 正在研究两个测量平台：Affymetrix 外显子阵列和 ExonHit junction 探针阵列。 整个 Framiningham Heart Survey saber 项目已开始使用这项新技术，与标准表达阵列相比，该技术将可用的转录信息增加了大约 10 倍。 近十年来，我们小组一直充当 CCMD/CC 的大容量微阵列实验室的“统计分析核心”。该小组的所有微阵列研究现在都通过我们的分析流程。我们现在还充当 NHLBI 微阵列核心设施的分析核心，将我们数据库和管道中的微阵列研究吞吐量增加了两倍多。这个“核心”设施每年产生十多个新的合作项目，其中我们的工作人员主要负责微阵列数据的统计分析和解释。 今年，我们小组完成了多年弗雷明汉心脏调查 saber 项目第一部分的分析，最终将分析约 5,000 个生物样本的微阵列表达谱。 结合临床和其他实验室数据，该数据集无疑将在理解表达特征和心脏病方面带来重大进展。 第一项可行性研究分析了 50 个人的样本，每个人有四种血液样本类型； PBMC、类淋巴母细胞系、PaxGene 管和血沉棕黄层。 技术目标是选择最好的或至少可用的样本类型以在更大的研究中进行分析。 结果表明，PBMC 和 PaxGene 管的结果质量大致相同。 经济实惠、高质量的软件可用性一直是微阵列数据分析的瓶颈之一。 我们不断开发“MSCL Analyst's Toolbox”来满足这一需求。 该工具箱以商业统计包 JMP 为基础，允许研究人员从中央数据库下载 Affymetrix 微阵列数据，对数据进行标准化和转换，检查其是否有各种异常值或缺陷，执行各种统计测试以选择受影响的相关基因。实验，然后对基因表达的各种模式进行可视化和分类。 由于我们的工具箱是用开源脚本编写的，因此可以根据需要修改其统计测试，以符合新颖或独特的实验设计。 与 CC、NHLBI、NIDCR 和其他 IC 的 40 多名研究人员合作，该工具已应用于数十项微阵列研究。 NIH 校园定期举办为期一天和两天的 Toolbox 培训研讨会。 在 NIH 范围内的一个主要项目中，我们维护一个用于存储、检索和分析 Affymetrix 微阵列的数据库 NIHLIMS。作为此次合作的一部分，我们创建了数据分析管道和生物信息学工具集，包括商业软件和免费软件。该数据库目前存储了 4000 多个微阵列的信息。我们的可下载工具集（MSCL Analyst's Toolbox）现已成熟，经过广泛测试并应用于众多研究。我们与 NCI、CC、NHLBI、NINDS、NIAID、NHGRI、NICHD、NIA、NIDDK、NIDA 的研究人员合作，开发、定制和应用该软件来分析基于微阵列的研究。我们还维护一组每季度更新的注释文件，用于 Affymetrix 数据，其格式便于我们的合作者下载和使用。 在与 NEI 研究人员进行的另一项研究中，我们正在使用微阵列技术评估几种生物模型在年龄相关性黄斑变性和视网膜色素上皮 (RPE) 组织发育中的效用。 初步结果表明，通过增加一些 RPE 特异性基因的表达，可以将多种来源的 RPE 组织与非 RPE 清楚地区分开来。 我们现在正在研究 RNAseq 的特性，这是一种使用下一代测序技术更准确地评估转录组的方法。 在一个项目中，我们与 NHGRI 的研究人员一起评估该方法在受试者内和泳道内的可重复性。 在另一项研究中，我们分析了白天和夜间大鼠松果体的转录组。 我们发现了大量新的意想不到的差异，以及通过微阵列分析已知的数十种表达差异。 事实上，本研究中产生的约 50% 的“读数”不属于记录良好的大鼠基因，并且可能是尚未注释的基因组部分的新转录的结果。