Software for the statistical analysis of microarray probe level data

用于微阵列探针水平数据统计分析的软件

• 批准号: 8333372
• 负责人: Rafael Angel Irizarry
• 金额: $ 39.53万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-07-01 至 2014-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8333372
• 关键词: Academia; Achievement; Address; Adoption; Algorithms; Anus; Autoimmunity; Basic Science; Bioconductor; Biological Assay; Biological Process; Biology; Communication; Communities; Complex; Computer software; DNA; DNA copy number; Data; Data Analyses; Data Set; Development; Diabetes Mellitus; Disease; Distant; Environment; Excision; Exons; FDA approved; Gene Expression; Generations; Genes; Genomics; Genotype; Heart Diseases; Human Resources; Individual; Industry; Knowledge; Laboratories; Literature; Maintenance; Malignant Neoplasms; Manufacturer Name; Manuscripts; Mathematics; Measurement; Measures; Medical; Memory; Methodology; Methylation; MicroRNAs; Microarray Analysis; Modeling; Morphologic artifacts; Names; Oligonucleotides; Operative Surgical Procedures; Paper; Patients; Play; Procedures; Process; PubMed; Publications; Publishing; Recurrence; Relative (related person); Reporting; Reproducibility; Request for Applications; Research; Research Infrastructure; Resources; Risk; Role; Science; Software Tools; Solutions; Speed; Statistical Computing; Structure; Systematic Bias; Technology; Testing; Time; Translational Research; Variant; base; cancer recurrence; clinical application; design; flexibility; genome wide association study; high throughput technology; improved; knowledge base; malignant breast neoplasm; probe-level data; research in practice; response; software development; statistics; tool

DESCRIPTION (provided by applicant): Microarray technology has become a standard tool in medical science and basic biology research. A major achievement of the technology is the successful development of an FDA approved breast cancer recurrence assay making it possible to identify patients at risk of distant recurrence following surgery. Microarrys have also become the standard tool of genome wide association studies (GWAS) which, according to Francis collins, have led to "an astounding number of common DNA variations that play a part in the risk of developing common diseases such as heart disease, diabetes, cancer or autoimmunity". Approximately one half of all PubMed publications citing microarrays were published during the last 2 years (15,275 published during 2009-2010; 15,926 published prior to 2009). We therefore expect that laboratories in academia and industry will continue to rely on these technologies for several years and that manufacturers will continue to develop new products at a rapid pace. With microarray technologies, a number of critical steps are required to convert raw measures into the data relied upon by biologists and clinicians. These data manipulations referred to as preprocessing, have enormous influence on the quality of the ultimate measurements and on the studies that rely upon them. However, the typical analysis software does not provide access to raw probe-level data. Our group has previously demonstrated that the use of alternative methodology can substantially improve accuracy and precision, relative to ad-hoc procedures introduced by default tools provided by the manufacturers. Through our suite of Bioconductor packages, we offer a flexible environment for statistical computing that continues to be the most widely used tool for the analysis of microarray probe-level data. During the last decade, much of our research has been dedicated to understanding the bias and systematic errors that can arise in high-throughput technologies. Systematic errors obscure results, thwart discovery, and contribute to findings that are not reproducible. The challenges for removing systematic errors are not isolated to array-based technologies. For example, similar problems to those encountered in microarrays have been reported for second generation sequencing raw data. For microarrays, we have amassed a substantial knowledge base and data analysis tools to effectively preprocess raw data, making the technology prime for translational research and clinical applications. Our software tools have partly facilitated this achievement and will play an important role in the promising next period of research driven by microarray technology. We are therefore responding to the request for application (RFA) for the continued development and maintenance of software, by proposing to continue to provide our successful and widely used resources.

描述（由申请人提供）：微阵列技术已成为医学科学和基础生物学研究的标准工具。该技术的一项重大成就是成功开发了 FDA 批准的乳腺癌复发检测方法，使识别手术后有远处复发风险的患者成为可能。微阵列也已成为全基因组关联研究 (GWAS) 的标准工具，根据弗朗西斯·柯林斯 (Francis Collins) 的说法，该研究导致了“数量惊人的常见 DNA 变异，这些变异在导致心脏病、糖尿病等常见疾病的风险中发挥了作用” 、癌症或自身免疫”。所有引用微阵列的 PubMed 出版物中大约有一半是在过去 2 年发表的（2009-2010 年期间发表了 15,275 篇；2009 年之前发表了 15,926 篇）。因此，我们预计学术界和工业界的实验室将在数年内继续依赖这些技术，并且制造商将继续快速开发新产品。使用微阵列技术，需要许多关键步骤才能将原始测量结果转换为生物学家和临床医生所依赖的数据。这些数据操作（称为预处理）对最终测量的质量以及依赖这些测量的研究具有巨大的影响。然而，典型的分析软件不提供对原始探针级数据的访问。我们的团队之前已经证明，相对于制造商提供的默认工具引入的临时程序，使用替代方法可以大大提高准确性和精确度。通过我们的 Bioconductor 软件包套件，我们为统计计算提供了灵活的环境，该环境仍然是微阵列探针级数据分析中使用最广泛的工具。在过去的十年中，我们的大部分研究致力于了解高通量技术中可能出现的偏差和系统错误。系统性错误会掩盖结果，阻碍发现，并导致发现结果不可重复。消除系统误差的挑战不仅仅局限于基于阵列的技术。例如，第二代测序原始数据也报告了与微阵列中遇到的类似问题。对于微阵列，我们积累了丰富的知识库和数据分析工具，可以有效地预处理原始数据，使该技术成为转化研究和临床应用的主要技术。我们的软件工具在一定程度上促进了这一成就，并将在微阵列技术驱动的下一阶段的有希望的研究中发挥重要作用。因此，我们响应持续开发和维护软件的申请请求 (RFA)，建议继续提供我们成功且广泛使用的资源。