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

基本信息

批准号：
8237124
负责人：
Rafael Angel Irizarry
金额：
$ 37.33万
依托单位：
JOHNS HOPKINS UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2007
资助国家：
美国
起止时间：
2007-09-02 至 2014-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8237124
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

项目摘要

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. PUBLIC HEALTH RELEVANCE: The research community has amassed substantial knowledge and developed reliable data analysis tools that effectively deal with bias and systematic error in microarray technology. The technology is 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.

描述（由申请人提供）：微阵列技术已成为医学和基本生物学研究的标准工具。该技术的主要成就是FDA认可的乳腺癌复发测定法的成功开发使您有可能识别出手术后有远处复发风险的患者。弗朗西斯·柯林斯（Francis Collins）认为，微疗法也已成为基因组广泛关联研究（GWAS）的标准工具，该工具已导致“大量的常见DNA变异，这些变化在患有心脏病，糖尿病，癌症，癌症或自身免疫性等常见疾病的风险中起着作用”。在过去的两年中，所有PubMed出版物中约有一半的出版物发表（2009 - 2010年期间出版了15,275；在2009年之前出版了15,926。因此，我们预计，学术界和工业的实验室将在几年中继续依靠这些技术，并且制造商将继续快速开发新产品。借助微阵列技术，需要采取许多关键步骤将生物学家和临床医生依靠的数据转换为数据。这些数据操作称为预处理，对最终测量的质量以及依赖它们的研究产生了巨大影响。但是，典型的分析软件无法提供对原始探针级数据的访问。我们的小组以前已经证明，相对于制造商提供的默认工具引入的临时程序，替代方法的使用可以大大提高准确性和精度。通过我们的生物导体包装套件，我们为统计计算提供了一个灵活的环境，它仍然是用于分析微阵列探针级数据的最广泛使用的工具。在过去的十年中，我们的许多研究都致力于了解高通量技术中可能出现的偏见和系统错误。系统的错误掩盖了结果，阻碍了发现，并促进了不可再现的发现。消除系统错误的挑战并非隔离到基于数组的技术。例如，已经报道了与微阵列中遇到的相似问题用于第二代测序原始数据。对于微阵列，我们积累了大量的知识库和数据分析工具，以有效地预处理原始数据，从而使转化研究和临床应用的技术素养。我们的软件工具部分促进了这一成就，并将在微阵列技术驱动的下一个研究时期发挥重要作用。因此，我们通过建议继续提供我们成功且广泛使用的资源来响应继续开发和维护软件的申请请求（RFA）。公共卫生相关性：研究界已经积累了大量知识，并开发了可靠的数据分析工具，这些工具有效地应对微阵列技术的偏见和系统错误。该技术是转化研究和临床应用的主要水平。我们的软件工具部分促进了这一成就，并将在微阵列技术驱动的下一个研究时期发挥重要作用。