Data Analysis Tools for Emerging High-Throughput Technologies

适用于新兴高通量技术的数据分析工具

• 批准号: 10461727
• 负责人: Rafael Angel Irizarry
• 金额: $ 59.68万
• 依托单位: DANA-FARBER CANCER INST
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-05-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10461727
• 关键词: Adoption; Affect; Algorithms; Basic Science; Biological; Biomedical Research; Complex; Computer software; DNA Methylation; DNA Sequence; Data; Data Analyses; Data Analytics; Data Set; Database Management Systems; Development; FarGo; Gene Expression; Goals; Investigation; Laboratories; Measurement; Measures; Methodology; Molecular; National Research Council; Nucleic Acids; Outcome; Protocols documentation; Province; Publications; Research Personnel; Sampling; Scanning; Signal Transduction; Source; Statistical Methods; Systematic Bias; Techniques; Technology; Translational Research; Variant; Work; base; clinical application; disease phenotype; flexibility; frontier; high throughput technology; indexing; interest; precision medicine; success; tool

Project Summary Biomedical research and the basic sciences are increasingly dependent on high-throughput technologies that have the ability to simultaneously measure thousands of nucleic acid molecules in a sample. In combination with ingenious laboratory protocols, these technologies have permitted unprecedented ways of studying the molecular basis of disease and phenotypic variation. As a result of the increasing adoption of these technologies, more investigations rely on complex datasets and require the development of new statistical techniques to adequately interpret data. Today, high-throughput technologies applications go far beyond their original task of studying DNA sequence itself and also include the measurement of quantitative and dynamic outcomes such as gene expression levels and DNA methylation (DNAm) status. These quantitative and dynamic outcomes introduce levels of variability that give rise to further data analytic challenges related to distinguishing unwanted sources of variability from bio- logically relevant signals. Furthermore, when measuring these quantitative outcomes, data are subject to severe technological and biological biases that can substantially impact downstream analyses. Our group has previously demonstrated that statistical methodology can provide great improvements over ad-hoc algorithms offered as de- faults by technology developers. Our highly cited statistical methodology and our widely used software demonstrate the success of our work. The National Research Council's Frontiers in Massive Data Analysis publication states that, “the challenges for massive data go beyond the storage, indexing, and querying that have been the province of classical database systems and instead hinge on the ambitious goal of inference”. Inference is particularly relevant in biomedical applications since we often look to draw conclusions based on observed differences between groups in the presence of within group variability. Two particularly challenging tasks relate to performing valid inference when 1) we perform scans over large spaces to identify small regions of interests and 2) the data is affected by unexpected systematic bias or batch effects. We will focus on these two general challenges. Our specific proposal is to work on the most urgent needs of researchers facing new challenges as they increasingly rely on high-throughput techniques. We will leverage the expertise of our collaborators to prioritize projects. We greatly appreciate the flexibility permitted by the R35 mechanism as it will help us maximize the impact of our work.

项目摘要 生物医学研究和基本科学越来越依赖于具有的高通量技术 能够同时测量样品中数千个核酸分子。结合巧妙的 实验室协议，这些技术允许前所未有的方法研究分子基础 疾病和表型变异。由于这些技术的采用越来越多，更多的调查 依靠复杂的数据集并需要开发新的统计技术来充分解释数据。 如今，高通量技术应用程序远远超出了其研究DNA序列的最初任务 本身，还包括测量定量和动态结果，例如基因表达水平和 DNA甲基化（DNAM）状态。这些定量和动态结果引入了可变性水平 引起进一步的数据分析挑战，与将不良变异源与生物区分开 逻辑相关信号。此外，在测量这些定量结果时，数据会遭受严重的影响 可能会影响下游分析的技术和生物偏见。我们的小组以前有 证明统计方法论可以提供比临时算法的巨大改进 技术开发人员的故障。我们高度引用的统计方法论和我们广泛使用的软件证明了 我们工作的成功。 国家研究委员会在大规模数据分析中的前沿出版物指出，“挑战 对于大量数据，超越了作为古典数据库省的存储，索引和查询 系统，而取决于雄心勃勃的推理目标”。推论在生物医学中特别相关 应用程序，因为我们经常希望根据在存在下的组之间观察到的差异来得出结论 组内变异性。两个特别挑战任务与执行有效推断有关时的任务1） 在大空间上进行扫描以识别兴趣的小区域，2）数据伴随着意外 系统偏见或批处理效应。我们将专注于这两个一般挑战。我们的特殊建议是从事 研究人员越来越依赖高通量技术，面临新挑战的最紧迫需求。 我们将利用合作者的专业知识来确定项目的优先级。我们非常感谢灵活性 R35机制允许，因为它将有助于我们最大程度地发挥作品的影响。