Integrated analysis of time-course multi-type Next Generation Sequencing data and biomarker discovery using multiple studies

使用多项研究对时程多类型下一代测序数据和生物标志物发现进行综合分析

• 批准号: RGPIN-2017-04722
• 负责人: Zhang, Xuekui
• 金额: $ 1.38万
• 依托单位: University of Victoria
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=711172
• 关键词: Integrated; analysis; time; course; multi

Background (The needs) 1. Tremendous volumes of genomics/epigenomics data in public resources are generated with substantial effort of the research community, e.g. the Encyclopedia of DNA Elements (ENCODE) project, The Cancer Genome Atlas (TCGA) project, and the Gene Expression Omnibus (GEO) data repository. How to best utilize such valuable information, becomes critical to conduct biological research in the era of information explosion. Integrate such information in new research needs development of statistical methodologies and software. 2. Ongoing developments in next-generation sequencing (NGS) technologies enable biological experiments to be conducted on much larger scales. This has created new opportunities in biological and medical research, while posing new challenges in analyzing these data. Various NGS data of new and complex structures are continuously generated in different applications of NGS technologies. To extract useful information from such data, new statistical methodologies and software are urgently needed. 3. Precision Medicine is a young but rapidly advancing field of healthcare that involves tailoring medical treatments for individual patients based on the context of a patient's biomarkers. Thus, discovery of biomarkers is the first and most important step for the success of precision medicine. High-throughput technologies (e.g. NGS) enable researchers to cost-efficiently generate data from large numbers of candidate biomarkers. This potentially improves discovery by widening the range of candidates, but requires paying more attention to multiple testing problems. When conducting a biomarker discovery study, similar studies might be conducted by other teams or already published in data repositories mentioned above. How to integrate such information also need development of statistical methodologies. Proposed research My proposed research focuses on developing methods and software in: (1) integrated analysis of time-course multi-type NGS data; (2) biomarker discovery from integrated analysis of multiple studies. Topic (1) is to address the needs 1 and 2 described above, it will provide a software tool for integrated analysis of temporal multi-type NGS data, which can be either from a single study or from multiple studies available in public data repositories. It will also provide an approach to integrate temporal data generated with different time systems, and to make them comparable via appropriate standardization and adjustments. Topic (2) is to address the needs 1 and 3 described above. It will allow researchers to conduct biomarker discovery from individual studies and to make an integrate decision based on all results. Our goal is to maximize power of biomarker discover, while keep the false positive rate under control. My research will be implemented in R/Bioconductor software, and all researchers can use it in their research.

需求（Needs） 1.公共资源中大量的基因组学/表观基因组学数据是在研究界的大量努力下产生的，例如DNA元件百科全书（ENCODE）项目、癌症基因组图谱（TCGA）项目和基因表达综合数据库（GEO）。如何最好地利用这些宝贵的信息，成为在信息爆炸时代进行生物研究的关键。将这些信息纳入新的研究需要开发统计方法和软件。 2.下一代测序（NGS）技术的持续发展使生物实验能够在更大的规模上进行。这为生物和医学研究创造了新的机会，同时也为分析这些数据带来了新的挑战。在NGS技术的不同应用中，不断产生各种新的复杂结构的NGS数据。为了从这些数据中提取有用的信息，迫切需要新的统计方法和软件。 3.精准医学是一个年轻但发展迅速的医疗保健领域，涉及根据患者生物标志物的背景为个体患者量身定制医学治疗。因此，生物标志物的发现是精准医疗成功的第一步也是最重要的一步。高通量技术（例如NGS）使研究人员能够从大量候选生物标志物中以成本效益的方式生成数据。这可能通过扩大候选者的范围来提高发现，但需要更多地关注多个测试问题。在进行生物标志物发现研究时，类似的研究可能由其他团队进行，或者已经在上述数据库中发表。如何整合这些信息还需要制定统计方法。 拟议研究 我建议的研究重点是开发方法和软件： (1)时程多类型NGS数据的综合分析; (2)从多项研究的综合分析中发现生物标志物。 主题（1）是为了解决上述需求1和2，它将提供一个软件工具，用于集成分析时间多类型的NGS数据，这些数据可以来自单个研究，也可以来自公共数据库中的多个研究。它还将提供一种方法，将不同时间系统生成的时间数据整合起来，并通过适当的标准化和调整使其具有可比性。 主题（2）是解决上述需求1和3。它将允许研究人员从个体研究中发现生物标志物，并根据所有结果做出综合决策。我们的目标是最大限度地提高生物标志物发现的能力，同时控制假阳性率。 我的研究将在R/Bioconductor软件中实现，所有研究人员都可以在研究中使用它。