Identification and characterization of microbiome-derived biomarkers via novel and robust systems-based approaches.

通过新颖且强大的基于系统的方法来鉴定和表征微生物组衍生的生物标志物。

• 批准号: RGPIN-2022-05010
• 负责人: Langille, Morgan
• 金额: $ 2.48万
• 依托单位: Dalhousie University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=762088
• 关键词: Identification; characterization; microbiome; derived; biomarkers

Research Goal & Scientific Approach: Microbiomes exist in many different environments and interact with most living species. Over the last decade, the level of interest and research on the microbiome has exploded. However, the rate at which data is being generated is outpacing researchers' current ability to identify robust microbial biomarkers in various environmental and host-associated conditions. Identifying microbial biomarkers from these large surveys is the first crucial step before being further studied to determine how they function and interact with other living organisms. Current methods for characterizing the relationship between types of microbes and their function are still limited and underdeveloped. Therefore, the main goal of this research program is to create a cohesive, visual framework that uses a combination of novel and existing bioinformatic approaches for the identification of new microbial biomarkers. Building upon recent research in our lab, our research program will focus on three main objectives. First, we propose a harmonized approach to characterize the microbial taxa and functions from metagenomic sequencing data by integrating two of the main approaches currently used. This integrated approach would provide the benefits of both techniques while minimizing their individual drawbacks. Second, we will explore and develop new ways to identify functions that are of biological interest in a community, while reducing noise due to technical variation. Third, we will create a novel graphical framework for biomarker discovery and visualization. This framework will include existing statistical and machine learning methods, along with the novel approaches developed in this research program. Unlike previous tools, this framework will focus on utilizing the taxonomic context of all microbial functions to improve the identification of robust biomarkers. These new approaches will be tested and validated using multiple datasets from various sources including environmental, host-associated, and human studies. Importance of Research & Anticipated Outcomes: Given the drastic increase in microbiome studies in the natural sciences and the interest in the discovery of microbial biomarkers from these types of studies, improvement to these approaches will have a profound affect across numerous research areas such as microbiology, genomics, and bioinformatics. In addition, the field of microbiome research requires skills and knowledge across many disciplines and as such training of highly qualified personnel (HQP) through this program will include cross disciplinary training in these diverse disciplines. These highly qualified personnel are in high demand in academic, government, and industry settings. The proposed research program will accelerate microbiome research in Canada and beyond, by tackling current bottlenecks in computational analysis and biomarker discovery from complex microbiome data.

研究目标与科学方法：微生物组存在于许多不同的环境中，并与大多数活物种相互作用。在过去的十年中，对微生物组的兴趣和研究水平爆炸了。但是，生成数据的速率超过了研究人员目前在各种环境和宿主相关条件下识别强大微生物生物标志物的能力。从这些大型调查中鉴定微生物生物标志物是进一步研究以确定其功能和与其他生物体相互作用的第一步。当前表征微生物类型及其功能之间关系的方法仍然有限且不发达。因此，该研究计划的主要目标是创建一个具有凝聚力的视觉框架，该框架结合了新颖和现有的生物信息学方法来识别新的微生物生物标志物。在我们实验室的最新研究基础上，我们的研究计划将重点介绍三个主要目标。首先，我们提出了一种统一的方法来表征微生物分类单元和元基因组测序数据的功能，通过整合当前使用的两种主要方法。这种综合方法将为两种技术提供好处，同时最大程度地减少其个人缺点。其次，我们将探索并开发新的方法来识别社区中具有生物学兴趣的功能，同时由于技术变化而降低噪音。第三，我们将创建一个新颖的图形框架，用于生物标志物发现和可视化。该框架将包括现有的统计和机器学习方法，以及本研究计划中开发的新方法。与以前的工具不同，该框架将着重利用所有微生物功能的分类环境来改善稳健生物标志物的识别。这些新方法将使用来自环境，相关和人类研究在内的各种来源的多个数据集进行测试和验证。研究和预期结果的重要性：鉴于自然科学中的微生物组研究的急剧增加以及对从这些类型的研究发现微生物生物标志物的兴趣，这些方法的改进将在许多研究领域（例如微生物学，基因组学和生物信息学）产生深远的影响。此外，微生物组研究领域还需要许多学科的技能和知识，因此通过该计划对高素质的人员（HQP）进行培训将包括这些不同学科的跨学科培训。这些高素质的人员在学术，政府和行业环境中需求很高。拟议的研究计划将通过从复杂的微生物组数据中解决计算分析和生物标志物发现中的当前瓶颈，从而加速加拿大及其他地区的微生物组研究。