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.

研究目标和科学方法：微生物存在于许多不同的环境中，并与大多数生物物种相互作用。在过去的十年中，人们对微生物组的兴趣和研究水平呈爆炸式增长。然而，数据生成的速度超出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。从这些大型调查中识别微生物生物标志物是进一步研究以确定它们如何发挥作用以及与其他生物体相互作用的关键第一步。目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有凝聚力的视觉框架，结合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。基于我们实验室最近的研究，我们的研究计划将重点关注三个主要目标。首先，我们提出了一种统一的方法，通过整合目前使用的两种主要方法来表征宏基因组测序数据中的微生物分类群和功能。这种集成方法将提供这两种技术的优点，同时最大限度地减少它们各自的缺点。其次，我们将探索和开发新的方法来识别群落中具有生物学意义的功能，同时减少由于技术变化而产生的噪音。第三，我们将为生物标志物发现和可视化创建一个新颖的图形框架。该框架将包括现有的统计和机器学习方法，以及本研究项目中开发的新方法。与以前的工具不同，该框架将侧重于利用所有微生物功能的分类背景来改进稳健生物标志物的识别。这些新方法将使用来自各种来源的多个数据集进行测试和验证，包括环境、宿主相关和人类研究。研究和预期结果的重要性：鉴于自然科学中微生物组研究的急剧增加以及人们对从这些类型的研究中发现微生物生物标志物的兴趣，这些方法的改进将对微生物学、微生物学、微生物学等众多研究领域产生深远的影响。基因组学和生物信息学。此外，微生物组研究领域需要跨多个学科的技能和知识，因此通过该计划对高素质人才（HQP）的培训将包括这些不同学科的跨学科培训。学术界、政府和工业界对这些高素质人才的需求量很大。拟议的研究计划将通过解决当前复杂微生物组数据中计算分析和生物标志物发现的瓶颈，加速加拿大及其他地区的微生物组研究。