Enriching MGnify Genomes to capture the full spectrum of the microbiota and bolster taxonomic classifications

丰富 MGnify 基因组以捕获微生物群的全谱并支持分类学分类

• 批准号: BB/V01868X/1
• 负责人: Robert Finn
• 金额: $ 118.4万
• 依托单位: European Bioinformatics Institute
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FV01868X%2F1
• 关键词: Enriching; MGnify; Genomes; capture; full

Microbes (viruses, bacterial and single celled eukaryotes) are ubiquitous in nature and perform key roles essential to sustain life, e.g. oxygenation of the planet by marine microbes, soil nutrient cycling to support plant growth or facilitating animal digestion, especially human. Increasing knowledge about microbial ecosystems has accompanied a broadening scope of environments analysed, such as anaerobic digesters, food production systems and the built environment (extending as far as the International Space Station). Metagenomics is a culture independent method that applies modern DNA sequencing technologies to study the genomes of the organisms present in a microbiome. The latest approaches combine advanced sequencing technologies, throughput, and bioinformatics techniques to enable the assembly of short DNA fragments (produced by sequencing machines) into larger chromosomal fragments. Subsequently, these fragments are classified into sets belonging to an individual species, i.e. metagenome assembled genomes (MAGs). While the first MAG was reported in 2004, the first large-scale study applying these techniques was published only in 2015. Since then, there has been an explosion in the number of MAGs reported, which not only provides novel insights into the ~99% of organisms yet to be experimentally cultured but also dramatically expands the Tree of Life. In addition to capturing biodiversity of microbes, these MAGs facilitate a genome centric understanding of their functional role within the community, and how they interact with each other and their surroundings. A substantive section of applied research leverages these findings to restore perturbed microbiomes to a healthy state or to harness the enzymes they encode.This proposal focuses on MGnify, a resource that already performs four major roles in microbial community research: (i) it facilitates the capture of petabytes of sequence data being generated currently; (ii) it provides users access to the computational resources to conduct metagenomic assembly; (iii) it generates new knowledge by analysing microbiome derived sequence data and presenting this via a website and API to the user community; (iv) it has initiated capture of prokaryotic MAGs. In this proposal, we will extend MGnify to recover Eukaryotic MAGs using innovative new methodologies and capture the viruses in the MGnify assemblies. These non-redundant catalogues of Eukaryotic and viral genomes will be used to supplement the existing MGnify genomes. To perfect the MAG generation process, we propose to develop additional pipelines that will identify and remove the contaminants found in the prokaryotic MAGs. In addition to generating high-quality MAGs that cover the entire range of microbial taxa, we will harmonise efforts with the Genome Taxonomy Database (GTDB) to ensure that this newly discovered bacterial diversity is properly represented therein, as it is one of the most widely used resources for taxonomic classification. Underpinning this, we will enhance the metagenomic sequence submission systems to better cater for all data types and improve the internal mechanisms for data exchange, so that MGnify can perform submission on behalf of the users and gain access to all data types, whether the data is public or private (prepublication), given the appropriate user consent. Finally, in addition to updating the reference databases in our analysis pipelines, we will also improve the annotation of carbohydrate metabolism enzymes, which are poorly represented in databases currently.Collectively, these developments will reinforce MGnify's crucial importance to the microbiome research community. It will serve as the foundational knowledgebase that propels integrative microbiome research and its translation to real world applications.

微生物（病毒、细菌和单细胞真核生物）在自然界中无处不在，并发挥着维持生命所必需的关键作用，例如。海洋微生物给地球充氧，土壤养分循环支持植物生长或促进动物消化，尤其是人类消化。随着对微生物生态系统的了解不断增加，分析的环境范围也不断扩大，例如厌氧消化池、粮食生产系统和建筑环境（最远延伸至国际空间站）。宏基因组学是一种独立于培养物的方法，应用现代 DNA 测序技术来研究微生物组中存在的生物体的基因组。最新的方法结合了先进的测序技术、通量和生物信息学技术，能够将短 DNA 片段（由测序仪产生）组装成更大的染色体片段。随后，这些片段被分类为属于单个物种的集合，即宏基因组组装基因组（MAG）。虽然第一个 MAG 是在 2004 年报道的，但第一个应用这些技术的大规模研究直到 2015 年才发表。从那时起，报道的 MAG 数量出现爆炸式增长，这不仅为 ~99%尚未进行实验培养的生物体，但也极大地扩展了生命之树。除了捕获微生物的生物多样性之外，这些 MAG 还有助于以基因组为中心了解微生物在群落中的功能作用，以及它们如何与彼此及其周围环境相互作用。应用研究的一个实质性部分利用这些发现将受干扰的微生物组恢复到健康状态或利用它们编码的酶。该提案重点关注 MGnify，这是一种已经在微生物群落研究中发挥四个主要作用的资源：(i) 它促进捕获当前生成的 PB 级序列数据； (ii) 它为用户提供计算资源以进行宏基因组组装； (iii) 它通过分析微生物组衍生的序列数据并通过网站和 API 向用户社区呈现该数据来生成新知识； (iv) 它已开始捕获原核 MAG。在本提案中，我们将扩展 MGnify 以使用创新的新方法恢复真核 MAG，并捕获 MGnify 组件中的病毒。这些非冗余的真核和病毒基因组目录将用于补充现有的 MGnify 基因组。为了完善 MAG 生成过程，我们建议开发额外的管道来识别和去除原核 MAG 中发现的污染物。除了生成涵盖整个微生物类群范围的高质量 MAG 之外，我们还将与基因组分类数据库 (GTDB) 协调努力，以确保新发现的细菌多样性在其中得到正确体现，因为它是最广泛的细菌多样性之一。使用资源进行分类学分类。在此基础上，我们将增强宏基因组序列提交系统，以更好地满足所有数据类型的需求，并完善数据交换的内部机制，以便MGnify可以代表用户进行提交并获得所有数据类型的访问权限，无论数据是公开或私有（预发布），前提是适当的用户同意。最后，除了更新我们分析流程中的参考数据库外，我们还将改进碳水化合物代谢酶的注释，目前这些酶在数据库中的代表性较差。总的来说，这些发展将加强 MGnify 对微生物组研究界的至关重要性。它将作为基础知识库，推动综合微生物组研究及其转化为现实世界的应用。

项目成果

MGnify Genomes: A Resource for Biome-specific Microbial Genome Catalogues.

MGnify Genomes：生物群系特定微生物基因组目录的资源。

• DOI: http://dx.10.1016/j.jmb.2023.168016
• 发表时间: 2023
• 期刊: Journal of molecular biology
• 影响因子: 5.6
• 作者: Gurbich TA

The European Nucleotide Archive in 2022.

2022 年欧洲核苷酸档案。

• DOI: http://dx.10.1093/nar/gkac1051
• 发表时间: 2023
• 期刊: Nucleic acids research
• 影响因子: 14.9
• 作者: Burgin J

Establishing the ELIXIR Microbiome Community

建立 ELIXIR 微生物群落

• DOI: http://dx.10.12688/f1000research.144515.1
• 发表时间: 2024
• 期刊: F1000Research
• 作者: Finn R

MGnify: the microbiome sequence data analysis resource in 2023.

MGnify：2023 年微生物组序列数据分析资源。

• DOI: http://dx.10.1093/nar/gkac1080
• 发表时间: 2023
• 期刊: Nucleic acids research
• 影响因子: 14.9
• 作者: Richardson L