2020BBSRC-NSF/BIO: REDEFINE - Development of efficient, large-scale metagenomics sequence comparison algorithms to facilitate novel genomic insights

2020BBSRC-NSF/BIO：REDEFINE - 开发高效、大规模的宏基因组序列比较算法，以促进新的基因组见解

• 批准号: BB/W002965/1
• 负责人: Robert Finn
• 金额: $ 63.57万
• 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%2FW002965%2F1
• 关键词: 2020BBSRC; NSF; BIO; REDEFINE; Development

Microbes are ubiquitous and perform essential roles that help sustain life on earth, for e.g. environmental oxygenation, soil nutrient cycling to support plant growth or facilitating animal digestion. They cause many diseases in plants and animals and have the ability to rapidly evolve to exploit new niches and/or combat antimicrobials. A relatively new field, metagenomics is a culture independent method that applies sophisticated DNA sequencing technologies to analyse the total microbial genetic material from any environment. It is now possible to reassemble the millions of short DNA sequences to produce representations of the microbial genomes in a sample, termed metagenome assembled genomes (MAGs), especially for bacteria. While this approach remains computationally expensive, the computer algorithms used to recover these genomes have been substantially improved to increase accuracy of MAGs. Just in the past five years, many large-scale studies, including our own, have successfully applied these techniques to cumulatively generate millions of MAGs. This has provided scientists with novel insights into ~99% of organisms yet to be experimentally cultured and dramatically expanded the Tree of Life. These MAGs are reshaping our understanding of microbial community structure and the functional capacities of constituent members. This explosion in MAG numbers nevertheless presents new challenges. These large-scale analyses can generate genomes at magnitudes that match GenBank's large genome collection, which is derived from traditional techniques of sequencing experimentally isolated microbes. Such genome collections have taken decades to build and are managed by large data centres. Yet, there is now the need for groups to routinely perform comparisons between new MAG collections and such large reference genome collections. We propose to use a particular class of algorithm called MinHash, which rapidly estimates similarity between two sets based on the number of shared entities, in our case short sequences. Most implementations of this approach have focused on the rapid comparison of one genome to another. In this proposal, we aim to use a range of computational techniques to enable the comparison of a large query dataset to a large reference database, with the purview of being applied to microbial genomes, MAG collections and metagenomic sequences. We will develop and apply this tool to a range of datasets, particularly those housed in MGnify, a leading database of metagenomic data. The key applications are the identification of errors in MAGs which were introduced by the computational methods, data reduction by identifying duplicate MAGs between datasets, the rapid incorporation of MAGs into catalogues of genomes that have been found in a particular environment, taxonomic classification of MAGs (by converting similarity distances to evolutionary distances), and the profiling of metagenome datasets to determine which genomes are likely to be found. The latter set of profiles will also enable the delineation of datasets that are poorly characterised by MAG/genome collections and prioritise them for analysis (i.e. MAG generation). The outputs of this proposal are manifold. The first is a suite of software tools and associated workflows that can be installed and run on the computer command line. The application of the tool will lead to multiple new data outputs (refined MAGs, improved catalogues and metagenomic profiles) which will be made available via MGnify's web interfaces. To provide rapid access to these MAG catalogues, we will also deploy new web interfaces (implementing the new tools) that allow users to compare their own MAGs against established collections. This will not only democratise scientific research but also reduce the need for data duplication. We will also use specific use cases to demonstrate the utility of our tools and provide training and support for their use.

微生物无处不在，在维持地球生命方面发挥着重要作用，例如：环境氧合、土壤养分循环以支持植物生长或促进动物消化。它们会在植物和动物中引起许多疾病，并且能够快速进化以开发新的生态位和/或对抗抗菌药物。宏基因组学是一个相对较新的领域，是一种独立于培养物的方法，它应用复杂的 DNA 测序技术来分析来自任何环境的总微生物遗传物质。现在可以重新组装数百万个短 DNA 序列，以生成样本中微生物基因组的表示，称为宏基因组组装基因组 (MAG)，尤其是细菌。虽然这种方法的计算成本仍然很高，但用于恢复这些基因组的计算机算法已得到大幅改进，以提高 MAG 的准确性。就在过去的五年里，包括我们自己在内的许多大规模研究已经成功地应用这些技术累计生成了数百万个 MAG。这为科学家提供了对约 99% 尚未进行实验培养的生物体的新见解，并极大地扩展了生命之树。这些 MAG 正在重塑我们对微生物群落结构和组成成员功能的理解。尽管如此，MAG 数量的爆炸式增长也带来了新的挑战。这些大规模分析可以生成与 GenBank 的大型基因组集合相匹配的基因组，该集合源自对实验分离的微生物进行测序的传统技术。这样的基因组集合花费了数十年的时间才建立起来，并由大型数据中心管理。然而，现在小组需要定期对新的 MAG 集合和如此大的参考基因组集合进行比较。我们建议使用一类称为 MinHash 的特定算法，该算法根据共享实体（在我们的例子中是短序列）的数量快速估计两个集合之间的相似性。这种方法的大多数实施都集中在一个基因组与另一个基因组的快速比较上。在本提案中，我们的目标是使用一系列计算技术来将大型查询数据集与大型参考数据库进行比较，并将其应用于微生物基因组、MAG 集合和宏基因组序列。我们将开发该工具并将其应用于一系列数据集，特别是位于领先的宏基因组数据数据库 MGnify 中的数据集。关键应用是识别由计算方法引入的 MAG 中的错误、通过识别数据集之间重复的 MAG 来减少数据、将 MAG 快速纳入在特定环境中发现的基因组目录、MAG 的分类（通过将相似性距离转换为进化距离），以及元基因组数据集的分析以确定可能发现哪些基因组。后一组配置文件还将能够描绘 MAG/基因组集合特征较差的数据集，并优先考虑它们进行分析（即 MAG 生成）。该提案的成果是多方面的。第一个是一套软件工具和相关工作流程，可以在计算机命令行上安装和运行。该工具的应用将带来多种新的数据输出（精炼的 MAG、改进的目录和宏基因组图谱），这些数据将通过 MGnify 的网络界面提供。为了快速访问这些 MAG 目录，我们还将部署新的 Web 界面（实施新工具），允许用户将自己的 MAG 与已建立的馆藏进行比较。这不仅将使科学研究民主化，还能减少数据重复的需求。我们还将使用具体的用例来展示我们工具的实用性，并为其使用提供培训和支持。

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

Ensembl 2024.

合奏 2024。

• DOI: http://dx.10.1093/nar/gkad1049
• 发表时间: 2024
• 期刊: Nucleic acids research
• 影响因子: 14.9
• 作者: Harrison PW