Bilateral NSF/BIO-BBSRC:A Metagenomics Exchange - enriching analysis by synergistic harmonisation of MG-RAST and the EBI Metagenomics Portal

双边 NSF/BIO-BBSRC：宏基因组学交流 - 通过 MG-RAST 和 EBI 宏基因组学门户的协同协调丰富分析

• 批准号: BB/N018354/1
• 负责人: Robert Finn
• 金额: $ 104.13万
• 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: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FN018354%2F1
• 关键词: Bilateral; NSF; BIO; BBSRC; Metagenomics

Micro-organisms are found in virtually all environments. Typically, they form the base of the food chain (such as plankton in the sea) and play essential roles in their ecosystems. There is often a complex interplay between different micro-organisms, with some organisms requiring that others be present in order for them to exist. When there is an imbalance within a community, this can lead to severe effects, such as disease in the human gut, or the inability for plants to grow efficiently in soil. An understanding of the composition and interplay within the communities allows us to potentially manipulate them. Thus, there is intense research into micro-organism communities in many different fields, such as improving livestock yields, the recovery from bacterial infections using fecal transplants and the efficient production of biofuels. Many of these communities also contain important proteins that could be useful to the biotechnological and pharmaceutical industries, such as enzymes involved in the production of antibiotics.Metagenomics is the study of these different micro-organism communities, which is achieved by isolating the DNA from the organisms within an environmental sample (e.g. water, soil, animal stool), sequencing the DNA, followed by the computational analysis to decode which organisms are present and the functions they might be performing. This computation is complicated: (1) there is a huge amount of data; (2) The sequence data is a jumbled mix of fragments from different organisms; (3) Decoding the DNA is hard - typically >90% of organisms within a sample are not well characterised.This proposal brings together three major resources within the field of metagenomics data archiving and analysis. The European Nucleotide Archive (ENA) is a repository of DNA sequence data. Importantly, ENA also captures metagenomic contextual data, such as where and when the sample was taken, how the DNA was extracted and sequenced. The EBI metagenomics portal (EMG, UK) and MG-RAST (MGR, US) are two metagenomics sequence analysis platforms. Uniquely, they represent the only free to use services, whereby researchers can upload sequence data and have it analysed without restriction. Despite the widespread use of metagenomics, currently the community lacks standards to ensure that metagenomics sequence data and the derived functional and taxonomic information are deposited within a database of record. Consequently, the navigation between metagenomics datasets is very difficult for even experienced users. As they offer slightly different, yet complementary, analysis services, there is often the desire to have a metagenomics dataset analysed by both resources. But, the number of equivalent datasets between the two resources is unknown. Unless a user has prior knowledge about equivalent projects, they remain disconnected. Also, sequence data submitted to MGR may not necessarily be deposited in ENA. We propose to set up a computational framework, termed Metagenomics Exchange (ME), to enable metagenomics datasets and the results of their analysis to be linked. All sequences will become available to the research community via ENA and analysis results we be automatically exchanged between EMG and EMR. The ME will be implemented to enable other metagenomics analysis providers to join, and so that it can be used by researchers wishing to perform large scale analyses. We will also investigate ways that our own pipelines can be enhanced through the use of the ME, sharing software and processing tasks, for example. This will lead to computational savings, increasing the capacity for metagenomics analysis. We will also generate a knowledge transfer forum, enabling the exchange of ideas on a range of topics, from hardware solutions to algorithms. Finally, we will undertake a research program to investigate the optimal combination of pipeline analysis components, and whether a single, unified analysis pipeline could be engineered.

微生物几乎存在于所有环境中。通常，它们构成食物链的基础（例如海洋中的浮游生物）并在其生态系统中发挥重要作用。不同微生物之间通常存在复杂的相互作用，某些微生物需要其他微生物的存在才能存在。当群落内存在不平衡时，可能会导致严重的影响，例如人类肠道疾病，或植物无法在土壤中有效生长。了解社区内的组成和相互作用使我们能够潜在地操纵它们。因此，许多不同领域都对微生物群落进行了深入的研究，例如提高牲畜产量、利用粪便移植从细菌感染中恢复以及生物燃料的高效生产。其中许多群落还含有对生物技术和制药行业有用的重要蛋白质，例如抗生素生产中涉及的酶。宏基因组学是对这些不同微生物群落的研究，其实现方法是从环境样本（例如水、土壤、动物粪便）中的生物体中分离 DNA，对 DNA 进行测序，然后进行计算分析以解码其中的 DNA。 生物体的存在及其可能执行的功能。这种计算比较复杂：（1）数据量巨大； (2) 序列数据是来自不同生物体的片段的混乱组合； (3) 解码 DNA 很困难——通常样本中 90% 以上的生物体都没有得到很好的表征。该提案汇集了宏基因组数据归档和分析领域的三个主要资源。欧洲核苷酸档案库 (ENA) 是 DNA 序列数据的存储库。重要的是，ENA 还捕获宏基因组背景数据，例如样本采集地点和时间、DNA 提取和测序方式。 EBI 宏基因组学门户（EMG，英国）和 MG-RAST（MGR，美国）是两个宏基因组序列分析平台。独特的是，它们代表了唯一免费使用的服务，研究人员可以上传序列数据并不受限制地对其进行分析。尽管宏基因组学得到广泛使用，但目前业界缺乏标准来确保宏基因组序列数据以及衍生的功能和分类信息存储在记录数据库中。因此，即使对于经验丰富的用户来说，宏基因组数据集之间的导航也非常困难。由于它们提供略有不同但互补的分析服务，因此通常希望拥有由这两种资源分析的宏基因组数据集。但是，两个资源之间的等效数据集的数量未知。除非用户事先了解等效项目，否则他们将保持断开连接。此外，提交给 MGR 的序列数据不一定存放在 ENA 中。我们建议建立一个称为宏基因组交换（ME）的计算框架，以使宏基因组数据集及其分析结果能够链接起来。所有序列都将通过 ENA 提供给研究界，并且分析结果将在 EMG 和 EMR 之间自动交换。 ME 的实施将使其他宏基因组分析提供商能够加入，以便希望进行大规模分析的研究人员可以使用它。我们还将研究如何通过使用 ME、共享软件和处理任务等来增强我们自己的管道。这将节省计算量，提高宏基因组分析的能力。我们还将创建一个知识转移论坛，以便就从硬件解决方案到算法的一系列主题交流想法。最后，我们将开展一项研究计划，调查管道分析组件的最佳组合，以及是否可以设计一个单一的、统一的分析管道。

项目成果

The European Nucleotide Archive in 2020.

• DOI: 10.1093/nar/gkaa1028
• 发表时间: 2021-01-08
• 期刊: Nucleic acids research
• 影响因子: 14.9
• 作者: Harrison PW;Ahamed A;Aslam R;Alako BTF;Burgin J;Buso N;Courtot M;Fan J;Gupta D;Haseeb M;Holt S;Ibrahim T;Ivanov E;Jayathilaka S;Balavenkataraman Kadhirvelu V;Kumar M;Lopez R;Kay S;Leinonen R;Liu X;O'Cathail C;Pakseresht A;Park Y;Pesant S;Rahman N;Rajan J;Sokolov A;Vijayaraja S;Waheed Z;Zyoud A;Burdett T;Cochrane G
• 通讯作者: Cochrane G

The European Nucleotide Archive in 2018.

• DOI: 10.1093/nar/gky1078
• 发表时间: 2019-01-08
• 期刊: Nucleic acids research
• 影响因子: 14.9
• 作者: Harrison PW;Alako B;Amid C;Cerdeño-Tárraga A;Cleland I;Holt S;Hussein A;Jayathilaka S;Kay S;Keane T;Leinonen R;Liu X;Martínez-Villacorta J;Milano A;Pakseresht N;Rajan J;Reddy K;Richards E;Rosello M;Silvester N;Smirnov D;Toribio AL;Vijayaraja S;Cochrane G
• 通讯作者: Cochrane G

EBI Metagenomics in 2017: enriching the analysis of microbial communities, from sequence reads to assemblies.

• DOI: 10.1093/nar/gkx967
• 发表时间: 2018-01-04
• 期刊: Nucleic acids research
• 影响因子: 14.9
• 作者: Mitchell AL;Scheremetjew M;Denise H;Potter S;Tarkowska A;Qureshi M;Salazar GA;Pesseat S;Boland MA;Hunter FMI;Ten Hoopen P;Alako B;Amid C;Wilkinson DJ;Curtis TP;Cochrane G;Finn RD
• 通讯作者: Finn RD

A unified catalog of 204,938 reference genomes from the human gut microbiome.

• DOI: 10.1038/s41587-020-0603-3
• 发表时间: 2021-01
• 期刊: Nature biotechnology
• 影响因子: 46.9
• 作者: Almeida A;Nayfach S;Boland M;Strozzi F;Beracochea M;Shi ZJ;Pollard KS;Sakharova E;Parks DH;Hugenholtz P;Segata N;Kyrpides NC;Finn RD
• 通讯作者: Finn RD

Computational strategies to combat COVID-19: useful tools to accelerate SARS-CoV-2 and coronavirus research.

• DOI: 10.1093/bib/bbaa232
• 发表时间: 2021-03-22
• 期刊: Briefings in bioinformatics
• 影响因子: 9.5
• 作者: Hufsky F;Lamkiewicz K;Almeida A;Aouacheria A;Arighi C;Bateman A;Baumbach J;Beerenwinkel N;Brandt C;Cacciabue M;Chuguransky S;Drechsel O;Finn RD;Fritz A;Fuchs S;Hattab G;Hauschild AC;Heider D;Hoffmann M;Hölzer M;Hoops S;Kaderali L;Kalvari I;von Kleist M;Kmiecinski R;Kühnert D;Lasso G;Libin P;List M;Löchel HF;Martin MJ;Martin R;Matschinske J;McHardy AC;Mendes P;Mistry J;Navratil V;Nawrocki EP;O'Toole ÁN;Ontiveros-Palacios N;Petrov AI;Rangel-Pineros G;Redaschi N;Reimering S;Reinert K;Reyes A;Richardson L;Robertson DL;Sadegh S;Singer JB;Theys K;Upton C;Welzel M;Williams L;Marz M
• 通讯作者: Marz M