CLIMB-BIG-DATA: A Cloud Infrastructure for Big-Data Microbial Bioinformatics

CLIMB-BIG-DATA：大数据微生物生物信息学的云基础设施

• 批准号: MR/T030062/1
• 负责人: Mark Pallen
• 金额: $ 254.14万
• 依托单位: Quadram Institute
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FT030062%2F1
• 关键词: CLIMB; BIG; DATA; Cloud; Infrastructure

High-throughput sequencing has transformed microbiology, delivering an explosion in genomic and metagenomic big data. However, many microbiologists remain unable to exploit large genomics datasets to address key questions in microbiology, because they lack access to the relevant computational resources, bioinformatics tools or expertise in data analysis. To address this problem, six years ago we launched CLIMB, a pioneering British cloud-computing infrastructure project funded by the MRC that has supported >900 users. As CLIMB comes to an end, we propose a unique new partnership--CLIMB-BIG-DATA (Cloud Infrastructure for Big-Data Microbial Bioinformatics)- to meet the bioinformatic needs of the UK microbiology community as we head into the 2020s. This new CLIMB-BIG-DATA partnership will occupy a distinctive position in the UK, underpinning research in the academic sector alongside the front-line work of government agencies and the health service, while also supporting research that maps on to a wide variety of national/UKRI and international strategic priorities and Official Development Assistance objectives. In response to community needs (as evidenced by >160 signatories), the proposed partnership will maintain the existing CLIMB infrastructure to support hundreds of research projects including high-profile efforts to track the spread of Ebola or Zika virus. However, we also promise to deliver a step-change in the scale and scope of what we can offer to users. We will adopt a matrix model, in which a range of activities will be mapped on to strategically important themes championed by our investigators, including Antimicrobial Resistance; Emerging Infectious Disease and Global Health; Microbial Genomics for Public Health; Microbial communities and metagenomics; Pathogen Biology and Functional Genomics; Sequencing Technologies. Activities aimed at community engagement will include bioinformatics workshops, hackathons and symposia. Activities focused on tools and integration will include enhanced support for sharing software and data, workflow integration and migration between clouds; enhanced support and security for clinical applications; plus integration with large datasets at external facilities, such as the European Nucleotide Archive. Activities focused on infrastructure include: provision of graphics processing units and enhanced storage; maintenance of our original cloud-computing infrastructure to support microbial bioinformatics; plus incorporation of cloud infrastructures from the MRC unit in the Gambia and from the Quadram Institute. The CLIMB-BIG-DATA partnership will run as a UKRI-supported project for five years, with the expectation that the project will become self-financing through robust pathways to sustainability and expansion. The partnership will draw upon a diverse team of partners from multiple research organisations and collaborators from government agencies, and it will be run from the Quadram Institute in Norwich, which as a strategically funded UKRI research institute will provide a first-class stable and resilient environment for the project's future.

高通量测序改变了微生物学，带来了基因组和宏基因组大数据的爆炸式增长。然而，许多微生物学家仍然无法利用大型基因组数据集来解决微生物学中的关键问题，因为他们无法获得相关的计算资源，生物信息学工具或数据分析方面的专业知识。为了解决这个问题，六年前，我们推出了CLIMB，这是一个由MRC资助的开创性的英国云计算基础设施项目，已经支持了超过900个用户。随着CLIMB即将结束，我们提出了一个独特的新伙伴关系-CLIMB-BIG-DATA（大数据微生物生物信息学云基础设施）-以满足英国微生物学界的生物信息学需求，因为我们进入2020年代。这种新的CLIMB-BIG-DATA伙伴关系将在英国占据独特的地位，支持学术部门的研究以及政府机构和卫生服务的一线工作，同时还支持映射到各种各样的研究国家/UKRI和国际战略优先事项和官方发展援助目标。为了满足社区需求（超过160个签署者证明了这一点），拟议的伙伴关系将维持现有的CLIMB基础设施，以支持数百个研究项目，包括追踪埃博拉或寨卡病毒传播的高调努力。然而，我们也承诺在我们可以为用户提供的规模和范围上进行逐步改变。我们将采用矩阵模型，其中一系列活动将映射到我们的研究人员所倡导的具有战略意义的主题，包括抗菌素耐药性;新发传染病和全球健康;公共卫生微生物基因组学;微生物群落和宏基因组学;病原体生物学和功能基因组学;测序技术。旨在促进社区参与的活动将包括生物信息学讲习班、黑客马拉松和专题讨论会。侧重于工具和集成的活动将包括加强对共享软件和数据的支持，云之间的工作流集成和迁移;加强对临床应用程序的支持和安全性;以及与外部设施（如欧洲核苷酸档案馆）的大型数据集的集成。侧重于基础设施的活动包括：提供图形处理单元和增强的存储;维护我们原来的云计算基础设施，以支持微生物生物信息学;加上纳入冈比亚MRC单位和Quadram研究所的云基础设施。CLIMB-BIG-DATA伙伴关系将作为UKRI支持的项目运行五年，预计该项目将通过强大的可持续性和扩展途径实现自筹资金。该合作伙伴关系将利用来自多个研究机构和政府机构合作者的多元化合作伙伴团队，并将由诺维奇的Quadram研究所运营，该研究所作为战略资助的UKRI研究所将为该项目的未来提供一流的稳定和弹性环境。

项目成果

Dadaist2: A Toolkit to Automate and Simplify Statistical Analysis and Plotting of Metabarcoding Experiments.

• DOI: 10.3390/ijms22105309
• 发表时间: 2021-05-18
• 期刊: International journal of molecular sciences
• 影响因子: 5.6
• 作者: Ansorge R;Birolo G;James SA;Telatin A
• 通讯作者: Telatin A

A large outbreak of COVID-19 in a UK prison, October 2020 to April 2021.

• DOI: 10.1017/s0950268822000991
• 发表时间: 2022-05-30
• 期刊: EPIDEMIOLOGY AND INFECTION
• 影响因子: 4.2
• 作者: Adamson, James P.;Smith, Christopher;Pacchiarini, Nicole;Connor, Thomas Richard;Wallsgrove, Janet;Coles, Ian;Frost, Clare;Edwards, Angharad;Sinha, Jaisi;Moore, Catherine;Perrett, Steph;Craddock, Christie;Sawyer, Clare;Waldram, Alison;Barrasa, Alicia;Thomas, Daniel Rh;Daniels, Philip;Lewis, Heather
• 通讯作者: Lewis, Heather

Household transmission of COVID-19 cases associated with SARS-CoV-2 delta variant (B.1.617.2): national case-control study.

• DOI: 10.1016/j.lanepe.2021.100252
• 发表时间: 2022-01
• 期刊: The Lancet regional health. Europe
• 作者: Allen H;Vusirikala A;Flannagan J;Twohig KA;Zaidi A;Chudasama D;Lamagni T;Groves N;Turner C;Rawlinson C;Lopez-Bernal J;Harris R;Charlett A;Dabrera G;Kall M;COVID-19 Genomics UK (COG-UK Consortium)
• 通讯作者: COVID-19 Genomics UK (COG-UK Consortium)

Streptococcus pyogenes carriage acquisition, persistence and transmission dynamics within households in The Gambia (SpyCATS): protocol for a longitudinal household cohort study

冈比亚家庭内化脓性链球菌的携带获取、持久性和传播动态（SpyCATS）：纵向家庭队列研究方案

• DOI: 10.12688/wellcomeopenres.18716.1
• 发表时间: 2023
• 期刊: Wellcome Open Research
• 作者: Armitage E

The ecogenomics of dsDNA bacteriophages in feces of stabled and feral horses.

• DOI: 10.1016/j.csbj.2020.10.036
• 发表时间: 2020
• 期刊: Computational and structural biotechnology journal
• 影响因子: 6
• 作者: Babenko VV;Millard A;Kulikov EE;Spasskaya NN;Letarova MA;Konanov DN;Belalov IS;Letarov AV
• 通讯作者: Letarov AV