iPlant UK

英国 iPlant

• 批准号: BB/M018431/1
• 负责人: David Wild
• 金额: $ 226.32万
• 依托单位: University of Warwick
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2015
• 资助国家: 英国
• 起止时间: 2015 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FM018431%2F1
• 关键词: iPlant; UK

Biology is increasingly a 'big data' science as new high-throughput technologies support faster, cheaper generation of sequencing, metabolite and image data. This enables potentially exciting breakthroughs as researchers spot undiscovered patterns and make new discoveries of biological importance. However, many individual biologists, and in some areas the community as a whole, struggle to take full advantage of the data generated because of a lack of computing resource, appropriate support and technical skill. It is not only the output of data analyses, such as a models, curated datasets, or raw data, that have value to the wider community, but also the tools generated during research projects that are used to support researchers to test and validate their hypotheses. Currently these tools often remain in prototype form, for use only within the group or laboratory that generated them, because there is comparatively little standardisation and no easy means of sharing an accessible, user-friendly version of the tool. To undertake world-class bioscience, researchers therefore need to be able to store and access datasets, models and analysis tools, ideally from different locations across the globe due to the need for international collaboration. The iPlant Collaborative was funded by US agency the National Science Foundation (NSF) in 2008 to help solve these issues. The iPlant Data Store is a cloud-based storage space, accessed via iPlant's Discovery Environment (DE), a virtual work/lab bench. In the DE, users can share datasets and tools to analyse data with as many or as few people as they wish. Tools to analyse data developed by iPlant staff or built by others can be shared with the wider community, in a similar manner to 'apps' on smartphones. The iPlant Collaborative is currently distributed across three US locations; we propose to extend this into an international collaboration by building a UK iPlant node at The Genome Analysis Centre (TGAC). TGAC provides the National Capability of computational infrastructure and as such is perfectly situated to provide the foundations for the iPlant UK node. The UK iPlant node would provide independent versions of the iPlant Data Store and DE but would also be linked to the US nodes to share resources and expertise. Physical resource alone is not sufficient for a successful infrastructure: it also needs to be used, maintained and expanded as demand increases. To demonstrate the versatility, power and value of iPlant UK, a dedicated team of programmers based at the Universities of Warwick, Liverpool and Nottingham will adapt tools that have been generated for use in a single project for wider community adoption. Three suites of tools to benefit key areas of UK plant science - sequencing, systems biology and image analysis - will be made available to the global plant research community via the iPlant DE.In less than 10 years, iPlant has built a global user base of over 18,500 users. As this continues to expand, iPlant's future sustainability must be considered. A UK iPlant node will help ensure the future existence and reliability of iPlant, spread expertise and best practice between the UK and US, allow the UK to input to the future direction of this valuable resource and provide an exemplar project to others wishing to establish future international iPlant nodes. By establishing iPlant UK and promoting access to a resource that allows users to readily store and analyse their data, this project will help support a wide range of research including genome-wide association projects exploiting natural variation in crops, predicting biological networks and pathways, and the high-throughput imaging and image analysis services that take researchers one step closer to bridging the genotype to phenotype gap.

随着新的高通量技术支持更快、更便宜地生成测序、代谢物和图像数据，生物学越来越成为一门“大数据”科学。这使得研究人员能够发现未发现的模式并做出具有生物重要性的新发现，从而实现潜在的令人兴奋的突破。然而，由于缺乏计算资源、适当的支持和技术技能，许多生物学家个人以及在某些地区的整个社区都很难充分利用所产生的数据。它不仅是数据分析的输出，例如模型，策划的数据集或原始数据，对更广泛的社区有价值，而且是在研究项目中生成的工具，用于支持研究人员测试和验证他们的假设。目前，这些工具通常仍处于原型形式，仅在生成它们的小组或实验室内使用，因为标准化程度相对较低，也没有简单的手段来共享可访问的、用户友好的工具版本。因此，为了从事世界级的生物科学，研究人员需要能够存储和访问数据集、模型和分析工具，由于需要国际合作，最好是从地球仪的不同地点。2008年，美国国家科学基金会（NSF）资助了iPlant合作项目，以帮助解决这些问题。iPlant数据存储是一个基于云的存储空间，可通过iPlant的Discovery Environment（DE）（一个虚拟工作/实验室工作台）访问。在DE中，用户可以共享数据集和工具，以便与他们希望的尽可能多或尽可能少的人分析数据。iPlant员工开发或其他人构建的数据分析工具可以与更广泛的社区共享，类似于智能手机上的“应用程序”。iPlant协作目前分布在美国的三个地点;我们建议通过在基因组分析中心（TGAC）建立一个英国iPlant节点，将其扩展为国际合作。TGAC提供国家计算基础设施能力，因此完全可以为iPlant UK节点提供基础。英国的iPlant节点将提供独立版本的iPlant数据存储和DE，但也将与美国的节点连接，以共享资源和专门知识。要使基础设施取得成功，仅靠物质资源是不够的：还需要随着需求的增加而加以利用、维护和扩大。为了展示iPlant UK的多功能性、强大性和价值，沃里克大学、利物浦大学和诺丁汉大学的一个专门的程序员团队将调整已生成的用于单个项目的工具，以供更广泛的社区采用。通过iPlant DE，将向全球植物研究界提供三套工具，使英国植物科学的关键领域-测序，系统生物学和图像分析受益。在不到10年的时间里，iPlant已经建立了超过18，500名用户的全球用户群。随着这种情况的继续扩大，必须考虑iPlant未来的可持续性。英国iPlant节点将有助于确保iPlant的未来存在和可靠性，在英国和美国之间传播专业知识和最佳实践，使英国能够为这一宝贵资源的未来方向提供投入，并为希望建立未来国际iPlant节点的其他国家提供示范项目。通过建立iPlant UK并促进对允许用户随时存储和分析其数据的资源的访问，该项目将有助于支持广泛的研究，包括利用作物自然变异的全基因组关联项目，预测生物网络和途径，以及高通量成像和图像分析服务，使研究人员更接近弥合基因型与表型之间的差距。

项目成果

Rice auxin influx carrier OsAUX1 facilitates root hair elongation in response to low external phosphate.

• DOI: 10.1038/s41467-018-03850-4
• 发表时间: 2018-04-12
• 期刊: Nature communications
• 影响因子: 16.6
• 作者: Giri J;Bhosale R;Huang G;Pandey BK;Parker H;Zappala S;Yang J;Dievart A;Bureau C;Ljung K;Price A;Rose T;Larrieu A;Mairhofer S;Sturrock CJ;White P;Dupuy L;Hawkesford M;Perin C;Liang W;Peret B;Hodgman CT;Lynch J;Wissuwa M;Zhang D;Pridmore T;Mooney SJ;Guiderdoni E;Swarup R;Bennett MJ
• 通讯作者: Bennett MJ

The Ontologies Community of Practice: A CGIAR Initiative for Big Data in Agrifood Systems.

• DOI: 10.1016/j.patter.2020.100105
• 发表时间: 2020-10-09
• 期刊: Patterns (New York, N.Y.)
• 作者: Arnaud E;Laporte MA;Kim S;Aubert C;Leonelli S;Miro B;Cooper L;Jaiswal P;Kruseman G;Shrestha R;Buttigieg PL;Mungall CJ;Pietragalla J;Agbona A;Muliro J;Detras J;Hualla V;Rathore A;Das RR;Dieng I;Bauchet G;Menda N;Pommier C;Shaw F;Lyon D;Mwanzia L;Juarez H;Bonaiuti E;Chiputwa B;Obileye O;Auzoux S;Yeumo ED;Mueller LA;Silverstein K;Lafargue A;Antezana E;Devare M;King B
• 通讯作者: King B

Mapping-by-sequencing in complex polyploid genomes using genic sequence capture: a case study to map yellow rust resistance in hexaploid wheat.

• DOI: 10.1111/tpj.13204
• 发表时间: 2016-08
• 期刊: The Plant journal : for cell and molecular biology
• 作者: Gardiner LJ;Bansept-Basler P;Olohan L;Joynson R;Brenchley R;Hall N;O'Sullivan DM;Hall A
• 通讯作者: Hall A

Bringing numerous methods for expression and promoter analysis to a public cloud computing service.

• DOI: 10.1093/bioinformatics/btx692
• 发表时间: 2018-03-01
• 期刊: Bioinformatics (Oxford, England)
• 作者: Polanski K;Gao B;Mason SA;Brown P;Ott S;Denby KJ;Wild DL
• 通讯作者: Wild DL

AutoCloner: automatic homologue-specific primer design for full-gene cloning in polyploids.

AutoCloner：自动同源特异性引物设计，用于多倍体中的全基因克隆。

• DOI: 10.1186/s12859-020-03601-7
• 发表时间: 2020
• 期刊: BMC bioinformatics
• 影响因子: 3
• 作者: Coulton A