Crop Diversity GPU - Growing Plant Understanding

作物多样性 GPU - 种植植物的理解

• 批准号: BB/X019683/1
• 负责人: Iain Milne
• 金额: $ 83.37万
• 依托单位: James Hutton Institute
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FX019683%2F1
• 关键词: Crop; Diversity; GPU; Growing; Plant

Crop Diversity GPU - Growing Plant Understanding for resilient food systems and global plant biodiversity conservation (CD-GPU)Comprehensive collections of plant biological materials that capture wide genetic diversity have been carefully established and curated at institutes across the UK, such as the national collections curated by our project partners at the Natural History Museum and the Royal Botanic Gardens in Kew and Edinburgh, as well as diverse materials for crop pre-breeding at NIAB, the James Hutton Institute (JHI) and Scotland's Rural College (SRUC).Such resources are increasingly being exploited via the application of high-throughput 'omics' approaches aimed at capturing large and complex datasets relating to the features they display such as DNA sequence ('genomics'), plant characteristics (from the level of whole fields, down to individual plant tissues and cells; 'phenomics'), and the amount and nature of gene and protein expression ('transcriptomics' and 'proteinomics'). Such approaches underpin broad areas of plant R&D, from landscape-scale analysis all the way down to fundamental research of individual genes and their variants. Further, there is a technological revolution underway within industrial crop production systems, whereby increased crop monitoring combined with robotic technologies are being exploited to streamline crop production - providing further opportunity for academic-industrial joint research underpinned by detailed temporal and spatial datasets from real production scenarios. This requires powerful hardware in order to fully exploit the resulting datasets and information, both within and at the research interface between these disciplines. Specifically, computing infrastructures must incorporate graphical processing units (GPUs), alongside central processing units (CPU) and storage, to provide the appropriate task parallelisation and memory bandwidth required for the analysis of datasets at this scale, as well as to support their analysis and interpretation via emerging Machine Learning (ML) and Artificial Intelligence (AI) approaches.CD-GPU will provide compute capabilities to a consortium of seven UK institutes working in complementary areas of plant and crop science. Specifically, it will deliver the following hardware:1) A 300% increase in GPU capacity for artificial intelligence and machine learning methods2) A 65% increase in storage capacity for 'omics' data and associated modelling3) A 55% increase in CPU capacity to meet the demand for high memory bioinformatics applications such as plant genome assemblies at pangenome scaleThe resource will provide a step-change in the research work we undertake to help underpin sustainable food production, and to understand and reverse plant biodiversity loss across the world. Importantly, by tailoring CD-GPU to our common research needs, and via the provision of associated technical support and training to the users of the resource, this project will build a strong user community with complementary research aims and help encourage collaboration and innovation between our seven institutes.Finally, while there is an environmental cost to run such a resource, the plant, crop and agri-tech science it will enable helps the drive to net zero. Shared infrastructure located at a single site, rather that separate resources at each institute, rationalises compute provision, so reducing environmental impact of installing, maintaining and the resource. The hardware we select is prioritised on performance-per-unit-energy-used, and rather than processors with fewer very fast cores, we select ones with a lot of cores that run more efficiently - a perfect fit for many of our targeted analysis tasks where parallelizing jobs can realise huge cost and performance benefits. Similarly, a single GPU with its thousands of parallel cores can - when appropriate - perform the same work both faster and more efficiently than hundreds of CPUs.

Crop Diversity GPU - Growing Plant Understanding for Resilient Food Systems and Global Plant Biodiversity Conservation（CD-GPU）在英国各地的研究所精心建立和策划了捕捉广泛遗传多样性的植物生物材料的全面收藏，例如由我们的项目合作伙伴在自然历史博物馆和基尤和爱丁堡的皇家植物园策划的国家收藏，以及NIAB、詹姆斯赫顿研究所（JHI）和苏格兰农村学院（SRUC）的作物预育种的各种材料。这些资源越来越多地通过应用高通量“组学”方法来开发，这些方法旨在捕获与它们显示的特征（如DNA序列）相关的大型复杂数据集基因组学（“基因组学”）、植物特征（从整个领域的水平，下至单个植物组织和细胞;"表型学“）以及基因和蛋白质表达的数量和性质（”转录组学“和”蛋白质组学“）。这些方法支撑了植物研发的广泛领域，从小规模分析一直到单个基因及其变体的基础研究。此外，在工业作物生产系统中正在进行技术革命，利用增加的作物监测与机器人技术相结合来简化作物生产-为来自真实的生产场景的详细时间和空间数据集支持的学术-工业联合研究提供了进一步的机会。这需要强大的硬件，以充分利用所产生的数据集和信息，无论是在这些学科之间的研究接口。具体而言，计算基础设施必须结合图形处理单元（GPU）以及中央处理单元（CPU）和存储，以提供分析这种规模的数据集所需的适当任务并行化和内存带宽，以及通过新兴的机器学习（ML）和人工智能（AI）方法支持其分析和解释。CD-GPU将为七个英国研究所组成的联盟提供计算能力，这些研究所在植物和作物科学的互补领域开展工作。具体而言，它将提供以下硬件：1）用于人工智能和机器学习方法的GPU容量增加300% 2）“组学”数据和相关建模的存储容量增加65% 3）CPU容量增加55%，以满足高内存生物信息学应用的需求，例如泛基因组规模的植物基因组组装该资源将为我们开展的研究工作提供一个步骤，以帮助支持可持续的粮食生产，并了解和扭转世界各地植物生物多样性的丧失。重要的是，通过根据我们共同的研究需求定制CD-GPU，并通过向资源用户提供相关的技术支持和培训，该项目将建立一个强大的用户社区，其研究目标互补，并有助于鼓励我们七个研究所之间的合作和创新。最后，尽管运行这样一个资源会产生环境成本，但工厂，作物和农业技术科学将有助于实现净零排放。位于单个站点的共享基础设施，而不是每个机构的单独资源，使计算供应合理化，从而减少安装，维护和资源对环境的影响。我们选择的硬件优先考虑的是单位能耗的性能，而不是具有更少的非常快的核心的处理器，我们选择了具有大量运行效率更高的核心的处理器-非常适合我们的许多目标分析任务，其中并行化作业可以实现巨大的成本和性能优势。同样，一个拥有数千个并行内核的GPU在适当的时候可以比数百个CPU更快更高效地执行相同的工作。

项目成果

Integrative taxonomy reveals unrecognised species diversity in African Corypha larks (Aves: Alaudidae)

• DOI: 10.1093/zoolinnean/zlad107
• 发表时间: 2023-09-28
• 期刊: ZOOLOGICAL JOURNAL OF THE LINNEAN SOCIETY
• 影响因子: 2.8
• 作者: Alstroem，Per;Mohammadi，Zeinolabedin;Stervander，Martin
• 通讯作者: Stervander，Martin

Re-evaluating the importance of threatened species in maintaining global phytoregions.

重新评估受威胁物种在维护全球植物区中的重要性。

• DOI: 10.1111/nph.19295
• 发表时间: 2023
• 期刊: The New phytologist
• 作者: Brown MJM

Global analysis of Poales diversification - parallel evolution in space and time into open and closed habitats

Poales多样化的全球分析——在空间和时间上平行进化为开放和封闭栖息地

• DOI: 10.1111/nph.19421
• 发表时间: 2023
• 期刊: New Phytologist
• 影响因子: 9.4
• 作者: Elliott T

Recoding Amino Acids to a Reduced Alphabet may Increase or Decrease Phylogenetic Accuracy

• DOI: 10.1093/sysbio/syac042
• 发表时间: 2022-07-28
• 期刊: SYSTEMATIC BIOLOGY
• 影响因子: 6.5
• 作者: Foster, Peter G.;Schrempf, Dominik;Embley, T. Martin
• 通讯作者: Embley, T. Martin

HISS: Snakemake-based workflows for performing SMRT-RenSeq assembly, AgRenSeq and dRenSeq for the discovery of novel plant disease resistance genes.

• DOI: 10.1186/s12859-023-05335-8
• 发表时间: 2023-05-17
• 期刊: BMC bioinformatics
• 影响因子: 3