Gene expression and regulation is the foundation of plant development, organ specific differences and response to environment. RNA-Seq is a high-throughput sequencing technology that has become the primary platform for the study of gene and transcript level expression. Public data archives store vast volumes of raw RNA-Seq data that requires specialist analysis skills and large-scale computational resources to be of value to biologists and is thus an underutilised resource. Since our first attempt at accessing and processing quantitative gene expression data from publicly archived RNA-seq samples, the number of barley RNA-seq datasets have increased >5 fold, which brings challenges in the scale of processing and visualising large numbers of datasets. We propose to build on our existing barley gene expression database and website, EORNA, to provide a scalable, highly automated system for the discovery, retrieval and quantification of barley RNA-seq data. Comparative visualisation of transcript-level expression data will be coupled to improved curation of the experimental metadata. We will update and expand our current EORNA database with all available public barley RNA-Seq data, quantified against the latest state-of-the-art barley pan-transcriptome reference dataset. We will provide scalable transcript expression level plots with direct access to gene sequence information and annotation through an enhanced and easily searchable website. Finally, we will make the entire system generic and provide it as a free resource to the wider scientific community so that researchers working on other organisms can establish their own species-specific databases. Together, this will create an essential gene and gene transcript discovery resource for barley researchers and breeders, and the wider scientific community.

Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.

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 methods 2) A 65% increase in storage capacity for 'omics' data and associated modelling 3) A 55% increase in CPU capacity to meet the demand for high memory bioinformatics applications such as plant genome assemblies at pangenome scale The 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.

Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.

The work described will provide a generic resource for the amalgamation of data on the genetic control of target traits in grass, wheat, barley, rice and other monocots, i.e. information obtained from one species will be directly transferable to the other species. This information will be used by grass, wheat and barley breeders etc. for the development of superior plant varieties. The work is aimed at all traits and not merely a single trait. However, initial work will concentrate on the amalgamation and transfer of genetic information between species on key sustainability and climate change breeding priorities outlined by the Crop Science Review and Defra as well as production traits. These include the traits of nitrogen use efficiency (leading to low fertiliser input), tolerance to abiotic stress and root architecture (leading to prevention of water run off and thus prevention of pollution to water courses and tolerance to drought) for which genes have been identified on Lolium/Festuca chromosomes. The work will make explicit use of the rice genome which has been substantially sequenced and the fact that gene order in the monocot species has to a large extent been maintained during evolution. Research will also be undertaken to improve the efficiency of breeding strategies in grass, wheat, barley and other monocots. In addition the work undertaken will facilitate the identification of genes controlling target traits. This knowledge will be employed and exploited in breeding programmes via conventional means.