State-of-the-art technologies are generating unprecedented amounts of complex data, from genomes, to proteomes and transcriptomes, thus spanning mechanistic and functional diversity. Handling, interpreting and integrating these large scale data into descriptive models that interpret the molecular functions at a system level requires continued development of algorithms, robust computational models, and interoperable analytical frameworks. Supported by our core capability, In this work package, we will contribute to the newest developments in the data sciences and facilitate the extrapolation of meaningful signals from often noisy data. We will continue to develop efficient, reproducible and robust assembly and scaffolding algorithms, and robust statistical models to handle diverse complex genomic and metagenomics datasets. We will improve and further develop software to facilitate orthology assignment across complex and highly divergent species. We will also incorporate machine learning approaches to integrate the conservation of functional signals across species to infer functionality and improve annotation. We are also developing new statistical and network analytical approaches will be applied to track temporal and spatial changes across and within species to further inform phenotypic complexity. Our algorithm optimisation expertise will enable us to drive computational advances in accuracy and efficiency across our research into assembly and variant calling, annotation and, network analysis. These efforts will put the platforms in place to consistently collect and rapidly feed datasets into downstream integrative analyses, enabling the extensive and complex data interrogation processes required for bringing together multiple heterogeneous datasets. We will also apply these strategies to investigate and implement low power consumption computing technologies for data acquisition and analysis that will be deployed in environmental situations at a previously unavailable scale. We will carry out fundamental research into software engineering methods to manage, share, visualise and integrate the large and complex datasets. We will develop research data management and dissemination layers, underpinned by community standards, that provide the granularity and searchability of EI’s large-scale and diverse data outputs that we are generating, and integrate the statistical, machine-learning, and network-based models developed under this programme. We will also build semantic knowledge graphs annotated with ontology-based descriptions in order to represent the body of information gathered through harmonised data and network integration. These will comprise metadata that describe reusable interconnected research datasets, and we will feed these methods into appropriate information systems to enable national and international collaborative research through open multi-omics platforms. This will lay the foundations for an interconnected network of hardware and software to deliver real-time monitoring of crop development and pathogen detection and screening. Subsequent federation of these activities with national and international services will be facilitated through the EI NCG in e-Infrastructure (NC3), and interactions with the ELIXIR-UK community for pan-European life science infrastructures. This will also underpin the computational biology needs of the QIB Microbes in the Food Chain ISP in standardised and interoperable analysis systems.

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.

Phenotyping photosynthetic characters from diverse lines of wheat will be combined with next generation genetic approaches to enable the identification of markers and genes associated with each trait. Such knowledge will enable combinations of these traits to be rapidly incorporated into elite wheat lines to increase yields based on improved photosynthetic efficiency. Moreover, identifying the genes and mutations responsible for the traits will provide an understanding of the biology underpinning the trait and the ability to use precision genome engineering tools in the future. The project will identify wheat material, develop markers and build bioinformatics tools. All of this will be made available to the international community via CIMMYT and iPlant. The project builds upon high throughput methods and knowledge developed by the wheat yield consortium and utilises exome capture technology to discover the relevant genetic information in a cost effective manner. The project combines the diverse expertise in photosynthesis, genetics, wheat physiology and breeding from Lancaster, Liverpool, ANU and CIMMYT and leverages off related existing research.

All honeybee colonies in the UK (excepting parts of Scotland and some islands) have the ectoparasitic mite Varroa destructor. Varroa acts as a vector for a range of viruses of honeybees which are transferred when the mite feeds on haemolymph (blood) from the developing pupa. The most important of these viruses is Deformed Wing Virus (DWV). We have demonstrated that mite infestation preferentially leads to amplification of a specific virulent form of DWV. In mite-exposed developing pupae this particular virus reaches levels almost 10,000 times higher than seen in the absence of the mite, despite the development of an immune response to the infecting virus. We want to understand why the virulent form of DWV observed in mite-infested colonies or mite-exposed pupae replicates to such elevated levels. Varroa-free honeybee colonies or Varroa-infested colonies will be used as a source of individual honeybee larvae, harvested and maintained in the laboratory under carefully controlled conditions. Individual larvae will be either fed or injected with a characterised virus population. The tissues and organs in which the virus replicates will be determined using exquisitely sensitive hybridisation techniques on either dissected pupae or sections. We will repeat these studies in larvae in which we have deliberately suppressed key components of the immune response by inhibiting expression of the genes Dicer and Argonaute. We will pre-expose larvae via feeding, to short RNA molecules designed to inhibit DWV replication. We will test whether DWV carries a gene that inhibits the effectiveness of RNA-based immune responses using well-established techniques. We will investigate the role of specific host genes implicated in components of the immune response or development to enhanced susceptibility to DWV-mediated disease. We will suppress individual genes of interest and then challenge larvae with known virus populations.

Potato is one of the world's most important food crops, and production is threatened by several pests and pathogens such as late blight, soil-dwelling cyst nematodes, and a number of bacteria and viruses. It is calculated that global potato production could increase by up to a third if the diseases that reduce yields could be controlled. The most promising route for combating crop pests and diseases is by deployment of naturally-occurring plant resistance genes. In plants, including potato, disease resistance is typically controlled by resistance genes, known as 'R genes' that have a recognizable structure. Recently a new method, called RenSeq, takes advantage of the conserved structure to capture R gene containing DNA fragments from the full genome and sequence them. Using this technique we can now sequence the R genes (which represent only about 0.25% of the DNA in the potato genome) from any Solanum species in an efficient and cost effective manner. The main aims of this project are to combine genetics, RenSeq , and improved sequencing technologies to determine the R genes within Solanum species that convey resistance against potato pathogens. By combining expertise from multiple institutes (JHI, TSL, UoD and TGAC) with the breeding company SimPlot we hope to develop new resistant cultivars. 1. We will improve the RenSeq method using longer sequence reads to more accurately discriminate between closely related R genes. 2. Identify R genes that appear to convey resistance to P. infestans (late blight), potato cyst nematode, and potato virus Y. 3. Confirm candidate R genes function and devise markers for breeding. 4. Test and combine R genes in individual cultivars for the UK and US markets.