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.

Doctoral Training Partnerships: a range of postgraduate training is funded by the Research Councils. For information on current funding routes, see the common terminology at https://www.ukri.org/apply-for-funding/how-we-fund-studentships/. Training grants may be to one organisation or to a consortia of research organisations. This portal will show the lead organisation only.

Proteins are macromolecules responsible for biological processes in the cell. At their most basic level, they consist of a sequence of amino acids, determined by the sequence of nucleotides (the ATGC building blocks of life) in a gene. Proteins usually fold into three-dimensional structures, allowing them to interact with other molecules and perform their functions. Recent advances in sequencing technologies have led to a substantial accumulation of protein data, and our capacity of generating new protein sequences has surpassed our ability to fully understand their functions. Therefore, it is crucial to develop computational methods that identify sequence or structural similarities between characterised and uncharacterised proteins to transfer functional information from the former to the latter. InterPro, Pfam and FunFam are world-leading, UK-based resources that group similar protein sequences together, forming protein families. Pfam is a collection of protein domain families containing functional annotations. FunFam focuses on protein structural domains that share a common function. InterPro merges information from 13 expert protein databases, including Pfam and FunFam, into a single searchable resource, and further annotates protein families. In the past few years, Artificial Intelligence methods have been successfully applied to several biological applications. For instance, DeepMind's AlphaFold has revolutionised the prediction of how protein sequences fold into three-dimensional structures. Several promising tools are being developed by our collaborators to better identify protein families using Deep Learning (DL). These methods outperform current state-of-the-art approaches in terms of accuracy, coverage and computing efficiency, thus making them more environmentally sustainable. In this ambitious project, we will improve the efficiency, accuracy, and sustainability of InterPro, Pfam and FunFam. This will be accomplished by reducing the technical debt of Pfam, established almost three decades ago, adopting DL approaches to enhance the classification of protein sequences into families, and significantly reducing the carbon footprint of sequence annotation. Finally, we will improve the annotation of agriculturally important plant pathogens, resulting in the creation of hundreds of additional InterPro and Pfam entries.

The plant R&D community has generated high-quality annotated reference genome assemblies for numerous model and crop species, available for web-based investigation via our existing Ensembl Plants platform. Similarly, numerous plant genetic resources and populations have been generated using different approaches to capture and exploit genetic diversity. The most important of these serve as focal community resources for plant R&D, and many now come with associated parental genome assemblies and extensive variant data on the offspring. However, making use of these genetic resources and analysing the results in the context of the genes, genetic variants and appropriate reference genomes, remains a disjointed workflow and their use requires significant bioinformatic, genetic, statistical and technical expertise from users. Coordinated integration of the results of genetic analyses with variant datasets against one or more reference genomes within the familiar Ensembl Plants environment would enable a broad range of UK users to rapidly access and use these complementary datasets for multiple plant species. We will establish the 'Ensembl Plant Populations' platform - a web-tool containing existing population-based sequence and variant data, allowing users to easily run statistically sound genetic analyses using key plant populations. We will focus on seven plant/crop species of high relevance to UK researchers: wheat, barley, rice, brassica, arabidopsis, tomato and oat. These species have been selected based on current Ensembl Plants UK user access statistics, and on the importance of the species to UK agriculture and research. The Ensembl Plant populations tool will provide users with an integrated pipeline to undertake genetic analyses from start to finish, including: (i) upfront investigation of the predicted power of the selected population to detect genetic loci, (ii) inclusion of pre-prepared statistics to support users e.g. to account for varying levels of relatedness between genotypes, (iii) interactive genome-wide view of the results allowing users to move to identified genomic locations of interest in Ensembl Plants, (iv) presentation of useful information linked to genes and variants within those identified regions to help users identify candidate genes for further study. We will work with the UK plant research community to select appropriate populations for inclusion, a process which has already started, and attend community meetings throughout the project to raise awareness and gather feedback, including holding a dedicated stakeholder workshop. By adding targeted value to our current Ensembl-based tools, resources, and user-base, and tailoring these to plant species of high importance to UK research and agriculture, we aim to maximise the impact of the bioinformatic resources generated here. Collectively, these activities will further support the use and exploitation of the powerful biological resources the wider community has generated and genotyped.

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.