The Oxford-Warwick Statistics Programme will train a new cohort of at least 50 graduates in the theory, methods and applications of Statistical Science for 21st Century data-intensive environments and large-scale models. This is joint project lead by the Statistics Departments of Oxford and Warwick. These two departments, ranked first and second for world leading research in the last UK research assessment exercise, can provide a wonderful stimulating training environment for doctoral students in statistics. The Centre's pool of supervisors are known for significant international research contributions in modern computational statistics and related fields, contributions recognised by over 20 major National and International Awards since 2008. Oxford and Warwick attract students with competitively won international scholarships. The programme leaders expect to expand the cohort to 11 or 12 per year by bringing these students into the CDT, and raising their funding up to CDT-level using £188K in support from industry and £150K support from donors. The need to engage in large-scale highly structured statistical models has been recognized for some time within areas like genomics and brain-imaging technologies. However, the UK's leading industries and sciences are now also increasingly aware of the enormous potential that data-driven analysis holds. These industries include the engineering, manufacturing, pharmaceutical, financial, e-commerce, life-science and entertainment sectors. The analysis bottleneck has moved from being able to collect and record relevant data to being able to interpret and exploit vast data collections. These and other businesses are critically dependent on the availability of future leaders in Statistics, able to design and develop statistical approaches that are scalable to massive data. The UK can take a world lead in this field, being a recognized international leader in Statistics; and OxWaSP is ideally placed to realize the potential of this opportunity. The Centre is focused on a new type of training for a new type of graduate statistician in statistical methodology and computation that is scalable to big data. We will bring a new focus on training for research, by teaching directly from the scientific literature. Students will be thrown straight into reading and summarizing journal papers. Lecture-format contact is used sparingly with peer-to-peer learning central to the training approach. This is teaching and learning for research by doing research. Cohort learning will be enhanced via group visits to companies, small groups reproducing results from key papers, student-orientated paper discussions, annual workshops and a three-day off-site retreat. From the second year the students will join their chosen supervisors in Warwick and Oxford, five in each Centre coming together regularly for research group meetings that overlap Oxford and Warwick, for workshops and retreats, and teaching and mentoring of students in earlier years. The Centre is timely and ambitious, designed to attract and nurture the brightest graduate statisticians, broadening their skills to meet the new challenge and allowing them to flourish in a focused, communal, research-training environment. The strategic vision is to train the next generation of statisticians who will enable the new data-intensive sciences and industries. The Centre will offer a vehicle to bring together industrial partners from across the two departments to share ideas and provide an important perspective to our students on the research challenges and opportunities within commercial and social enterprises. Student's training will be considerably enhanced through the Centre's visits, lectures, internships and co-supervision from global partners including Amazon, Google, GlaxoSmithKline, MAN and Novartis, as well as smaller entrepreneurial start-ups Deepmind and Optimor.

(written with PPI panel) Many aspects of a young person's life can affect their mental health(MH), and there is a crisis in our ability to support childhood mental illness. Problems often have to become serious before young people can access Child & Adolescent Mental Health Services(CAMHS). CAMHS are stretched, offering help to only a quarter of those in need, and often intervene late. Early identification and treatment are beneficial, but could swamp services and create even longer waits. Some young people are reluctant to access CAMHS because of stigma (e.g. self-harm). Inequity also limits access (e.g. those experiencing economic hardship or from minority groups). These variations leave many struggling to get help, affecting their health lifelong and their and their families' lives. We need to re-think how CAMHS are delivered. Using digital tools to make CAMHS fairer and more efficient could help young people get the right treatment sooner. For example, apps or websites could be used to: (1) identify problems early before someone needs intensive treatments, (2) signpost young people to the most useful services for them rather than sending everyone to CAMHS, or (3) help predict who would benefit most from which treatments, so young people get the right treatment first time. This could be achieved by harnessing the power of 'big data'. Information (data) about a young person's life could help. For example, the risk of serious problems is indicated by an accumulation of factors such as early childhood experiences (e.g. bullying, neglect, racism), the environment (e.g. housing, diet, the amount of green space near home) or physical factors (e.g. genetics, inflammation, brain chemistry). Data like these are already collected from a range of sources such as maternity, health visitors, GP records, schools and social care, but are never brought together. This information, if brought together, could be used to create digital tools to identify patterns using artificial intelligence (AI). However, there are problems to solve first. We do not know which data are most useful, how best to bring data together securely, or the most effective AI methods. Importantly, we have not got agreement on which information should be used for which purposes. For example, it might be acceptable to use genetic information in a hospital to decide which medication is safest, but maybe not to identify who is at risk of suffering from a problem in the community. We must get this right. In this study, we will access data from a broad range of sources, some of which we will collect and organise in the early stage of this project, and use it to establish the best way to develop digital tools to support CAMHS. We will then work with the public, and experts who work with or have experience of MH problems, to translate AI algorithms into digital tools. These digital tools must be part of a clinical service that can intervene early. We want to create a new early identification and prevention service and establish what digital tools are needed to make early detection work effectively, safely, and fairly. We will bring together experts who are doing ground-breaking work in academia, industry, and the clinic, with policy makers. We want to turn their attention to solving these problems, together with young people, their carers, and people with lived experience. The people whose data is used should direct the building of these tools and new clinical pathways. We need their help thinking about which data should be used for what purposes, for which people, what should happen when a young person is thought to be developing MH problems, and how to use digital tools to support treatment decisions. In later years we will explore the effectiveness of the early identification and prevention approach, create recommendations for overhauling inefficient systems and develop a template for data-guided, individualised, and timely MH interventions for the future.

AIM OF THE PHD PROJECT - High throughput sequencing (HTS) of genomes and transcriptomes will lead to the availability of sequencing data for numerous samples across many species. However, there are major problems in the exploitation of this information due to difficulties in the storage, transfer between sites, and visualisation of the large data sets. The aim of this cross-disciplinary PhD project is to (1) To develop novel data reduction methods to streamline data storage and analysis of large complex multi-genomic data (2) To develop visualisation tools to produce compacted visualisation (3) To use these tools to undertake mining of a biological dataset to investigate specific points of biological interest. DATA REDUCTION - The first challenge will be to achieve a major reduction in the size of the data without losing critical meta-data associated to each base sequenced (i.e. the quality of the data or even the original read). We will need to develop novel data reduction algorithms since traditional lossless compression techniques are unsuitable for HTS data because they do not manage both rapid decoding starting from any point in the stream combined with rapid mutual comparison of several compressed streams. Additionally, current DNA compression methods (DNACompress, LCA, and DNAzip) primarily consider a single genome algorithm. Here we will use the repeatability and the consistency of sequencing technologies: applying the same technology and method to very similar genomes sequences is likely to show strong similarities in systematic deviations (sequencing errors, variations in coverage, etc.). This would make the differential compression or other de-duplication techniques highly efficient for the whole data. The second challenge will be to design protocols to improve data transfers. A large number of scientists will be querying consolidated data sets from several locations around the world. We need to provide efficient storage that will support real time partial extraction of data at various resolutions similarly to the functionalities provided by BigBed and BigWig. In addition to data format definitions, it will be necessary to define the protocols that will efficiently support the distributed nature of the work. VISUALISATION - Existing genome browsers are not suited for large scale comparative genomics studies as at best they work for simultaneous visualization of a small number of genomes. Visualization of a large number of genomes will require the identification of new concepts for the navigation and visualization of genomic data. The data reduction techniques we will develop naturally lead towards compact data visualisation with the ability to use interactive thresholds and cut-offs to display comparative features, and the ability to toggle between data sub-sets. Once the right queries have been presented to the appropriate databases, and the results aggregated, the remaining step is to present the data in a meaningful way. APPLICATION - Our current favoured exemplar dataset is from genomic and transcriptomic studies of the obligate fungal pathogen of Barley Blumeria graminis hordei and other closely related fungi. A large collaborative effort including Butcher and Spanu (Imperial) is underway involving BBSRC support (BB/E000983/1; BB/H001646/1). Several completed genomes (>120Mbases range) are available, several others underway with international collaborators; also transcriptomes. We will use the developed computational tools to study phenotypic variation between species. Other biological topics which can be explored include analysis of strain data of plant and animal pathogens and cross genomic studies on related bacteria .

A high density array comprising approximately one billion short nucleic acid sequences (aptamers) will be generated on a next generation sequencing system and tested as an assay platform to identify binding agents to a range of antigens. The initial focus of the project will study variations to a well-characterized G quadruplex sequence that is known to bind specific protein targets, and will explore the capacity of this sequence motif as a generalized scaffold for binding a range of protein and small antigens. The project will generate an alternative technology to SELEX for the display and identification of aptamers as a discovery tool for the characterization of specific DNA -small molecule and DNA-protein interactions, and will lead to the design of molecular probes that can be exploited to study biological hypotheses that are the subject of current interest.

The human genome project and the technological advances that accompanied it, including the recent advent of the "thousand dollar genome" have opened up new possibilities in medicine, including the opportunities for more precise, molecular diagnoses and personalised treatment based on genome information. The technologies are now at a stage where, with appropriate validation and optimisation, they will soon be moved into routine clinical care to accelerate disease diagnosis and improve patient outcomes. However, to introduce this "step-change" in diagnostics and pathology successfully into the clinic, will require the coordinated action of expertise from multiple fields, including the physical sciences, and training of modern-style pathologists to be familiar with multiple advanced technologies. The Edinburgh-St Andrews Molecular Pathology Node will integrate the proven strengths of the Universities of Edinburgh and St Andrews in molecular pathology and diagnostics (training, development and clinical implementation), image analysis of complex phenotypes and computing, with the breadth of genome medicine and genome sciences experience available within the Universities and NHS Lothian. These strengths include institutes and centres with substantial existing MRC, EPSRC and charitable investment including the MRC Human Genetics Unit, MRC Farr Institute, CRUK Cancer Centre and EPSRC-funded supercomputer and optical imaging facilities. The main aims of the Node will be: (1) training a new generation of molecular pathologists capable of handling modern genome-analysis-aided approaches to diagnosis and treatment of human disease; (2) developing new tests and clinical applications utilizing the advantages of novel technologies; (3) creation of new algorithms, standard operating procedures, data flow schemes and advanced statistical and computational methods that will directly facilitate analysis of the vast and complex data generated by genomics and imaging methods, to implement these new molecular pathology approaches in the clinic. We will focus on areas of clinical need where we believe genome-based assays will most rapidly enter the clinic, particularly the genetic diagnosis of acutely ill children and babies, genetic diagnosis in fetuses with congenital malformations, inherited subtypes of common diseases in adults, and the diagnosis and monitoring of patients with cancer through development of "liquid biopsies" from cell-free DNA in circulating blood. A significant part of the proposed work will be done by practicing clinicians and diagnosticians in the framework of a purpose-designed Masters Research Programme in Molecular Pathology, to which experts in many fields will contribute, including those in the UK National External Quality Assurance Scheme (UK NEQAS) for Molecular Genetics and Pathology, which is based at the Royal Infirmary in Edinburgh. Together with our world-leading partners from the biotechnology and pharmaceutical industry, we will develop and integrate these genome and imaging-based methods to implement new diagnostic methods in healthcare and to produce and sustain a generation of "genomically-skilled" pathologists who will be leaders in the introduction of these methods into routine practice for the next generation of doctors and scientists.