Next-generation sequencing technologies are revolutionizing the way we do research in molecular biology and genetics. One of the leading companies driving the development of next-generation technologies is Illumina. The cost of DNA sequencing has dropped so much that within the next five years sequencing whole genomes for many individuals will become a standard technique, as being undertaken in the Wellcome Trust Sanger Institute's UK10K project. However, we are just at the very beginning of analysing and understanding these massive amounts of data. This project will develop new bioinformatics methodologies for the analysis of next-generation sequence data. Individuals of one species show many differences in DNA sequence, many variants appear to be without phenotypic effect. But recent publications demonstrated elegantly that analysing the protein-coding sequences in few individuals is sufficient to identify the gene responsible for monogenic traits, for example responsible for particular genetically inherited diseases (Choi 2009; Ng 2009; Ng 2010). In these cases the strong phenotypic effect of the individual sequence variants allowed to exclude all previously known sequence variants from the candidate lists. However, most traits are not determined by single genes, but rather depend on many different genes. Sequence variants contributing to such complex traits will be much harder to identify, because individual variants might not have any phenotypic effects unless they occur in combination with other sequence variants, i.e. we cannot exclude previously known sequence variants per se any longer. Prediction of the deleterious effects of individual sequence variants on the amino-acid sequence of the protein products can provide further evidence for the identification of causal variants, e.g. (Ng 2003), though this approach on its own is not powerful enough to identify the causal gene(s). The aim of this project is to establish a systems approach utilizing biological networks in combination with sequence analysis methods to identify sequence variants in silico that are likely to be important for complex phenotypic traits. The underlying assumption is that multiple sequence variants that hit different proteins involved in functionally related processes will in combination lead to phenotypic effects. This project will use gene networks, protein networks and metabolic networks that we have collected from public data repositories and publications to examine the function and potential impact of sequence variants on the biological system. The approaches developed here will be relevant for the study of biological organisms in general; they will also be very instrumental for the identification of genetic effects contributing to complex phenotypes, which could be relevant for breeding of plants and animals, as well as to improve our understanding of complex diseases such as Crohn's disease, Psoriasis and Cancer. Large-scale sequence data for individuals suffering from these disorders are currently being obtained within the Department and by Illumina and will be available for analysis. The outcomes of this project will benefit researchers in the areas of genetics, bioinformatics, gene and protein networks, systems biology and ultimately disease processes. Bioinformatics software developed as part of this project will be made available free of charge as open source software. Molecular biologists will benefit as users of our software for the analysis of their sequence data and the exploration biological networks; the project will thus support the design of novel experimental approaches. Choi, M. (2009). Proc Natl Acad Sci U S A 106(45): 19096-19101. Ng, P. C. (2003). Nucleic Acids Res 31(13): 3812-3814. Ng, S. B. (2010). Nat Genet 42(1): 30-35. Ng, S. B. (2009). Nature 461(7261): 272-276.

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 .

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.

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.

(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.