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 .

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.

Imagine you are on a journey across Britain. The landscape you pass through is rich in historical meaning, yet the knowledge of this history is compartmentalised: some lies in everyday experiences and memories, some lies in scholarly monographs, some is found on display (and even more, behind the scenes) in our great museums. We live in an old country - but its meaning can be disconnected and hidden. Surprisingly this situation is true even for the aspects of our lives that seem so modern, so vivid, so everyday: the communications technologies that we use to organise our lives.\n\nLocating Communications Heritage aims to reconnect the history of communications and relevant information technologies to the mobile user. What if we go on our journey again but this time a mobile application can tell us about the history of communications around us? As your train arrives in Paddington station the application pulls up an image of a telegraphic apparatus held in the Science Museum, and relates the story of a crime solved by the rapid exchange of information. Passing down the Oxford Road in Manchester would reveal that only a hundred yards west lies a room in which one of the first electronic stored-program computers operated. The application brings up an image of the object and - drawing on the best scholarship and curatorial insight - its meaning is illuminated. Object, history and place are brought together. \n\nLast but not least a channel will be opened up so that the public's experiences and memories of communications and information technologies can contribute to the ongoing reinterpretation of their histories. An aim is that the full potential of citizen mobile in the cause of history of communications will be tapped.\n\nLocating Communications Heritage will pilot a project to make this journey possible. \n\nLocating Communications Heritage is a collaborative project between an academic historian of science and techology, the curator of computing and information at the Science Museum, and commercial partners, British Telecom and Illumina Digital. It seeks the understand what needs to be accomplished to make the mobile application described above possible. The application will be designed. A pilot project will be run, revealing the potential strengths and weaknesses of the application. Finally, we intend to reflect on the experience for the benefit of academic audiences, exhibit design, object interpretation, and future application development.

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.