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Dear All The School of Informatics at the University of Edinburgh, Biomathematics and Statistics Scotland (BioSS) and the Moredun Research Institute (MRI) are offering a jointly funded PhD. studentship in Metagenomics. Developing metagenomic approaches to study the aetiology, pathogenesis and prevalence of Bovine Respiratory Disease (BRD) == Project Description == Bovine respiratory disease (BRD) is a major cause of mortality and morbidity in farmed cattle costing UK industry an estimated £80 million annually. BRD is a complex multi-factorial disease in which viruses and bacteria combine with environmental stressors in the development and spread of the disease. Many cases of BRD are preceded by immune-suppression that predisposes the animal to the development of BRD either by reducing resilience against BRD causative commensal pathogens or to novel pathogens from the environment. Recent studies have shown that the identification of organisms populating both healthy animals and those suffering from BRD can be instructive in identifying the likely cause of BRD in an animal and in identifying novel organisms associated with the condition. These have the potential to open new opportunities for the development of therapeutic approaches and to inform changes in farming practice to minimise the severity and prevalence of BRD. Over the last decade the development of high throughput sequencing has revolutionised the field of metagenomics from one of candidate based screening and profiling to untargeted discovery. In pathogenic disease research, traditional techniques for viral and microbial identification through isolation and culturing are being replaced by metagenomic sequencing of nucleic acids from biological samples which has the potential to catalogue the community diversity and functional potential of the organisms in the sample. Although there are now many examples of the value of metagenomics to disease research there are many practical and analytical hurdles to overcome with regards to sample preparation, pre-processing, quality control, assembly, sequence annotation, taxonomic classification and estimation of community diversity and structure. In this project we will use existing and novel metagenomic sequence data from healthy and BRD affected cattle to characterise the bacterial communities present and their biological functionality using sequence similarity, compositional and hybrid based methods and assess the association of bacterial pathogens with BRD. We will develop novel analytical pipelines for animal disease metagenomics on a HPC system using simulated and experimental data to quantify the impact of algorithm selection and parameterisation. The project will have a strong methodological focus with an emphasis on establishing novel procedures for optimising taxonomic sequence assignment and estimating community diversity and structure. Candidate profile == We are looking for a highly motivated student with a strong background in Computational Biology, Statistical Genetics or Informatics who have or expect to achieve a degree (2:1 or above) or equivalent in a related discipline. Masters students or those with practical experience in research or industry are also encouraged to apply. Funding == Due to the funding restrictions, this studentship is open to EU/UK candidates only and will cover all tuition fees and provide a tax-free stipend at the standard EPSRC rate for 3.5 years. Research partners == School of Informatics, University of Edinburgh (UoE) http://www.ed.ac.uk/informatics/ Biomathematics and Statistics Scotland (BioSS) https://www.bioss.ac.uk/ Moredun Research Institute (MRI) http://www.moredun.org.uk/ Contact == Applicants are encouraged to contact Dr. Ian Simpson, UoE and BioSS ([log in to unmask]) or Dr. David Longbottom, MRI ([log in to unmask]) for further information and initial discussions. To Apply == Applicants should use the standard University of Edinburgh website to apply for the programme: “PhD Informatics: ANC: Machine Learning, Computational Neuroscience, Computational Biology - 3 Years (Full-time)”. The application should include a covering letter stating why they are interested in applying, up-to-date C.V. and details of two academic referees. You may leave the list at any time by sending the command SIGNOFF allstat to [log in to unmask], leaving the subject line blank.