A long term resource to maximise the potential of laboratory mouse strains for medical research

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Our key aim is to explore the relationship between genetic and medically relevant human disease phenotypes. One way to do this is to assess the genetic differences between long-established laboratory mouse strains. Laboratory mouse strains display many important disease phenotypes such as resistance to various forms of cancer (e.g. liver, lung, and skin cancer), bacterial, and viral infection and are used as models for many human diseases. The foundation for studying the genetic differences in these strains is having accurate genome sequences. In this project, we will first generate genome sequences for the most commonly used laboratory mouse strains and then use these sequences and knowledge of the gene structures to determine the genetic cause of observed disease response and behaviour differences between these strains. By combining sequence and phenotypic data we will determine whether sequence variants are likely to be contributing to disease susceptibility.The main aim of this project is to correctly identify all the genes on the newly completed release of genome sequences of 12 laboratory mouse strains. This is achieved in a combination of two strategies. Initially the genes will be identified using state of the art bioinformatic programs and pipelines. The genes are identified by matches to known mouse proteins on the genome, other transcribed data such at mRNAs and ESTs or conserved proteins from other species. As this is an automatic pipeline, there will be complex gene families that cannot be correctly identified and require manual inspection. The HAVANA team have been involved in manual annotation of the human, mouse and zebrafish reference genomes and have developed in-house specialist tools to help accurate identification of genes within different genomes. Since manual inspection is expensive and time consuming the manual effort will be targeted on complex gene families and genes of specific interest to the mouse scientific research community. Engaging with the community will be essential to receive feedback about targeting of annotation as well as to generate community participation in the manual inspection of genes of interest. Automatic annotation identifies around 70% of genes correctly, therefore the aim would be to use bioinformatics analysis and feedback from researchers to target the 30% incorrectly annotated genes and improve them.