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

我们的主要目标是探索遗传和医学上相关的人类疾病表型之间的关系。要做到这一点，一种方法是评估长期建立的实验室小鼠品系之间的遗传差异。实验室小鼠品系表现出许多重要的疾病表型，如对各种形式的癌症(如肝癌、肺癌和皮肤癌)、细菌和病毒感染的抵抗力，并被用作许多人类疾病的模型。研究这些菌株的遗传差异的基础是拥有准确的基因组序列。在这个项目中，我们将首先为最常用的实验室小鼠品系生成基因组序列，然后使用这些序列和基因结构的知识来确定观察到的疾病反应和这些品系之间行为差异的遗传原因。通过结合序列和表型数据，我们将确定序列变异是否可能导致疾病易感性。本项目的主要目标是正确识别新发布的12个实验室小鼠品系基因组序列上的所有基因。这是通过两种战略的结合来实现的。最初，这些基因将使用最先进的生物信息学程序和管道进行识别。这些基因是通过与基因组上已知的小鼠蛋白、其他转录数据(如mRNAs和EST)或其他物种的保守蛋白的匹配来识别的。由于这是一条自动管道，将会有复杂的基因家族无法正确识别，需要人工检查。哈瓦那团队参与了对人类、老鼠和斑马鱼参考基因组的手动注释，并开发了内部专家工具，以帮助准确识别不同基因组中的基因。由于人工检查昂贵且耗时，人工工作将针对复杂的基因家族和小鼠科学研究界特别感兴趣的基因。与社区接触对于获得针对注释的反馈以及促使社区参与对感兴趣的基因的手动检查至关重要。自动注释正确识别了大约70%的基因，因此目标是使用生物信息学分析和研究人员的反馈来针对30%错误注释的基因进行改进。

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.