Mouse Phenome Project

基础和临床研究越来越依赖于复杂的信息技术来存储、交换和挖掘大量数据。生物医学研究界需要通用的生物信息学基础设施来定位和分析不同类型和格式以及来自不同来源的数据集。将人类研究数据与模式生物（特别是实验室小鼠）的数据整合起来，这是研究人类疾病最强大和最广泛使用的动物模型，对于最终改善诊断、确定个体化治疗和预测临床结果的转化研究至关重要。小鼠表型组计划是在杰克逊实验室（JAX）构思和启动的，以补充小鼠基因组测序工作，并为复杂性状分析提供研究资源和集成工具。小鼠表型数据库（MPD; http://www.jax.org/phenome）是一个已建立的表型组学数据库，包含600多种小鼠的表型和/或基因型数据。MPD包含世界各地研究团队贡献的丰富数据，包括与人类健康相关的表型，如癌症易感性、衰老、肥胖、对传染病的易感性、动脉粥样硬化、血液疾病、神经感觉障碍和药物毒性。这些研究来自12个国家的约130个研究机构，得到了全球约65个资助机构和研究基金会的支持。基于我们的MPD经验，进一步推进加速生物医学研究的目标，我们建议获取、注释和整合新的数据集供公众访问；确保MPD数据、信息和格式符合社区采用的标准；并实施新的工具和网站功能，以最好地呈现和分析这些数据。表型反映潜在基因组及其对环境影响的反应的内在本质是推断遗传决定因素功能的关键。因此，一个高通量的系统生物学方法来识别表型网络将提供见解，反过来有助于产生新的假设。我们将利用MPD越来越多的定量数据来构建表型网络，为可视化表型之间假定的功能关系提供一个框架，并将这些信息与其他生物信息（如基因网络、代谢途径、表观遗传信息、snp和拷贝数变异）整合在一起。我们的工作将使跨物种识别成为可能，并有助于精确定义和分类人类疾病及其风险因素，并确定预测生理和病理反应的网络组成部分。

Basic and clinical research increasingly depends on sophisticated information technologies for storing, exchanging, and mining large volumes of data. The biomedical research community needs common bioinformatics infrastructures for locating and analyzing datasets of different types and formats and from disparate sources. The integration of data from human studies with data from model organisms, particularly the laboratory mouse, which is the most powerful and widely used animal model for studying human disease, is critical for translational research to ultimately improve diagnosis, determine individualized treatment, and predict clinical outcomes. The Mouse Phenome Project was conceived and launched at The Jackson Laboratory (JAX) to complement the mouse genome sequencing effort and to provide a research resource and integral tool for complex trait analysis. The Mouse Phenome Database (MPD; http://www.jax.org/phenome) is an established phenomics database containing phenotypic and/or genotypic data for over 600 strains of mice. MPD contains a wealth of data contributed by research teams worldwide, including phenotypes relevant to human health such as cancer susceptibility, aging, obesity, susceptibility to infectious diseases, atherosclerosis, blood disorders, neurosensory disorders, and drug toxicity. These studies represent ~130 research institutions in 12 countries, and are supported by ~65 funding agencies and research foundations worldwide. Building on our MPD experience to further advance the goal of accelerating biomedical research, we propose to acquire, annotate, and integrate new datasets for public access; ensure MPD data, information, and formats are consistent with community-adopted standards; and implement new tools and website features to best present and analyze those data. The intrinsic nature of phenotypes to reflect the underlying genome and its response to environmental influences is key to inferring the function of genetic determinates. Thus a high-throughput systems biology approach to identify phenotypic networks will provide insights that in turn help generate new hypotheses. We will exploit MPD's growing collection of quantitative data to build phenotypic networks, providing a framework for visualizing putative functional relationships between phenotypes and integrating this information with other biological information, such as gene networks, metabolic pathways, epigenetic information, SNPs, and copy number variants. Our work will enable cross-species identification and facilitate efforts to precisely define and classify human diseases and their risk factors, and to identify network components that are predictive of physiological and pathological responses.