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共同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。将来自人类研究的数据与来自模型生物的数据整合在一起，特别是实验室小鼠，这是研究人类疾病最强大和最广泛使用的动物模型，对于转化性研究最终改善诊断、确定个性化治疗和预测临床结果至关重要。小鼠表型组计划是在杰克逊实验室(JAX)构思和启动的，目的是补充小鼠基因组测序工作，并为复杂特征分析提供研究资源和完整的工具。小鼠表型数据库(mpd；http://www.jax.org/phenome))是一个建立起来的包含600多个品系小鼠的表型和/或基因数据的表型数据库。MPD包含全球研究团队贡献的大量数据，包括与人类健康相关的表型，如癌症易感性、老龄化、肥胖症、传染病易感性、动脉粥样硬化、血液疾病、神经感觉障碍和药物毒性。这些研究代表了12个国家的约130个研究机构，并得到了全球约65个资助机构和研究基金会的支持。在我们MPD经验的基础上，为了进一步推进加速生物医学研究的目标，我们建议获取、注释和集成新的数据集，以供公众访问；确保MPD数据、信息和格式与社区采用的标准一致；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。表型的内在本质反映了潜在的基因组及其对环境影响的反应，这是推断遗传决定因素功能的关键。因此，识别表型网络的高通量系统生物学方法将提供洞察力，进而帮助产生新的假设。我们将利用MPD不断增长的量化数据来建立表型网络，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整合，如基因网络、代谢途径、表观遗传信息、SNPs和拷贝数变异。我们的工作将使跨物种识别成为可能，并促进准确定义和分类人类疾病及其风险因素的努力，以及识别预测生理和病理反应的网络组件。

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.