A BRAIN Initiative Resource: The Neuroscience Multi-omic Data Archive

项目摘要 通过推进创新神经技术(大脑)倡议进行脑研究 推动时空描述技术的发展和应用 大脑中细胞类型和神经回路的动力学。首席调查员，资深 该项目的人员和员工拥有各种专业知识，可在 大脑倡议联盟，包括从多个机构收集数据的经验， 具有大规模的质量控制和分析处理能力，熟悉NIH政策和 公共档案存放策略。在大型研究中促进顺利互动 财团，我们将开发神经科学多组体档案(NEMO档案)，一种数据 专门专注于存储和传播来自 大脑倡议和相关的大脑研究项目。我们将使用数据的联合模型 使数据的物理位置可以在NEMO本地文件之间分布的存储 系统、公共存储库和基于云的存储系统(例如Amazon S3)。我们会 利用此功能并在我们的本地文件系统和 云。NEMO档案馆将成为符合以下原则的数据资源 正在启动下一代NIH数据资源的研究社区成员 生态系统。这些做法包括公平原则、API文档、数据索引 基于云的系统、工作流共享、可共享软件管道和存储的使用 系统。纳入NEMO档案的信息将在一定程度上使 了解1)与脑异常和疾病相关的基因组区域；2) 转录因子结合部位和其他调节元件；3)转录活性；4)水平 5)组蛋白修饰图谱和染色质可及性。会的 使用户能够回答与大脑研究相关的各种问题，例如识别 诊断候选人，预测预后，选择治疗方法，并检验假设。会的 还提供了指导开发和执行预测性和 未来的机器学习算法。

Project Summary The Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative promotes the development and application of technologies to describe the temporal and spatial dynamics of cell types and neural circuits in the brain. The Principal Investigator, senior personnel and staff of this project have diverse expertise required to marshall data across the BRAIN Initiative consortium, including experience in data collection from multiple institutions, large-scale quality control and analysis processing capability, familiarity with NIH policy and public archive deposition strategies. To promote smooth interactions across a large research consortium, we will develop the Neuroscience Multi-Omic Archive (NeMO Archive), a data repository that is specifically focused on the storage and dissemination of omic data from the BRAIN Initiative and related brain research projects. We will utilize a federated model for data storage such that the physical location of data can be distributed between the NeMO local file system, public repositories, and a cloud-based storage system (e.g. Amazon S3). We will leverage this capability and distribute BRAIN Initiative data between our local filesystem and the cloud. The Nemo Archive will be a data resource consistent with the principles advanced by research community members who are launching resources in next generation NIH data ecosystem. These practices include FAIR Principles, documentation of APIs, data-indexing systems, workflow sharing, use of shareable software pipelines and storage on cloud-based systems. The information incorporating into the NeMO archive will, in part, enable understanding of 1) genomic regions associated with brain abnormalities and disease; 2) transcription factor binding sites and other regulatory elements; 3) transcription activity; 4) levels of cytosine modification; and 5) histone modification profiles and chromatin accessibility. It will enable users to answer diverse questions of relevance to brain research, such as identifying diagnostic candidates, predicting prognosis, selecting treatments, and testing hypotheses. It will also provide the basic knowledge to guide the development and execution of predictive and machine learning algorithms in the future.