Illuminating Neurodevelopment through Integrated Analysis and Vizualization of Multi-Omic Data

项目总结 通过大脑资助产生的多组数据的财富、深度和质量 主动性是史无前例的。它的范围从散装和单细胞RNA-SEQ，到详细的细胞类型- 在整个发育过程中进行特定的表观遗传学分析。然而，虽然技术方面的 获取数据在很大程度上得到了解决，从这些数据中生成了具有生物学意义的信息 多方面的数据集仍然是一个巨大的挑战。我们建议的长期目标是 让分子和细胞神经学家充分受益于不断增长的 通过允许他们执行基本和复杂的分析，而不必 获得编程方面的知识。我们最近获得了建立神经科学多组体的资金 将托管大部分大脑主动多组数据的数据档案(NEMO档案； R24MH114788)。在这里，我们建议构建Nemo Analytics-一个 将与尼莫档案馆完全整合。这将通过三层结构来实现 求婚。首先，我们将开发GEAR(基因表达分析资源-一个门户网站 RNA-Seq可视化)以(A)与NEMO档案和其他大脑活动数据相集成 档案馆；(B)通过与Epiviz的整合展示多组数据(交互式多组数据 基因组浏览器)；(C)实现简单和复杂的跨数据集分析；和(D)托管这些工具 作为提案的一部分而开发。第二，我们将生成和应用量化工具 通过综合分析和可视化工具重建基因调控网络 并将其应用于大脑皮层发育。这些工具将基于Trena(一种用于 转录调控网络分析)和投影仪。其他现有工具将包括 与NEMO Analytics挂钩，进一步改善工作环境。我们将应用这些 研究纹状体和内耳发育的工具，作为使用 系统。第三，我们将向更广泛的神经科学界传播环境 通过面对面和在线黑客松。该项目的成功完成将带来新的 知识、新工具，最重要的是-持久的变革性增强 多组学数据的可用性和意义。

PROJECT SUMMARY The wealth, depth and quality of multi-omic data generated through funding from the BRAIN initiative is unprecedented. It ranges from bulk and single cell RNA-seq, to detailed cell type- specific epigenetic analyses throughout development. However, while the technical aspect of obtaining the data is largely resolved, generating biological meaningful information from these multi-faceted datasets remains a great challenge. The long-term goal of our proposal is to allow molecular and cellular neuroscientists to fully benefit from the ever growing wealth of multi-omic data by allowing them to perform basic and complex analyses without having to obtain knowledge in programming. We were recently funded to build a Neuroscience Multi-Omic Data Archive which will host most of the BRAIN initiative multi-omic data (NeMO Archive; R24MH114788). Here we propose to construct the NeMO Analytics – a work environment that will be fully integrated with the NeMO archives. This will be achieved through a three-tier proposal. First, we will develop the gEAR (gene Expression Analysis Resource - a portal for RNA-seq visualization) to (a) integrate with NeMO archive and other BRAIN initiative data archives; (b) present multi-omic data through integration with Epiviz (an interactive multi-omic genome browser); (c) enable simple and complex cross-dataset analysis; and (d) host the tools developed as part of the proposal. Second, we will generate and apply tools for quantitative reconstruction of gene regulatory networks through integrative analysis and visualization tools and apply them to cortical development. These tools will build on TReNA (a tool for Transcriptional Regulatory Network Analysis) and projectoR. Additional existing tools will be linked to the NeMO Analytics to further enhance the work environment. We will apply these tools to study striatum and inner ear development to serve as real world test cases for using the system. Third we will disseminate the environment to the broader neuroscience community through in person and online hackathons. Successful completion of the project will result in new knowledge, new tools, and most importantly – long-lasting transformative enhancement of the usability and significance of multi-omic data.