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

通过多组学数据的综合分析和可视化阐明神经发育

• 批准号: 9766403
• 负责人: Ronna Hertzano
• 金额: $ 63.52万
• 依托单位: UNIVERSITY OF MARYLAND BALTIMORE
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2021-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9766403
• 关键词: ATAC-seq; Address; Archives; BRAIN initiative; Biological; Brain; Cells; ChIP-seq; Communities; Complement; Complex; Complex Analysis; Corpus striatum structure; DNA Methylation; DNase I hypersensitive sites sequencing; Data; Data Analyses; Data Set; Databases; Development; Disease; Documentation; Ecosystem; Environment; Epigenetic Process; Feedback; Funding; Gene Expression; Gene Expression Profile; Gene Expression Profiling; Generations; Genes; Genetic Transcription; Genomic Segment; Goals; Human; Imagery; Informatics; Investigation; Knowledge; Labyrinth; Link; Molecular; Multiomic Data; Multivariate Analysis; Neurosciences; Online Systems; Pathway Analysis; Pattern; Peripheral Nervous System; Persons; Phenotype; Principal Component Analysis; Regulator Genes; Research; Resources; Scientist; Source; System; Systems Analysis; Systems Development; Techniques; Testing; Time; Tissue Sample; Tissues; Training; Visualization software; analytical tool; arm; base; cell type; comparative; data archive; data resource; data visualization; epigenomics; genome browser; hackathon; inner ear development; multimodality; multiple omics; neurodevelopment; non-genomic; professional atmosphere; reconstruction; single-cell RNA sequencing; tool; transcription factor; transcriptome; transcriptome sequencing; trend; usability; user-friendly; virtual; web portal; web-accessible; web-based tool

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.

项目摘要 通过BRAIN的资助产生的多组学数据的财富，深度和质量 主动性前所未有。它的范围从批量和单细胞RNA-seq，以详细的细胞类型- 在整个发育过程中进行特定的表观遗传分析。然而，虽然技术方面， 获得数据在很大程度上是解决的，从这些数据中产生生物学上有意义的信息。 多面数据集仍然是一个巨大的挑战。我们提案的长期目标是 使分子和细胞神经科学家能够充分受益于不断增长的 多组学数据，使他们能够执行基本和复杂的分析，而不必 掌握编程知识。我们最近得到资助， 数据存档，将托管大多数BRAIN倡议多组学数据（NeMO存档; R24MH114788）。在这里，我们建议构建NeMO分析-一个工作环境， 将与NeMO档案完全整合。这将通过一个三层 提议首先，我们将开发一个门户网站， RNA-seq可视化），以（a）与NeMO档案和其他BRAIN计划数据集成 （B）通过与Epiviz（交互式多组学数据库）集成来呈现多组学数据。 基因组浏览器）;（c）实现简单和复杂的跨数据集分析;以及（d）托管工具 作为提案的一部分。第二，我们将开发和应用量化工具， 通过综合分析和可视化工具重建基因调控网络 并将其应用于皮层发育。这些工具将建立在TReNA（一种用于 转录调控网络分析）和projectoR。现有的其他工具将 链接到NeMO Analytics，以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 通过亲自和在线黑客马拉松。该项目的成功完成将导致新的 知识，新工具，最重要的是-长期的变革性增强 多组学数据的可用性和重要性。