A visualization interface for BRAIN single cell data, integrating transcriptomics, epigenomics and spatial assays

BRAIN 单细胞数据的可视化界面，集成转录组学、表观基因组学和空间分析

• 批准号: 10643313
• 负责人: Maximilian Haeussler
• 金额: $ 86.63万
• 依托单位: UNIVERSITY OF CALIFORNIA SANTA CRUZ
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-01 至 2026-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10643313
• 关键词: Acceleration; Address; Adopted; Algorithmic Analysis; Algorithms; Archives; Atlases; BRAIN initiative; Biological Assay; Biological Sciences; Brain; Cells; Cellular Assay; Censuses; Chromosomes; Collection; Computer Analysis; Computer software; Custom; Data; Data Analyses; Data Display; Data Files; Data Set; Databases; Dimensions; Environment; Funding; Future; Gene Expression; Genes; Genomics; Hour; Human; Image; Internet; Knowledge; Location; Maps; Metadata; Methylation; Microscopy; Modality; Molecular; Online Systems; Ontology; Pattern; Physiology; Process; Provider; Publications; Reaction Time; Research Personnel; Retrieval; Risk; Running; Security; Side; Slice; Specific qualifier value; Standardization; Structure; Technology; Testing; Time; United States National Institutes of Health; Universities; Visualization; Visualization software; Work; Writing; XCL1 gene; brain cell; cell type; cloud storage; computerized tools; data access; data archive; data centers; data management; data portal; data reuse; data sharing; data structure; data visualization; design; differential expression; distributed archives; epigenomics; field study; file format; flexibility; genome browser; improved; interest; open data; sex; success; tool; transcriptomics; user-friendly; web site

PROJECT SUMMARY / ABSTRACT The BRAIN Initiative’s -omics data archive NeMO contains all the BICCN single cell single cell data, more than one million files at the time of writing. However, no interactive visualization interface to inspect these results is available and many researchers are not proficient enough with computational tools (download data, convert, load, plot) to take advantage of the data in NeMO. This makes testing hypotheses by non-computational researchers a lot harder than necessary but the input from these domain experts and their knowledge about molecular processes and cell specification is crucial for the interpretation of the data. Here, I propose to repeat for the single cell field what has made genome browsers a pillar of data sharing: a standard file format and a visualization tool for it. A new format is a requirement, because in the era of cloud technology and distributed archives, data is usually not stored locally anymore. Sub-second reaction times from remote storage are necessary for visualization and possible but existing single cell file formats were not designed for this access pattern. In addition, for NeMO, being able to keep all results of a single cell assay in one defined structure makes data management and access easier. We will define such a format, it will integrate all the main analysis results and will convert all existing processed data in NeMO (not the raw read data) to the new format. We will then make the data available for interactive exploration directly on the NeMO website via our UCSC Cell Browser, which is already running on different University websites. It is actively used already by thousands of researchers every month to visualize hundreds of single datasets that we collected at UCSC and has been used for hundreds of scientific publications. We will extend our tool to provide direct support for the most recent assay and analysis types, mainly ATAC, methylation and spatial transcriptomics imaging data and integrate the UCSC Genome Browser for chromosome-level views and summaries. An improved split-screen mode will allow visualizing both spatial and transcriptomic data at the same time. In addition, the Cell Browser will allow users to run the most fundamental and well-known analysis algorithms on any subset of cells, with a focus on fast response time so that researchers do not have to download the whole dataset to get differential genes, dimensionality reduction or specific sub-clustering results of a few selected cell clusters of interest. Integrating these software features and the NeMO data into a single user-friendly environment will make sure that the BICCN -omics data can be accessed as easily as possible, which will help accelerate the path to discovery from the wealth of BRAIN single cell datasets being produced right now and in the future.

项目摘要/摘要