Core D: Data Analysis Core

项目摘要-核心D(数据分析核心) 数据分析核心(DAC)将建立在可用的计算基础设施之上，以满足以下所有需求 耶鲁Sennet TMC的高级数据分析和信息学。它还将为数据存储提供支持 和共享，元数据管理和引用，基于Web的数据可移植和查询，并且严谨 完成中心使命的统计分析。发改委将由尤瓦尔·克鲁格博士领导，他提供 卓越的生物信息学和多维数据计算分析，包括测序 和成像数据。他在方法论开发和协作方面有着广泛的记录 研究分析基因组学和蛋白质组学技术在NIH主要财团中产生的数据。CO-I Gershkovich是耶鲁临床病理学数据基础设施和管理的负责人。他将提供突出的 在构建管道、框架和门户以将生物样品元数据与生物学整合方面的专业知识 分析来自多个模式的数据，包括组学和图像。他将开发一个工作流跟踪系统 用于标本收集、注释、分析数据和共享。DAC将开发分析数据的管道 来自生物分析核心(BAC)的4种分析方法。具体地说，DAC将执行密钥 用于(1)数据处理的功能-建立一套多尺度和多模式数据处理管道和 质量保证协议，(2)数据分析--建立一体化的单元格和空间数据分析 工作流程和生物标志物签名推理，(3)图谱构建--构建组织分子和细胞 通过图像共同配准和多组学共同参考的地图，以及(4)联盟协调--与 CODCC和其他TMC开发公共数据库、可视化和查询。DAC将生成 人类淋巴器官细胞衰老的分子图谱和细胞图谱，定义细胞类型特异性 衰老生物标志物，剖析衰老细胞的异质性，并描绘与衰老的相互作用。 相关的组织环境。它还将汇编数据标准，促进传播，并将我们的 来自Sennet和其他NIH联盟的现有或新生成的地图集和地图的数据。这些都是高度 面向广泛的基础、转换和临床社区的宝贵的下一代生物医学数据资源 研究。

Project Summary – Core D (Data Analysis Core) The Data Analysis Core (DAC) will build on available computational infrastructure to fulfill all the needs for advanced data analysis and informatics of the Yale SenNet TMC. It will also provide support for data storage and sharing, metadata management and referencing, web-based data portable and query, and rigorous statistical analyses to accomplish the center’s missions. The DAC will be led by Dr. Yuval Kluger, who provides prominent expertise bioinformatics and computational analysis of multi-dimensional data including sequencing and imaging data. He has an extensive track record of methodology development and collaborative work in studies analyzing data generated by genomics and proteomics technologies in major NIH consortia. Co-I Gershkovich is directing Yale Clinical Pathology data infrastructure and management. He will provide prominent expertise in building pipelines, frameworks and portals for integrating biospecimen metadata with biological analysis data from multiple modalities including omics and images. He will develop a workflow tracking system for specimen collection, annotation, analytic data, and sharing. The DAC will develop pipelines to analyze data from 4 assay modalities in the Biological Analysis Core (BAC). Specifically, the DAC will carry out the key functions for (1) data processing – to set up a set of multiscale and multimodal data processing pipelines and quality assurance protocols, (2) data analysis – to establish an integrated single-cell & spatial data analysis workflow and biomarker signature inference, (3) map construction – to construct tissue molecular and cellular maps via image co-registration and multi-omics co-referencing, and (4) consortium coordination – to work with the CODCC and other TMCs to develop common database, visualization, and query. The DAC will generate the Molecular Maps and Cellular Maps of cellular senescence in human lymphoid organs, define cell type-specific senescence biomarkers, dissect senescent cell heterogeneity, and delineate the interactions with senescence- associated tissue environments. It will also assemble data standards, facilitate dissemination, and integrate our data with existing or newly generated atlases and maps from SenNet and other NIH Consortia. These are highly valuable next-generation biomedical data resources for a broad community of basic, translational, and clinical research.