Computational Core

计算核心

• 批准号: 10724222
• 负责人: Qin Ma
• 金额: $ 24.94万
• 依托单位: BETH ISRAEL DEACONESS MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-11 至 2028-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10724222
• 关键词: ATAC-seq; Address; Algorithms; Architecture; Bioinformatics; Biological; Biological Assay; Biometry; Cell Communication; Cells; Cellular Indexing of Transcriptomes and Epitopes by Sequencing; Clinical; Collaborations; Computer Analysis; Computing Methodologies; DNA; Data; Data Analyses; Data Set; Ensure; Experimental Designs; Flow Cytometry; Generations; Genes; Goals; HIV; HIV Infections; Hand; In Situ; Manuscripts; Methodology; Methods; Modality; Modeling; Molecular; Morphology; Multiomic Data; Ohio; Pathogenesis; Pattern; Preparation; Proteins; Quality Control; RNA; Reporting; Reproducibility; Research; Research Personnel; Research Project Grants; Ribosomal RNA; Sample Size; Sampling; Serology; Services; Statistical Data Interpretation; Statistical Methods; System; Testing; Tissues; Treatment Efficacy; Universities; Vaccine Therapy; Variant; Viral reservoir; Visualization; Work; cell type; computational pipelines; data integration; data management; data sharing; design; experience; genomic data; innovation; insight; medical schools; member; multiple omics; neutralizing antibody; power analysis; single cell analysis; single-cell RNA sequencing; success; tool; transcriptome sequencing; viral RNA

Computational Analysis Core (Core C) - Project Summary The primary objective of the Computational Analysis Core (Core C) is to provide centralized statistical and bioinformatics services for, and collaboration on, the research projects of this P01. Core C will serve as the focal point for P01 investigators to draw statistical and bioinformatics expertise for the design and analysis of their research projects as well as for staffing support to execute the planned studies. Core C will enable a deep, multi- model understanding to help identify cellular and/or spatial signatures that can predict or inform mechanisms of interventional efficacy against HIV viral reservoirs. The Specific Aims of the Core are to: 1) use established computational methods to power, quality control, and analyze bulk/single-cell genomics data; 2) use established computational methods to quality control and analyze spatial-omics data; and 3) Cross-platform and -species integration of single-cell and spatial-omics data for identifying predictive features of HIV interventional efficacy. Core C members will be involved in all Projects at every research stage. As the Projects yield results, the Core will conduct data analyses, prepare any necessary reports, and assist investigators with the preparation of presentations and manuscripts. Core C will be co-led by Drs. Qin Ma (Ohio State University), Sizun Jiang (Harvard Medical School), Alex K. Shalek (MIT/Ragon Institute/Broad Institute), and Dongjun Chung (Ohio State University). All Core members have extensive experience in applied biostatistics and bioinformatics methods for serology, bulk, single-cell, and spatial multi-omics data. They will work closely with other cores (e.g., Multi-omics Core) for seamless integration of all aspects of this innovative P01 project. Specifically, Drs. Ma and Shalek will oversee bulk and single-cell related data analyses, Drs. Jiang and Ma will oversee spatial omics data analysis (including CODEX and CosMX), and Dr. Chung will oversee the statistical and power analyses. Moreover, the integrative analysis of single-cell and spatial-omics data will be collaboratively overseen by all members. In summary, Core C will ensure rigor and reproducibility by applying accepted and appropriate statistical and bioinformatics methods to data analysis, by clearly describing methodology, rationale, and interpretations in reports and manuscripts, and by openly sharing research results and data.

计算分析核心（核心C）-项目摘要 计算分析核心（核心C）的主要目标是提供集中的统计和 为本P01研究项目提供生物信息学服务和合作。核心C将作为重点 P01研究者利用统计学和生物信息学专业知识设计和分析其 研究项目以及为执行计划中的研究提供人员支持。核心C将实现一个深度、多层次的 模型理解，以帮助识别细胞和/或空间特征，这些特征可以预测或告知 对HIV病毒储库的干预效果。核心的具体目标是：1）使用已建立的 计算方法来供电，质量控制和分析批量/单细胞基因组学数据; 2）使用已建立的 空间组学数据质量控制和分析的计算方法; 3）跨平台和跨物种 整合单细胞和空间组学数据，以确定HIV干预疗效的预测特征。 核心C成员将在每个研究阶段参与所有项目。随着项目取得成果， 将进行数据分析，准备任何必要的报告，并协助研究者准备 介绍和手稿。核心C将由Qin Ma博士（俄亥俄州州立大学）、Sizun Jiang共同领导 （哈佛医学院），亚历克斯K。Shalek（MIT/Ragon Institute/Broad Institute）和Dongjun Chung（俄亥俄州 州立大学）。所有核心成员都在应用生物统计学和生物信息学方面拥有丰富的经验 用于血清学、批量、单细胞和空间多组学数据的方法。它们将与其他核心密切合作（例如， Multi-omics Core），以无缝整合这一创新P01项目的各个方面。具体来说，马博士 Shalek博士将负责批量和单细胞相关数据分析，Jiang博士和Ma博士将负责空间组学 数据分析（包括CODEX和CosMX），Chung博士将监督统计和功效分析。 此外，单细胞和空间组学数据的综合分析将由所有人共同监督 成员总之，核心C将通过应用公认的和适当的 数据分析的统计和生物信息学方法，通过明确描述方法，原理， 通过在报告和手稿中进行解释，并公开分享研究成果和数据，