Systems Biology, Bioinformatics, & Data Integration

系统生物学、生物信息学、

• 批准号: 10271171
• 负责人: Catherine Marie Stein
• 金额: $ 25.48万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10271171
• 关键词: Animal Model; Area; Bioinformatics; Biological; Complex; Coupled; Data; Data Analyses; Data Set; Databases; Disease; Gene Proteins; Gene Targeting; Genes; Genetic; Goals; Human; Individual; Interdisciplinary Study; Lung; Methods; Modeling; Mus; Mycobacterium tuberculosis; Pathogenesis; Pathway Analysis; Pathway interactions; Pattern; Phenotype; Process; Proteins; Proteomics; Research; Research Methodology; Resistance; Signal Transduction; Source; Systems Biology; Testing; Tuberculosis; Uganda; Variant; Vietnam; clinical phenotype; data complexity; data integration; experimental study; genetic variant; genome wide association study; genome-wide; genomic RNA; innovation; insight; macrophage; multiple omics; novel; pathogen; pathogen genome; synergism; transcriptome; transcriptome sequencing

Genome-wide research strategies provide unprecedented opportunities for insight but also major bioinformatic challenges due to the size and complexity of the data. The multidisciplinary research in this TBRU utilizes cutting-edge research methods that utilize a broad spectrum of ‘omics platforms, including proteomics, genomics (RNA-seq), genome-wide association studies (GWAS) of the host and pathogen, cellular experimental screens with host and pathogen data, and targeted model organism experiments. Integration of these datasets and research strategies requires innovative approaches to mechanistically examine how Mtb and host genetic variants modulate TB pathogenesis. Core B uses pathway-driven and cutting-edge bioinformatics approaches to integrate the genetic results from Core A with multiple large-scale and diverse datasets from each project (proteomics, Path-Seq, RNAseq) to dynamically identify and prioritize pathways and protein networks for functional testing. While each of these experiments are analyzed individually within each project, the results have potential for greater insight beyond each dataset. Core B represents a key source of synergy as data will flow between all the Projects and Cores and will generate models leading to targeted experiments with an iterative analytic and hypothesis testing process. This Core will bring together expertise across the Projects for the different ‘omic platforms as well as bioinformatic strategies for data integration. Aim 1 provides integrated analyses of the diverse datasets from Core A and each Project. Aim 2 utilizes network propagation, a systems biology method applied to diverse disease areas, which uses networks to identify convergent pathways highlighted by distinct omics-level datasets. This method is useful when the individual gene overlap between studies is poor, while genes from distinct studies do possess pathway/functional overlap with one another. Here we apply it to study phenotypic variation in human TB and use it as an independent method to extract insights and new disease gene targets from the diverse and complex datasets of this consortium. The overall goal is to generate models from data integration that prioritize research directions across the Projects and Core A and create testable mechanistic hypotheses that are iteratively assessed between Core B and all TBRU components.

全基因组研究策略为洞察力提供了前所未有的机会， 由于数据的规模和复杂性，生物信息学面临重大挑战。多学科 在这个TBRU的研究利用尖端的研究方法，利用广泛的 组学平台，包括蛋白质组学、基因组学（RNA-seq）、全基因组关联研究 （GWAS）的宿主和病原体，宿主和病原体数据的细胞实验屏幕， 和有针对性的模式生物实验。这些数据集和研究的整合 战略需要创新的方法来机械地检查结核分枝杆菌和宿主遗传 变异体调节TB发病机制。核心B使用路径驱动和前沿 生物信息学方法将来自核心A的遗传结果与多个大规模 以及来自每个项目（蛋白质组学、Path-Seq、RNAseq）的不同数据集， 并优先考虑功能测试的途径和蛋白质网络。尽管每个 实验在每个项目中单独分析，结果有可能更大 超越每一个数据集。核心B代表数据流动时协同作用的关键来源 在所有项目和核心之间建立联系，并将生成导致有针对性实验的模型 通过迭代分析和假设检验过程。该核心将汇集专业知识 在不同的“组学平台”项目以及数据的生物信息学策略中， 一体化目标1提供了对核心A的不同数据集的综合分析， 项目目的2利用网络传播，一种系统生物学方法， 疾病领域，它使用网络来确定由不同的 omics级别的数据集。当研究之间的单个基因重叠是 差，而来自不同研究的基因确实具有相互重叠的途径/功能。 在这里，我们应用它来研究人类结核病的表型变异，并将其作为一个独立的 一种从多样化和复杂的数据集中提取见解和新的疾病基因靶点的方法 这个财团。总体目标是从数据集成中生成模型， 跨项目和核心A的研究方向，并创建可测试的机制 在核心B和所有TBRU组件之间反复评估的假设。