Systems Biology, Bioinformatics, & Data Integration

系统生物学、生物信息学、

• 批准号: 10653908
• 负责人: Catherine Marie Stein
• 金额: $ 41.52万
• 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/10653908
• 关键词: Area; Bioinformatics; Biological; Coupled; Data; Data Analyses; Data Set; Databases; Disease; Gene Targeting; Genes; Genetic; Genomics; Goals; Human; Individual; Interdisciplinary Study; Laboratory Organism; Lung; Macrophage; Methods; Modeling; Mus; Mycobacterium tuberculosis; Overlapping Genes; Pathogenesis; Pathway Analysis; Pathway interactions; Pattern; Phenotype; Process; Proteins; Proteomics; Research; Research Methodology; Research Priority; Resistance; Signal Transduction; Source; Systems Biology; Testing; Tuberculosis; Uganda; Variant; Vietnam; clinical phenotype; complex data; data complexity; data integration; diverse data; experimental study; genetic variant; genome wide association study; genome-wide; innovation; insight; model organism; 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未经预先的机会，但也 由于数据的大小和复杂性，主要的生物信息学挑战 在这方面进行的研究使使用广泛的尖端研究方法 ‘奥米斯平台，包含蛋白质组学，基因组学（RNA-seq），全基因组关联研究 （GWAS）的宿主和病原体，带有宿主和病原体数据的细胞体验筛选， 和有针对性的有机体实验。 策略需要创新的方法来检查MTB和宿主遗传 变体调节TB发病机理。 生物信息学方法将核心A的遗传结果与多个大规模整合 以及每个项目（蛋白质组学，Path-Seq，rnaseq）的各种数据集到动态识别 并优先考虑用于功能测试的途径和蛋白质网络。 在每个项目中分析了个人的经验，结果可能会增加 每个数据集以外的洞察力。 在所有项目和核心之间，并将生成模型，导致有针对性的实验 通过前部分析和假设测试过程。 在各个项目中，针对不同的“ OMIC平台以及数据的生物信息学策略” 集成AIM 1提供了对核心的多样性数据集的集成分析 AIM 2。 疾病区域，使用网络来识别收敛的途径，以不同的方式突出显示 OMICS级数据集。 贫穷，而来自不同研究的基因确实具有彼此的途径/功能重叠。 在这里，我们将其应用于研究人类结核的表型变异，并将其用作独立 从多样化和复杂数据集中提取见解和新的新疾病基因靶标的方法 该财团的总体目标是从数据集成的生成模型 研究方向以及核心A核心A和可创建的可测试机制 在核心B和所有TBRU组件之间进行迭代评估的假设。