Systems Biology, Bioinformatics, & Data Integration

系统生物学、生物信息学、

基本信息

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
项目类别：
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

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

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项目摘要

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大小和复杂性，主要的生物信息学挑战。多学科该TBRU的研究利用了尖端的研究方法，利用了广泛的范围 '奥学平台，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，带有宿主和病原体数据的细胞实验筛选，和目标模型有机体实验。这些数据集和研究的集成策略需要创新的方法来机械检查MTB和宿主遗传变体调节结核病发病机理。核心B使用途径驱动和尖端生物信息学方法将核心A的遗传结果与多个大规模整合和每个项目（蛋白质组学，Path-Seq，rnaseq）的潜水员数据集以动态识别并优先考虑用于功能测试的途径和蛋白质网络。每一个在每个项目中分别分析实验，结果可能会增加每个数据集之外的洞察力。核心B表示协同的关键来源，因为数据将流动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具有迭代性分析和假设检验过程。这个核心将汇集专业知识在各个项目中，针对不同的“ OMIC平台以及数据的生物信息学策略” 一体化。 AIM 1提供了从核心A和每个核心数据集的集成分析项目。 AIM 2利用网络传播，一种应用于潜水员的系统生物学方法疾病区域，使用网络来识别融合的途径，并以不同的方式突出显示 OMICS级数据集。当研究之间的单个基因重叠时，此方法很有用贫穷，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疾病基因靶标的方法这个财团。总体目标是从数据集成中生成模型，以优先考虑整个项目和核心A的研究指示并创建可测试的机械核心B和所有TBRU组件之间进行迭代评估的假设。