Systems Biology, Bioinformatics, & Data Integration

全基因组研究战略提供了前所未有的洞察机会，但也 由于数据的大小和复杂性，生物信息学面临的主要挑战。多学科的 这项研究利用了尖端的研究方法，利用了广泛的 ‘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 寄主和病原体的数据，细胞实验筛选宿主和病原体数据， 和有针对性的模型生物实验。整合这些数据集和研究 战略需要创新的方法来机械地检查结核分枝杆菌和宿主如何遗传 变异体调节结核病的发病机制。核心B使用路径驱动和尖端技术 生物信息学方法整合A岩心的遗传结果与多个大尺度 和来自每个项目(蛋白质组学、路径序列、RNAseq)的不同数据集，以动态识别 并为功能测试确定通路和蛋白质网络的优先顺序。虽然这其中的每一个 实验分别在每个项目中进行分析，结果具有更大的潜力 每个数据集之外的洞察力。随着数据的流动，核心B代表着协同的关键来源 在所有项目和核心之间，并将生成导致有针对性的实验的模型 通过迭代分析和假设检验过程。这一核心将把专业知识聚集在一起 针对不同的组学平台的项目以及针对数据的生物信息学策略 整合。AIM 1提供了对核心A和每个核心的不同数据集的综合分析 项目。目标2利用网络传播，这是一种应用于各种 疾病区域，它使用网络来识别不同的突出显示的聚合路径 组学水平的数据集。当研究之间的单个基因重叠时，这种方法是有用的 很差，而来自不同研究的基因确实存在途径/功能重叠。 在这里，我们应用它来研究人类结核病的表型变异，并将其作为独立的 从复杂多样的数据集中提取洞察力和新的疾病基因靶点的方法 这个财团的成员。总体目标是从数据集成中生成按优先级排序的模型 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。

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.