Computational and Systems Biology Core

摘要-计算与系统生物学核心 计算和系统生物学核心将提供访问高级数据分析算法和 整个项目的管道。将从OMICS无缝集成高质量的预处理数据 和技术核心。我们的分析管道将执行数据分析的所有主要步骤，包括离群值 检测、差异分析、通径分析和先进的网络方法。我们将制定方法 专门为该项目中的多隔室组学数据量身定做，例如来自血液、肠道和大脑的数据。这样的小说 集成多组学、多间隔数据的方法将提供唯一的AD病理和 允许我们释放这些异类数据集背后的全部潜力。此外，我们还将致力于 人-微生物共代谢的计算模型，这将允许计算机模拟外部 在生理水平上的影响，如饮食。核心解决的研究问题将主要是 在三个项目的推动下。为此，我们将重点关注人类组学数据集中的血液-肠道-脑轴 (项目1)，在动物模型数据集中(项目3)，以及环境和饮食对分子的影响 表型(项目2)。第二，核心的主要重点将是开发和应用 以微生物群为中心的生物信息学知识库(“图集”)。为此，将构建一个基于Neoj4的 集成各种异质信息的网络数据库，包括来自代谢组学的结果， EQTL研究、阿尔茨海默病与表型相关的关联研究(例如，阿尔茨海默病的代谢组学生物标志物 内表型)，微生物组-代谢组关联等。图谱将允许我们回答复杂的 研究问题，例如寻找一组给定代谢物、基因、代谢之间的联系 路径、GWASHITS和AD内表型在单个查询中。在这个项目的最后部分，我们将 在地图集上开发高级网络数据挖掘算法，以提取超出 简单的联想。这将导致与AD相关的整合分子模块，提供多组学 阿尔茨海默病的病理生物学观点。核心将由一个有十多年经验的国际私人投资机构小组领导 在这一领域的经验。该团队在代谢研究的主要领域有着良好的记录，包括糖尿病， 癌症、阿尔茨海默病、微生物组分析和新陈代谢。总而言之，计算性和 系统生物学核心将是该联盟内计算方法的核心要素，提供 数据分析以及先进的数据集成和数据挖掘技术。

ABSTRACT – Computational and Systems Biology Core The Computational and Systems Biology Core will provide access to advanced data analysis algorithms and pipelines for the entire project. High-quality preprocessed data will be seamlessly integrated from the Omics and Technology Core. Our analysis pipelines will perform all major steps of data analysis, including outlier detection, differential analysis, pathway analysis, and advanced network methods. We will develop methods specifically tailored for multi-compartment omics data in this project, e.g. from blood, gut, and brain. Such novel methods for integrated multi-omics, multi-compartment data will provide a unique readout of AD pathology and allow us to unlock the full potential behind these heterogeneous datasets. Moreover, we will work on computational models for human-microbe co-metabolism, which will allow in silico simulations of external influences, such as diet, at physiological scale. The research questions addressed by the core will mainly be driven by the three projects. To this end, we will focus on the blood-gut-brain axis in human omics datasets (project 1), in animal model datasets (project 3), and the effects of environment and diet on molecular phenotypes (project 2). A second, major focus of the core will be on the development and application of a microbiome-centric bioinformatic knowledge base (an “atlas”). To this end, will construct a Neoj4-based network database integrating various heterogenous information, including results from metabolomics GWAS, eQTL studies, Alzheimer-phenotype related association studies (e.g. metabolomics biomarkers of AD endophenotypes), microbiome-metabolome associations etc. The atlas will allow us to answer complex research questions, such as finding the connections between a given set of metabolites, genes, metabolic pathways, GWAS hits, and AD endophenotypes in one single query. In the final part of this project, we will develop advanced network data mining algorithms on the atlas, to extract novel information beyond that of simple associations. This will lead to integrated molecular modules associated with AD, providing a multi-omics view on AD pathobiology. The core will be led by an experienced, international group of PIs with over a decade of experience in the field. The team has a track record in major fields of metabolic research, including diabetes, cancer, Alzheimer’s disease, microbiome analysis, and metabolic GWAS. In summary, the Computational and Systems Biology Core will be central element for computational approaches within the consortium, providing both data analysis and advanced data integration and data mining techniques.