Computational and Systems Biology Core

计算和系统生物学核心

• 批准号: 10017873
• 负责人: Rima F Kaddurah-Daouk
• 金额: $ 41.31万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-15 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10017873
• 关键词: Address; Algorithmic Analysis; Algorithms; Alzheimer' s Disease; Alzheimer' s disease pathology; Animal Model; Animals; Atlases; Biochemical Pathway; Bioinformatics; Biological; Biological Markers; Blood; Brain; Complex; Computational Biology; Computer Models; Computing Methodologies; Data; Data Analyses; Data Set; Databases; Detection; Development; Diabetes Mellitus; Diet; Dietary Intervention; Disease; Disseminated Malignant Neoplasm; Elements; Environment; Ethnic Origin; Gender; General Population; Genes; Genetic; Genotype; Germany; Human; Human Genome; Image; International; Knowledge; Life; Link; Luxembourg; Malignant Neoplasms; Medicine; Metabolic; Metabolic Pathway; Metabolism; Metabolite Interaction; Methods; Microbe; Modeling; Molecular; Multiomic Data; Network-based; New York; Nutritional; Organ; Outcome; Pathway Analysis; Pathway interactions; Patients; Phenotype; Physiological; Plants; Play; Process; Proteomics; Publications; Publishing; Qatar; Reaction; Research; Research Personnel; Resources; Risk Factors; Role; Source; Standardization; Statistical Data Interpretation; Statistical Methods; Subgroup; System; Systems Biology; Techniques; Technology; Transcript; Universities; Work; analysis pipeline; base; biological systems; cohort; data analysis pipeline; data integration; data mining; endophenotype; experience; experimental study; fecal metabolome; genome wide association study; genomic data; gut microbiome; gut-brain axis; heterogenous data; host microbiome; human model; in silico; innovation; knowledge base; metabolic phenotype; metabolome; metabolomics; microbial; microbiome; microbiome analysis; microbiome research; microbiota; molecular phenotype; mouse model; multidimensional data; multiple omics; novel; novel therapeutics; phenotypic data; precision medicine; protein metabolite; reconstruction; simulation; transcriptomics; virtual

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.

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先进的数据分析算法， 整个项目的管道。高质量的预处理数据将从组学中无缝集成 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，包括异常值 检测、差异分析、途径分析和高级网络方法。我们将开发方法 专为该项目中的多室组学数据量身定制，例如来自血液、肠道和大脑的组学数据。此类新颖 集成多组学的方法，多室数据将提供AD病理学的独特读数， 使我们能够释放这些异构数据集背后的全部潜力。此外，我们将致力于 人类-微生物共代谢的计算模型，这将允许在计算机模拟外部 影响，如饮食，在生理规模。核心所解决的研究问题将主要是 由三个项目带动。为此，我们将重点关注人类组学数据集中的血-肠-脑轴 （项目1），动物模型数据集（项目3），以及环境和饮食对分子生物学的影响。 表型（项目2）。第二，核心的主要重点将是开发和应用一个 以微生物组为中心的生物信息学知识库（“图谱”）。为此，将构建一个基于Neoj 4 网络数据库集成了各种异质信息，包括来自代谢组学GWAS的结果， eQTL研究、阿尔茨海默病-表型相关关联研究（例如AD的代谢组学生物标志物 内表型），微生物组-代谢组关联等。该图谱将使我们能够回答复杂的 研究问题，如找到一组给定的代谢物，基因，代谢 途径、GWAS命中和AD内表型在一个单一查询中。在这个项目的最后一部分，我们将 在地图集上开发先进的网络数据挖掘算法，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 AD病理生物学的观点。核心将由一个经验丰富的国际PI小组领导， 在这个领域的经验。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、微生物组分析和代谢GWAS。总之，计算和 系统生物学核心将是联盟内计算方法的核心要素， 数据分析和先进的数据集成和数据挖掘技术。