Computational and Systems Biology Core

计算和系统生物学核心

基本信息

批准号：
10251261
负责人：
Rima F Kaddurah-Daouk
金额：
$ 40.95万
依托单位：
DUKE UNIVERSITY
依托单位国家：
美国
项目类别：
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/10251261
关键词：
Address Algorithmic Analysis Algorithms Alzheimer&apos s Disease Alzheimer&apos 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 dietary 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

项目摘要

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 – 计算和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和整个项目的高质量预处理数据将从 Omics 无缝集成。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。我们将开发方法。专门针对该项目中的多室组学数据，例如来自血液、肠道和大脑的数据。集成多组学、多室数据的方法将提供 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，我们将致力于研究。人类-微生物共同代谢的计算模型，这将允许对外部环境进行计算机模拟核心解决的研究问题主要是生理层面的影响，例如饮食。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），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）。核心的第二个主要重点是表型的开发和应用。为此，将构建一个基于Neoj4的微生物组生物信息学知识库（“图集”）。整合各种异质信息的网络数据库，包括代谢组学 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问题研究问题，例如寻找一组给定的代谢物、基因、代谢物之间的联系在这个项目的最后一部分，我们将在一个查询中分析路径、GWAS 命中和 AD 内表型。在图集上开发先进的网络数据挖掘算法，以提取超出现有范围的新信息这将导致与 AD 相关的集成分子模块，提供多组学。其核心将由拥有十多年经验的国际 PI 团队领导。该团队在代谢研究的主要领域拥有丰富的经验，包括糖尿病、癌症、阿尔茨海默病、微生物组分析和代谢 GWAS 总而言之，计算和分析。系统生物学核心将成为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。