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对高级数据分析算法和整个项目的管道。高质量的预处理数据将与OMIC无缝集成和技术核心。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，差分分析，途径分析和高级网络方法。我们将开发方法专门针对该项目中的多室多轨迹数据量身定制的，例如来自血液，肠道和大脑。这样的小说综合多媒体的方法，多校区数据将为广告病理学和允许我们解锁这些异质数据集背后的全部潜力。而且，我们将继续人类微叶合作代谢的计算模型，这将允许在外部模拟中影响饮食等影响力。核心解决的研究问题将主要是由三个项目驱动。为此，我们将重点放在人类幻象数据集中的血统脑轴上（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将放在一个开发和应用上以微生物组为中心的生物信息学知识库（“地图集”）。为此，将构建一个基于NEOJ4的网络数据库集成了各种异质信息，包括代谢组学GWAS的结果， EQTL研究，阿尔茨海默氏症 - 表型相关关联研究（例如，AD代谢组生物标志物内型型），微生物组 - 甲状谢物关联等。地图集将使我们能够回答复杂研究问题，例如找到一组代谢物，基因，代谢之间的联系一个查询中的途径，GWAS命中和广告内型。在该项目的最后部分，我们将在地图集上开发高级网络数据挖掘算法，以提取以外的新信息简单的关联。这将导致与AD相关的集成分子模块查看AD病理生物学。核心将由经验丰富的国际PI团体领导，十多年领域的经验。该团队在包括糖尿病在内的代谢研究的主要领域有往绩癌症，阿尔茨海默氏病，微生物组分析和代谢GWA。总之，计算和系统生物学核心将是财团内计算方法的核心要素，提供数据分析和高级数据集成和数据挖掘技术。