Microbiome Meta-Analysis Platform

微生物组荟萃分析平台

• 批准号: 10011865
• 负责人: Mansur R. Kabuka
• 金额: $ 29.38万
• 依托单位: INFOTECH SOFT, INC.
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10011865
• 关键词: Address; Algorithmic Analysis; Area; Biological; Characteristics; Clinical; Clinical Data; Code; Computer software; Cross-Sectional Studies; Data; Data Analyses; Data Management Resources; Data Pooling; Data Provenance; Data Storage and Retrieval; Development; Discipline; Disease; Distributed Systems; Docking; Ensure; Environment; Evaluation; Gene Expression; Generations; Genetic; Health; Human; Human Microbiome; Infrastructure; Internet; Intuition; Knowledge; Longitudinal Studies; Measurement; Meta-Analysis; Metadata; Methods; Microbe; Monitor; Ontology; Ownership; Performance; Phase; Process; Production; Quality Control; Reproducibility; Reproduction; Research; Research Personnel; Scientist; Security; Semantics; Software Tools; Source; Specialist; Specific qualifier value; Staging; System; Techniques; Technology; Testing; Trust; Variant; Work; analysis pipeline; base; bioinformatics pipeline; cluster computing; cohort; complex data; computer cluster; computerized data processing; computing resources; data format; data harmonization; data integration; data interoperability; data management; data modeling; data resource; data standards; data visualization; design; diverse data; experience; experimental study; flexibility; improved; microbial; microbiome; microbiome analysis; microbiome research; microbiota profiles; new technology; novel strategies; preservation; prototype; repository; symposium; tool; user-friendly; web based interface

Researchers from very diverse fields are expanding their research to microbiome studies to understand the interactions between microbes, hosts, and the environment. As new technologies for accelerated production of microbiome sequence data have enabled this type of research, there is a pressing need for high performance computation resources that accommodate flexible and consistent configuration, deployment, and execution of constantly improving analytical pipelines and that enable data harmonization and interoperability. Standards for data and experimental representation are still being developed and enhanced, resulting in semantic inconsistency and incompatible data formats and conventions, and therefore presenting data integration and management challenges. Meta-analyses of pooled data are becoming more widespread as computational power increases. Assessment of the sources of variation in microbiota profiling is sorely needed to understand how to combine and integrate data from different studies. As the field rapidly evolves and new sequencing and processing techniques are developed, the use of hard-coded scientific pipelines limits the scope of biological interpretations. We propose to develop a “Microbiome Meta-Analysis Platform” (MIMAP) that takes advantage of cluster computing, software containerization, and semantic data integration technologies to enable building, modifying and evaluating alternative bioinformatics pipelines for reproducibility studies, new studies, and meta-analysis of microbiome data from different cohorts, from cross-sectional and longitudinal studies, from public sources, collaborators and in-house studies. It enables deployment and testing of existing and emerging bacterial identification and downstream analysis algorithms, substitution of tools to test new approaches, and semantic modeling of data for pooling of multiple studies and for integration of clinical information through a friendly user interface designed with guidance of an expert team of microbiome specialists. It also allows researchers to perform quality control evaluations using positive and negative controls and provenance data. In Phase I, the main workflow execution, data modeling, and evaluation strategies will be prototyped to demonstrate feasibility. During Phase II development, the complete MIMAP system will be created as a solution for the execution of microbiome research.

来自不同领域的研究人员正在将他们的研究扩展到微生物组研究， 了解微生物，宿主和环境之间的相互作用。随着新 用于加速微生物组序列数据产生的技术已经使得这种类型的 在研究中，迫切需要高性能计算资源， 灵活一致的配置、部署和执行， 改进分析渠道，实现数据协调和互操作性。 数据和实验表示的标准仍在制定和加强中， 导致语义不一致和不兼容的数据格式和约定，以及 因此提出了数据集成和管理的挑战。汇总的荟萃分析 随着计算能力的增加，数据变得更加广泛。评估 迫切需要了解微生物群分布的变化来源，以了解如何将联合收割机和 整合不同研究的数据。随着该领域的迅速发展和新的排序， 处理技术得到发展，硬编码科学管道的使用限制了范围 生物学解释。我们建议开发一个“微生物组荟萃分析平台” （MIMAP），它利用了集群计算、软件容器化和语义 数据集成技术，以支持构建、修改和评估备选方案 用于再现性研究、新研究和荟萃分析的生物信息学管道 来自不同队列的微生物组数据，来自横截面和纵向研究，来自 公共来源、合作者和内部研究。它支持部署和测试 现有和新兴的细菌鉴定和下游分析算法，替代 测试新方法的工具，以及用于汇集多项研究的数据语义建模 并通过友好的用户界面整合临床信息， 微生物组专家团队的指导。它还允许研究人员执行 使用阳性和阴性对照以及来源数据进行质量控制评价。同相 I，主要的工作流执行、数据建模和评估策略将被原型化， 证明可行性。在第二阶段的发展，完整的MIMAP系统将是 作为微生物组研究执行的解决方案而创建。