A Cloud-based Dockerized Metagenomics Analysis of Biofilm Microbiome

项目摘要 生物膜是由不同类型的微生物组成的复杂的微生物群落，如 细菌、病毒和真菌。它们是人类微生物感染的主要原因。了解如何 生物被膜影响人类健康，如何控制它对于预防变得越来越重要 医药。在这里，我们建议开发一个生物膜元基因组学工作流自学习模块，以帮助 了解生物膜在人类健康中的作用。元基因组学已成为基因组分析的有力工具 通过基于功能的基因序列识别(功能元基因组学)和基于序列的功能来研究生物膜 鉴定(序列元基因组学)。元基因组测序提供了对所有 所有生物中的基因存在于每个生物膜样本中，使用群体感应信号。我们的生物膜元基因组学 工作流自主学习模块将为学生提供有益于聚合的分析资源 有关fic基因、微生物群落及其代谢途径的知识。 由于这个学习模块将通过云计算提供，学生们可以专注于生物膜元基因组学 分析，而不是首先必须安装软件并验证软件版本。我们经济高效的优化 Docker Image将包括工作流程的打包以及与三个Focus用例相关的样本数据集。 这些“小”数据集将确保云计算资源不会被过度分配。用户还将能够 上传其他样本数据，我们的模块将包括一个数据控制器，它将在工作流之前验证 执行时，会检查输入和参数，以确保它们落在“可接受”的范围内。 影响：我们将利用我们的联合专业知识开发生物膜元基因组学工作流自学习模块。这 学习模块将包括教学视频，这是一个使用Jupyter实施的Docker化交互工作流 笔记本和实践练习，将使自学玩具数据集。这将为我们提供教育 使用生物膜元基因组学资源的社区，这将有助于他们了解生物膜如何影响人类 健康。Biofilm元基因组学工作流程是一个通用的解决方案，允许研究人员将其部署到备选方案 平台，并将其应用于广泛的用例。

Project Summary Biofilms are complex formations of microbial communities composed of different types of microorganisms such as bacteria, viruses and fungi. They are responsible for the majority of human microbial infections. Understanding how biofilm impacts human health and how it can be controlled is becoming increasingly important for preventive medicine. Here, we propose the development of a Biofilm Metagenomics Workflow Self-Learning Module to aid in the understanding of biofilm’s role in human health. Metagenomics has emerged as a powerful tool for the genomic analysis of biofilm through function-based gene sequence identification (functional metagenomics) and sequence-based function identification (sequence metagenomics). Metagenomic sequencing provides the ability to comprehensively sample all genes in all organisms present in each biofilm sample using Quorum Sensing signatures. Our Biofilm Metagenomics Workflow Self-Learning Module will provide students with an analysis resource beneficial for the aggregation of knowledge about specific genes, microbial communities and its metabolic pathways. Because this learning module will be available through cloud computing, students can focus on biofilm metagenomics analysis rather than first having to install software and verifying software versioning. Our cost-effective optimization Docker Image will include the packaging of the workflow along with sample datasets related to the three focus use cases. These “small” datasets will ensure that the cloud computing resources will not be over allocated. Users will also be able to upload other sample data and our module will include a data controller that will verify that before the workflow is executed, the input and parameters are checked to ensure that they fall into the “acceptable” range. Impact: We will use our combined expertise to develop a Biofilm Metagenomics Workflow Self-Learning Module. This learning module will include instructional videos, a Dockerized, interactive workflow implemented using Jupyter Notebook, and practicum exercise that will enable self-learning on toy datasets. This will provide the educational community with a Biofilm Metagenomics resource that will aid in their understanding of how biofilm impacts human health. The Biofilm Metagenomics Workflow is a generalized solution, allowing researchers to deploy it to alternate platforms and apply it to a wide range of use cases.