生物膜标记物的发现与微生物群落的基于云的多克化元基因组学分析
Etienne Z Gnimpieba1, Timothy W Hartman1, Tuyen Do1
1Biomedical Engineering Department, University of South Dakota, 4800 N. Career Ave., Suite 221, Sioux Falls, South Dakota, 57107, United States.
Briefings in bioinformatics
|September 12, 2024
概括
这项研究引入了基于云的,多克化的生物信息工作流程,以简化微生物生物膜的元基因组分析. 自学模块提高了研究人员研究生物膜组成和功能的可访问性.
科学领域:
- 微生物学和生物信息学
- 计算生物学和基因组学
背景情况:
- 微生物生物膜是复杂的社区,对营养素的生产,殖民和生存至关重要.
- 生物膜对植物和人类健康产生重大影响,需要先进的分析方法.
- 超基因组学为生物膜组成提供了强大的洞察力,但需要专门的生物信息学技能.
研究的目的:
- 为微生物生物膜转基因组学开发一个可访问,对接,自我学习的生物信息学工作流.
- 促进社区采用超基因组工具来分析生物膜微生物群落.
- 为了解和实施生物膜元基因组分析提供全面的教程.
主要方法:
- 一个基于云的,多克化的工作流程,集成学习资源和交互式模块.
- 使用Nextflow管道进行数据处理,并使用Vertex AI Jupyter笔记本进行下游分析.
- 在谷歌云存储中存储和可视化结果,以管理计算负载.
主要成果:
- 一个用户友好的工作流程,简化复杂的生物信息学分析,用于所有技能水平的用户.
- 提高生物膜研究的元基因组和元转录组调查工具的可访问性.
- 一个全面的教程集成到NIGMS沙盒为基于云的学习.
结论:
- 开发的工作流程使生物膜元基因组分析实现了民主化,使得研究参与得更广泛.
- 云和容器化技术显著降低了生物信息学工具使用的进入壁垒.
- 本资源支持验证和推进微生物生态和健康领域的科学发现.
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