metaGOflow:用于分析海洋基因组观测站的猎枪甲基因组学数据的工作流
Haris Zafeiropoulos1,2, Martin Beracochea3, Stelios Ninidakis1
1Institute of Marine Biology, Biotechnology and Aquaculture (IMBBC), Hellenic Centre for Marine Research (HCMR), Former U.S. Base of Gournes, 71003 Heraklion, Crete, Greece.
GigaScience
|October 18, 2023
概括
我们开发了metaGOflow,这是一个高效的工作流程,用于分析来自欧洲海洋生物多样性观察网络 (EMO BON) 站点的基因组生物多样性数据. 这种工具可以快速分类分析和微生物群落的功能注释,确保及时和可重复的结果大规模的元基因组项目.
科学领域:
- 海洋生物学 海洋生物学
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
背景情况:
- 基因组观测站 (GOs) 监测长期的基因组生物多样性.
- 欧洲海洋奥米克生物多样性观测网络 (EMO BON) 从欧洲海洋站收集元基因组数据.
- 对大型元基因组数据集的有效分析对EMO BON至关重要.
研究的目的:
- 开发一个有效的工作流程来分析EMO BON的元基因组数据.
- 为了使微生物社区基因组数据的及时和可重复分析.
主要方法:
- 开发一个基于MGnify资源的工作流程metaGOflow.
- 利用核糖体RNA基因进行分类分析和原始阅读用于功能注释.
- 实施研究对象箱用于元数据包装和容器化以实现模块化.
主要成果:
- metaGOflow提供了基因组数据的快速分类分析和功能注释.
- 工作流成功地通过研究对象箱包装继承了元数据和分析细节.
- 在EMO BON和Tara Oceans样本上进行了用例分析.
结论:
- metaGOflow是一个高效和强大的工作流程,用于大规模的元基因组数据分析,适合像EMO BON.这样的项目.
- 容器化和元数据包装增强了生物数据的管理.
- 工作流是灵活的,用于更广泛的应用,包括单个样本猎枪元基因组学分析.
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