Tubbesing Tom, Schlüter Andreas, Sczyrba Alexander
Computational Metagenomics Group, Center for Biotechnology (CeBiTec), Bielefeld University, Universitätsstraße 27, 33615, Bielefeld, Germany.
IBG-5: Computational Metagenomics, Institute of Bio- and Geosciences (IBG), Forschungszentrum Jülich GmbH, c/o Centrum für Biotechnologie (CeBiTec), 33594, Bielefeld, Germany.
BioData Min. 2025 Jun 5;18(1):38. doi: 10.1186/s13040-025-00453-w.
Publicly available metagenomics datasets are crucial for ensuring the reproducibility of scientific findings and supporting contemporary large-scale studies. However, submitting a comprehensive metagenomics dataset is both cumbersome and time-consuming. It requires including sample information, sequencing reads, assemblies, binned contigs, metagenome-assembled genomes (MAGs), and appropriate metadata. As a result, metagenomics studies are often published with incomplete datasets or, in some cases, without any data at all. subMG addresses this challenge by simplifying and automating the data submission process, thereby encouraging broader and more consistent data sharing.
subMG streamlines the process of submitting metagenomics study results to the European Nucleotide Archive (ENA) by allowing researchers to input files and metadata from their studies in a single form and automating downstream tasks that otherwise require extensive manual effort and expertise. The tool comes with comprehensive documentation as well as example data tailored for different use cases and can be operated via the command-line or a graphical user interface (GUI), making it easily deployable to a wide range of potential users.
By simplifying the submission of genome-resolved metagenomics study datasets, subMG significantly reduces the time, effort, and expertise required from researchers, thus paving the way for more numerous and comprehensive data submissions in the future. An increased availability of well-documented and FAIR data can benefit future research, particularly in meta-analyses and comparative studies.
公开可用的宏基因组学数据集对于确保科学发现的可重复性和支持当代大规模研究至关重要。然而，提交一个全面的宏基因组学数据集既繁琐又耗时。这需要包含样本信息、测序读数、组装结果、分箱重叠群、宏基因组组装基因组（MAG）以及适当的元数据。因此，宏基因组学研究往往在数据集不完整的情况下发表，或者在某些情况下根本没有任何数据。subMG通过简化和自动化数据提交过程来应对这一挑战，从而鼓励更广泛、更一致的数据共享。
subMG通过允许研究人员以单一形式输入其研究中的文件和元数据，并自动执行原本需要大量人工和专业知识的下游任务，简化了向欧洲核苷酸档案馆（ENA）提交宏基因组学研究结果的过程。该工具附带全面的文档以及针对不同用例量身定制的示例数据，并且可以通过命令行或图形用户界面（GUI）操作，使其易于部署到广泛的潜在用户。
通过简化基因组解析宏基因组学研究数据集的提交，subMG显著减少了研究人员所需的时间、精力和专业知识，从而为未来更多、更全面的数据提交铺平了道路。更多有详细记录且符合FAIR原则的数据的可用性增加，将有利于未来的研究，特别是在荟萃分析和比较研究中。
