Zhao Zhe, Marotta Federico, Wu Min
College of Life Sciences, Zhejiang University, Hangzhou 310058, China.
Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany.
Microorganisms. 2024 Jun 21;12(7):1264. doi: 10.3390/microorganisms12071264.
As the amount of metagenomic sequencing continues to increase, there is a growing need for tools that help biologists make sense of the data. Specifically, researchers are often interested in the potential of a microbial community to carry out a metabolic reaction, but this analysis requires knitting together multiple software tools into a complex pipeline. Thanos offers a user-friendly R package designed for the pathway-centric analysis and visualization of the functions encoded within metagenomic samples. It allows researchers to go beyond taxonomic profiles and find out, quantitatively, which pathways are prevalent in an environment, as well as comparing different environments in terms of their functional potential. The analysis is based on the sequencing depth of the genes of interest, either in the metagenome-assembled genomes (MAGs) or in the assembled reads (contigs), using a normalization strategy that enables comparison across samples. The package can import the data from multiple formats and offers functions for the visualization of the results as bar plots of the functional profile, box plots of compare functions across samples, and annotated pathway graphs. By streamlining the analysis of the functional potential encoded in microbial communities, Thanos can enable impactful discoveries in all the fields touched by metagenomics, from human health to the environmental sciences.
随着宏基因组测序数量的不断增加，对帮助生物学家理解这些数据的工具的需求也日益增长。具体而言，研究人员通常对微生物群落进行代谢反应的潜力感兴趣，但这种分析需要将多个软件工具整合到一个复杂的流程中。Thanos提供了一个用户友好的R包，用于以途径为中心分析和可视化宏基因组样本中编码的功能。它使研究人员能够超越分类学概况，定量地找出哪些途径在某一环境中普遍存在，并根据其功能潜力比较不同的环境。该分析基于感兴趣基因在宏基因组组装基因组（MAG）或组装 reads（重叠群）中的测序深度，使用一种能够跨样本进行比较的标准化策略。该包可以从多种格式导入数据，并提供将结果可视化为功能概况柱状图、跨样本比较功能的箱线图以及带注释的途径图的功能。通过简化对微生物群落中编码的功能潜力的分析，Thanos能够在宏基因组学涉及的所有领域，从人类健康到环境科学，实现有影响力的发现。
