Dataset for Neural Network

This is the dataset for the manuscript named with "Neural Network Establish Co-Occurrence Links between Transformation Products of Contaminants and Soil Microbiomes". Quantitative data of HRMS were provided here. The abstract for the manuscript is as following: It is still challenging for ecologists and environmentalists to identify which microorganisms are carrying out specific metabolic processes in the natural environment, even though stable isotope probing (e.g., DNA-SIP) could link degraders and their substrates. As a new strategy, we combined the use of a network-based algorithm, MMvec, and our developed 2H-labeled Stable Isotope-Assisted Metabolomics pipeline (2H-SIAM) to discover links between transformation products (TPs) of the contaminant and microbes in soils. Abiotic stresses were firstly used to constitute the assembly of soil microbiomes, characterized by 16S rRNA gene sequencing. Pyrene and pyrene-d10 were added into soils for biodegradation, and 2H-SIAM was used to obtain TPs of pyrene. Then, MMvec was used to establish a co-occurrence network between TPs and microbiomes. The results confirmed the role of Pseudomonas and Phenylobacterium in the oxidation, mineralization, and methylation of pyrene. Sphingomonas and phylum Acidobacteria contributed to the oxidation of pyrene. The obtained co-occurrence network was markedly following the reports studied by DNA-SIP, indicating the performance and reliability of the co-occurrence network. In summary, we firstly depict the links between TPs and microbes in the environment matrix, which exhibits unique advantages comparing to the other isotope-based approaches.. The installation of the MMvec please refer to https://github.com/biocore/mmvec. In this study, the MMvec was carried out in qiime2-2020.6 platform, and the MMvec was carried out with the codes provided in a .doc document. Additionally, two necessary documents, "lcms_nt.txt" and "otus_nt.txt", for the evaluation of the MMvec are provided here.

本数据集对应题为《神经网络构建污染物转化产物与土壤微生物组的共现关联》的研究论文。本数据集提供了高分辨质谱（High Resolution Mass Spectrometry, HRMS）的定量数据。该论文的摘要如下：尽管稳定同位素探针（stable isotope probing, SIP，例如DNA-SIP）能够将降解菌与其底物关联起来，但生态学家与环境学家仍面临一项挑战：难以在自然环境中鉴定出执行特定代谢过程的微生物。为此，我们提出一种全新策略：将基于网络的算法MMvec与自主开发的氘标记稳定同位素辅助代谢组学流程（2H-labeled Stable Isotope-Assisted Metabolomics pipeline, 2H-SIAM）相结合，以揭示土壤中污染物转化产物（transformation products, TPs）与微生物之间的关联。我们首先通过非生物胁迫构建土壤微生物组群落，并通过16S核糖体RNA基因测序对其进行表征。向土壤中添加芘与氘代芘（芘-d10）以进行生物降解实验，随后利用2H-SIAM获取芘的转化产物。再通过MMvec算法构建转化产物与微生物组之间的共现网络。研究结果证实，假单胞菌属（Pseudomonas）与苯基杆菌属（Phenylobacterium）参与了芘的氧化、矿化与甲基化过程；鞘氨醇单胞菌属（Sphingomonas）与酸杆菌门（Acidobacteria）则参与了芘的氧化过程。所获得的共现网络与已发表的DNA-SIP研究结果高度吻合，证明了该共现网络的性能与可靠性。综上，本研究首次描绘了环境基质中污染物转化产物与微生物之间的关联，相较于其他基于同位素的分析方法，本方法展现出独特优势。MMvec的安装方法请参考https://github.com/biocore/mmvec。本研究在qiime2-2020.6平台上运行MMvec，所用代码来自一份.doc文档。此外，本数据集还提供了用于MMvec评估的两份必要文件："lcms_nt.txt"与"otus_nt.txt"。