Leibniz Institute of Plant Biochemistry, Stress and Developmental Biology, Weinberg 3, 06120 Halle (Saale), Germany.
Institute of Biology/Geobotany and Botanical Garden, Martin Luther University Halle Wittenberg, Am Kirchtor 1, 06108 Halle (Saale), Germany.
Sci Data. 2018 Aug 28;5:180179. doi: 10.1038/sdata.2018.179.
In Eco-Metabolomics interactions are studied of non-model organisms in their natural environment and relations are made between biochemistry and ecological function. Current challenges when processing such metabolomics data involve complex experiment designs which are often carried out in large field campaigns involving multiple study factors, peak detection parameter settings, the high variation of metabolite profiles and the analysis of non-model species with scarcely characterised metabolomes. Here, we present a dataset generated from 108 samples of nine bryophyte species obtained in four seasons using an untargeted liquid chromatography coupled with mass spectrometry acquisition method (LC/MS). Using this dataset we address the current challenges when processing Eco-Metabolomics data. Here, we also present a reproducible and reusable computational workflow implemented in Galaxy focusing on standard formats, data import, technical validation, feature detection, diversity analysis and multivariate statistics. We expect that the representative dataset and the reusable processing pipeline will facilitate future studies in the research field of Eco-Metabolomics.
在生态代谢组学中，研究的是自然环境中非模式生物的相互作用，并在生物化学和生态功能之间建立联系。目前，在处理此类代谢组学数据时面临的挑战包括复杂的实验设计，这些设计通常在涉及多个研究因素、峰检测参数设置、代谢物谱高度变化以及对代谢组学特征描述甚少的非模式物种进行的大型野外活动中进行。在这里，我们提供了一个使用非靶向液相色谱-质谱联用（LC/MS）采集方法从四个季节的 9 种苔藓物种中获得的 108 个样本生成的数据集。使用这个数据集，我们解决了处理生态代谢组学数据时当前面临的挑战。在这里，我们还提出了一个可重复使用的计算工作流程，该流程在 Galaxy 中实现，重点是标准格式、数据导入、技术验证、特征检测、多样性分析和多元统计。我们希望有代表性的数据集和可重复使用的处理管道将促进生态代谢组学研究领域的未来研究。
