用高分辨率质谱法对溶解有机物进行统计分析。

IF 4.5 Q1 MICROBIOLOGY
mLife Pub Date : 2025-04-14 eCollection Date: 2025-06-01 DOI:10.1002/mlf2.70002
Fanfan Meng, Ang Hu, Kyoung-Soon Jang, Jianjun Wang
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引用次数: 0

摘要

溶解有机物(DOM)含有数千个分子,通过与微生物相互作用,是水生和陆地生态系统生物地球化学循环的关键。在过去十年中,随着工具和统计方法的发展,DOM的研究得到了推进和加速。然而,由于分子组成的复杂性和潜在的生态机制,在统计分析、数据可视化和理论解释方面仍然具有挑战性。在这项研究中,我们开发了一个R包iDOM,用于傅立叶变换离子回旋共振质谱仪(FT-ICR MS)的基本和高级统计分析和DOM的可视化功能。该包可以处理DOM的各种数据类型,包括分子组成数据、分子特征和未表征的分子(即暗物质)。它可以整合解释性数据,如环境和微生物数据,以探索DOM与非生物或生物驱动因素之间的关系。为了说明它的用途,我们以实验变暖下DOM和微生物群落的示例数据集为例进行了案例研究。我们用实例研究了分子性状计算的基本函数,分子分类的分配,以及化学多样性和差异性的组成分析。我们进一步展示了具有先进功能的案例研究,以量化DOM组装过程,评估暗物质对分子相互作用的影响,分析DOM与微生物之间的生态网络,并探讨它们对变暖的响应。iDOM的源代码和示例数据集可在https://github.com/jianjunwang/iDOM上公开获得。我们期望iDOM将成为DOM统计分析的综合管道,并在理论框架中弥合化学表征和生态解释之间的差距。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
iDOM: Statistical analysis of dissolved organic matter characterized by high-resolution mass spectrometry.

Dissolved organic matter (DOM) contains thousands of molecules and is key for biogeochemical cycles in aquatic and terrestrial ecosystems by interacting with microbes. Over the last decade, the study of DOM has been advanced and accelerated with the developments of instrumental and statistical approaches. However, it is still challenging in statistical analyses, data visualization, and theoretical interpretations largely due to the complexity of molecular composition and underlying ecological mechanisms. In this study, we developed an R package iDOM with functions for the basic and advanced statistical analyses and the visualization of DOM derived from Fourier transform ion cyclotron resonance mass spectrometer (FT-ICR MS). The package could handle various data types of DOM, including molecular compositional data, molecular traits, and uncharacterized molecules (i.e., dark matter). It could integrate explanatory data, such as environmental and microbial data, to explore the relationships between DOM and abiotic or biotic drivers. To illustrate its use, we presented case studies with an example dataset of DOM and microbial communities under experimental warming. We included case studies of basic functions for the calculation of molecular traits, the assignment of molecular classes, and the compositional analyses of chemical diversity and dissimilarity. We further showed the case studies with advanced functions to quantify DOM assembly processes, assess the effects of dark matter on molecular interactions, analyze the ecological networks between DOM and microbes, and explore their response to warming. The source code and example dataset of iDOM are publicly available on https://github.com/jianjunwang/iDOM. We expect that iDOM will serve as a comprehensive pipeline for DOM statistical analyses and bridge the gap between chemical characterization and ecological interpretation in a theoretical framework.

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CiteScore
2.30
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