New R package "iDOM" unlocks complex patterns in dissolved organic matter research
image: The iDOM package includes five example datasets: molecular intensity data, molecular element composition, environmental data, dark matter data, and microbial data. Using these datasets, the package addresses four main aims: (1) calculating molecular traits and assigning molecular classes; (2) analyzing DOM chemodiversity and dynamics, including (2.1) quantifying molecular diversity and dissimilarity, (2.2) explaining the assembly processes of DOM, and (2.3) assessing the environmental responses of DOM; (3) examining the effects of DOM dark matter on molecular interactions; and (4) quantifying DOM-microbe associations through ecological networks.
Credit: Jianjun Wang, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences
Dissolved organic matter (DOM) is a central component of biogeochemical cycles, interacting closely with microbial communities and influencing ecosystem processes in both aquatic and terrestrial environments. Despite its importance, DOM remains analytically difficult to study due to its molecular complexity and the challenge of linking composition to ecological function.
To overcome these barriers, a research team led by Prof. Jianjun Wang of the Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, has developed iDOM, an open-source R package designed to advance the statistical analysis, visualization, and ecological interpretation of DOM data derived from Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS).
Built with a modular architecture, iDOM provides a suite of tools organized around four core analytical aims:
Molecular Trait Analysis and Classification
Users can calculate chemical traits from molecular formulas using molTrait and molTrans, and group compounds based on these traits with molGroup.
Integration with Environmental Drivers
A suite of functions—including commTD, commFD, commDD, and commDis—allows for the analysis of DOM diversity, dissimilarity, and community structure. Functions such as commProc and iCER help infer assembly processes and environmental responses of DOM.
Analysis of Dark Matter (Uncharacterized Molecules)
Through the iDME function, users can assess how unidentified molecules influence molecular networks and interactions within DOM assemblages.
DOM-Microbe Network Analysis
With the H2 function, iDOM quantifies the interaction between DOM and microbial communities using bipartite network models.
The developers showcased iDOM's capabilities using a microcosm experiment simulating climate warming. In this case study, sediments from China’s Taihu Lake were sterilized and inoculated with microbial communities from temperate and subtropical lakes. The iDOM analyses revealed how warming influences DOM traits, community assembly processes, and molecular network structures, while also highlighting the ecological significance of uncharacterized molecules and DOM–microbe associations.
By providing a reproducible, transparent, and standardized framework, iDOM bridges the gap between molecular-level DOM chemistry and ecosystem-level processes. Its integration of abiotic and biotic data represents a significant advance in environmental data science.
The iDOM package is freely available, along with detailed documentation and example datasets, on GitHub: https://github.com/jianjunwang/iDOM.
How to cite this article:
Meng F, Hu A, Jang K-S, Wang J. iDOM: Statistical analysis of dissolved organic matter characterized by high-resolution mass spectrometry. mLife. 2025;4:319-331. https://doi.org/10.1002/mlf2.70002