NetNiche: Microbe-Metabolite Network Reconstruction and Microbial Niche Analysis.

IF 6.2 Q2 GENETICS & HEREDITY

Phenomics (Cham, Switzerland) Pub Date : 2025-03-07 eCollection Date: 2025-04-01 DOI:10.1007/s43657-024-00168-8

Lu Wang, Lequn Wang, Luonan Chen

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Abstract

Metagenomics and metabolomics technologies have been widely used to investigate the microbe-metabolite interactions in vivo. However, the computational methods that accurately infer the microbe-metabolite interactions are lacking. We present a context-aware framework for graph representation learning, NetNiche, which predicts microbe-metabolite and microbe-microbe interactions in an accurate manner, by integrating their abundance data with prior knowledge. We applied NetNiche to datasets on gut and soil microbiome, and demonstrated that NetNiche can outperform the state-of-the-art methods, such as SParse InversE Covariance Estimation for Ecological Association Inference (SPIEC-EASI), Sparse Correlations for Compositional data (SparCC) and microbe-metabolite vectors (mmvec). NetNiche is an effective tool with wide applicability for the multi-omics study of human microbiome.

Supplementary information: The online version contains supplementary material available at 10.1007/s43657-024-00168-8.

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微生物-代谢物网络重构和微生物生态位分析。

宏基因组学和代谢组学技术已被广泛用于研究微生物-代谢物在体内的相互作用。然而，准确推断微生物-代谢物相互作用的计算方法是缺乏的。我们提出了一个上下文感知框架，用于图表示学习，NetNiche，它通过将微生物-代谢物和微生物-微生物的丰度数据与先验知识相结合，以准确的方式预测微生物-代谢物和微生物-微生物的相互作用。我们将NetNiche应用于肠道和土壤微生物组的数据集，并证明了NetNiche可以优于最先进的方法，如生态关联推断的稀疏逆协方差估计（SPIEC-EASI），成分数据的稀疏相关性（SparCC）和微生物代谢载体（mmvec）。NetNiche是一种广泛适用于人类微生物组学多组学研究的有效工具。补充资料：在线版本包含补充资料，下载地址：10.1007/s43657-024-00168-8。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Phenomics (Cham, Switzerland)

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