J Integr Plant Biol. ›› 2024, Vol. 66 ›› Issue (11): 2329-2345.DOI: 10.1111/jipb.13774  cstr: 32098.14.jipb.13774

• New Technology • Previous Articles     Next Articles

MetMiner: A user-friendly pipeline for large-scale plant metabolomics data analysis

Xiao Wang1†, Shuang Liang1†, Wenqi Yang1†, Ke Yu1†, Fei Liang1, Bing Zhao1, Xiang Zhu2, Chao Zhou3, Luis A. J. Mur4, Jeremy A. Roberts5, Junli Zhang1* and Xuebin Zhang1‡*   

  1. 1. State Key Laboratory of Crop Stress Adaptation and Improvement, Henan Joint International Laboratory for Crop Multi‐Omics Research, School of Life Sciences, Henan University, Kaifeng 475004, China
    2. Thermo Fisher Scientific, Shanghai 201206, China
    3. Waters Technologies Shanghai Ltd, Shanghai 201206, China
    4. Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth SY23 3FL, UK
    5. Faculty of Science and Engineering, School of Biological & Marine Sciences, University of Plymouth, PL4 8AA, UK
    These authors contributed equally.Lead contact.
    *Correspondences: Junli Zhang (zhangjunli0522@163.com); Xuebin Zhang (xuebinzhang@henu.edu.cn; Dr. Zhang is fully responsible for the distribution of all materials associated with this article)
  • Received:2024-01-10 Accepted:2024-08-16 Online:2024-09-10 Published:2024-11-01
  • Supported by:
    This work was supported by the National Key Research and Development Program of China (No. 2021YFA1300401), and the Henan Key Scientific Research Programs to Universities and Colleges (No. 22ZX006, to X.Z.), as well as the National Natural Science Foundation of China (No. 31970323 and 32170269, to X.Z.), and the Postdoctoral Research Initiation Program in Henan Province (No. HN2022138 to X.W.). L.A.J.M's contribution is aided by the Biotechnology and Biological Sciences Research Council (BBSRC, UK) “A China‐UK joint phenomics consortium to dissect the basis of crop stress resistance in the face of climate change” (grant no. BB/R02118X/1), exchange grant.

Abstract: The utilization of metabolomics approaches to explore the metabolic mechanisms underlying plant fitness and adaptation to dynamic environments is growing, highlighting the need for an efficient and user-friendly toolkit tailored for analyzing the extensive datasets generated by metabolomics studies. Current protocols for metabolome data analysis often struggle with handling large-scale datasets or require programming skills. To address this, we present MetMiner (https://github.com/ShawnWx2019/MetMiner), a user-friendly, full-functionality pipeline specifically designed for plant metabolomics data analysis. Built on R shiny, MetMiner can be deployed on servers to utilize additional computational resources for processing large-scale datasets. MetMiner ensures transparency, traceability, and reproducibility throughout the analytical process. Its intuitive interface provides robust data interaction and graphical capabilities, enabling users without prior programming skills to engage deeply in data analysis. Additionally, we constructed and integrated a plant-specific mass spectrometry database into the MetMiner pipeline to optimize metabolite annotation. We have also developed MDAtoolkits, which include a complete set of tools for statistical analysis, metabolite classification, and enrichment analysis, to facilitate the mining of biological meaning from the datasets. Moreover, we propose an iterative weighted gene co-expression network analysis strategy for efficient biomarker metabolite screening in large-scale metabolomics data mining. In two case studies, we validated MetMiner's efficiency in data mining and robustness in metabolite annotation. Together, the MetMiner pipeline represents a promising solution for plant metabolomics analysis, providing a valuable tool for the scientific community to use with ease.

Key words: data mining, iterative WGCNA, metabolomics, pipeline, shinyapp

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