MetaX: a Flexible and Comprehensive Software for Processing Metabolomics Data

Overview

Journal BMC Bioinformatics

Publisher Biomed Central

Specialty Biology

Date 2017 Mar 23

PMID 28327092

Citations 328

Authors

Bo Wen

Zhanlong Mei

Chunwei Zeng

Siqi Liu

Affiliations

Soon will be listed here.

Abstract

Background: Non-targeted metabolomics based on mass spectrometry enables high-throughput profiling of the metabolites in a biological sample. The large amount of data generated from mass spectrometry requires intensive computational processing for annotation of mass spectra and identification of metabolites. Computational analysis tools that are fully integrated with multiple functions and are easily operated by users who lack extensive knowledge in programing are needed in this research field.

Results: We herein developed an R package, metaX, that is capable of end-to-end metabolomics data analysis through a set of interchangeable modules. Specifically, metaX provides several functions, such as peak picking and annotation, data quality assessment, missing value imputation, data normalization, univariate and multivariate statistics, power analysis and sample size estimation, receiver operating characteristic analysis, biomarker selection, pathway annotation, correlation network analysis, and metabolite identification. In addition, metaX offers a web-based interface ( http://metax.genomics.cn ) for data quality assessment and normalization method evaluation, and it generates an HTML-based report with a visualized interface. The metaX utilities were demonstrated with a published metabolomics dataset on a large scale. The software is available for operation as either a web-based graphical user interface (GUI) or in the form of command line functions. The package and the example reports are available at http://metax.genomics.cn/ .

Conclusions: The pipeline of metaX is platform-independent and is easy to use for analysis of metabolomics data generated from mass spectrometry.

Citing Articles

Biliary-intestinal anastomosis leads to alterations in intestinal flora and its flora metabolites and increases the risk of long-term postoperative complications: a case-control study.

Li B, Chen Z, Wang G, Chen Y, Hou X, Lu B Front Microbiol. 2025; 16:1531955.

PMID: 40078541 PMC: 11900546. DOI: 10.3389/fmicb.2025.1531955.

Integrated Metabolomics and Transcriptomics Analyses Identify Key Amino Acid Metabolic Mechanisms in SMN-LBK.

Shen J, Du Y, Shen Y, Kang N, Fan Z, Fang Z Foods. 2025; 14(5).

PMID: 40077433 PMC: 11899468. DOI: 10.3390/foods14050730.

Effect of Photoperiod on Ascorbic Acid Metabolism Regulation and Accumulation in Rapeseed ( L.) Seedlings.

Wang C, Kuang L, Tian Z, Wang X, Tu J, Wang H Antioxidants (Basel). 2025; 14(2).

PMID: 40002347 PMC: 11851679. DOI: 10.3390/antiox14020160.

Non-targeted Metabolomics Reveals the Potential Role of MFSD8 in Metabolism in Human Endothelial Cells.

Xiang Q, Chen Y, Cheng X, Fang X, Liu Y, Huang Y Mol Biotechnol. 2025; .

PMID: 39992484 DOI: 10.1007/s12033-025-01396-7.

Identification of GDP as a small inhibitory molecule in HepG2 cells by non‑targeted metabolomics analysis.

Peng Z, Xu S, Wang H, Huang Y, Liu S, Jiao Z Oncol Lett. 2025; 29(4):178.

PMID: 39990806 PMC: 11843412. DOI: 10.3892/ol.2025.14924.

References

Fukushima A . DiffCorr: an R package to analyze and visualize differential correlations in biological networks. Gene. 2012; 518(1):209-14. DOI: 10.1016/j.gene.2012.11.028. View

Kamburov A, Cavill R, Ebbels T, Herwig R, Keun H . Integrated pathway-level analysis of transcriptomics and metabolomics data with IMPaLA. Bioinformatics. 2011; 27(20):2917-8. DOI: 10.1093/bioinformatics/btr499. View

Kanehisa M, Goto S . KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 1999; 28(1):27-30. PMC: 102409. DOI: 10.1093/nar/28.1.27. View

Kirwan J, Broadhurst D, Davidson R, Viant M . Characterising and correcting batch variation in an automated direct infusion mass spectrometry (DIMS) metabolomics workflow. Anal Bioanal Chem. 2013; 405(15):5147-57. DOI: 10.1007/s00216-013-6856-7. View

Gehlenborg N, Noble M, Getz G, Chin L, Park P . Nozzle: a report generation toolkit for data analysis pipelines. Bioinformatics. 2013; 29(8):1089-91. PMC: 3624805. DOI: 10.1093/bioinformatics/btt085. View

van den Berg R, Hoefsloot H, Westerhuis J, Smilde A, van der Werf M . Centering, scaling, and transformations: improving the biological information content of metabolomics data. BMC Genomics. 2006; 7:142. PMC: 1534033. DOI: 10.1186/1471-2164-7-142. View

Wang Y, Xiao J, Suzek T, Zhang J, Wang J, Bryant S . PubChem: a public information system for analyzing bioactivities of small molecules. Nucleic Acids Res. 2009; 37(Web Server issue):W623-33. PMC: 2703903. DOI: 10.1093/nar/gkp456. View

Shannon P, Markiel A, Ozier O, Baliga N, Wang J, Ramage D . Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003; 13(11):2498-504. PMC: 403769. DOI: 10.1101/gr.1239303. View

Edmands W, Barupal D, Scalbert A . MetMSLine: an automated and fully integrated pipeline for rapid processing of high-resolution LC-MS metabolomic datasets. Bioinformatics. 2014; 31(5):788-90. PMC: 4341062. DOI: 10.1093/bioinformatics/btu705. View

10.

Ernest B, Gooding J, Campagna S, Saxton A, Voy B . MetabR: an R script for linear model analysis of quantitative metabolomic data. BMC Res Notes. 2012; 5:596. PMC: 3532230. DOI: 10.1186/1756-0500-5-596. View

11.

Sud M, Fahy E, Cotter D, Brown A, Dennis E, Glass C . LMSD: LIPID MAPS structure database. Nucleic Acids Res. 2006; 35(Database issue):D527-32. PMC: 1669719. DOI: 10.1093/nar/gkl838. View

12.

Smith C, Want E, OMaille G, Abagyan R, Siuzdak G . XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal Chem. 2006; 78(3):779-87. DOI: 10.1021/ac051437y. View

13.

Frederich M, Pirotte B, Fillet M, de Tullio P . Metabolomics as a Challenging Approach for Medicinal Chemistry and Personalized Medicine. J Med Chem. 2016; 59(19):8649-8666. DOI: 10.1021/acs.jmedchem.5b01335. View

14.

Dunn W, Broadhurst D, Begley P, Zelena E, Francis-McIntyre S, Anderson N . Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nat Protoc. 2011; 6(7):1060-83. DOI: 10.1038/nprot.2011.335. View

15.

Wishart D, Jewison T, Guo A, Wilson M, Knox C, Liu Y . HMDB 3.0--The Human Metabolome Database in 2013. Nucleic Acids Res. 2012; 41(Database issue):D801-7. PMC: 3531200. DOI: 10.1093/nar/gks1065. View

16.

Feng Q, Liu Z, Zhong S, Li R, Xia H, Jie Z . Integrated metabolomics and metagenomics analysis of plasma and urine identified microbial metabolites associated with coronary heart disease. Sci Rep. 2016; 6:22525. PMC: 4773756. DOI: 10.1038/srep22525. View

17.

Kuhl C, Tautenhahn R, Bottcher C, Larson T, Neumann S . CAMERA: an integrated strategy for compound spectra extraction and annotation of liquid chromatography/mass spectrometry data sets. Anal Chem. 2011; 84(1):283-9. PMC: 3658281. DOI: 10.1021/ac202450g. View

18.

Fernandez-Albert F, Llorach R, Andres-Lacueva C, Perera A . An R package to analyse LC/MS metabolomic data: MAIT (Metabolite Automatic Identification Toolkit). Bioinformatics. 2014; 30(13):1937-9. PMC: 4071204. DOI: 10.1093/bioinformatics/btu136. View

19.

Rochfort S . Metabolomics reviewed: a new "omics" platform technology for systems biology and implications for natural products research. J Nat Prod. 2005; 68(12):1813-20. DOI: 10.1021/np050255w. View

20.

Thevenot E, Roux A, Xu Y, Ezan E, Junot C . Analysis of the Human Adult Urinary Metabolome Variations with Age, Body Mass Index, and Gender by Implementing a Comprehensive Workflow for Univariate and OPLS Statistical Analyses. J Proteome Res. 2015; 14(8):3322-35. DOI: 10.1021/acs.jproteome.5b00354. View