Network-based strategies in metabolomics data analysis and interpretation: from molecular networking to biological interpretation

References

• Toubiana D, Fernie AR, Nikoloski Z, et al. Network analysis: tackling complex data to study plant metabolism. Trends Biotechnol. 2013 January 1;31(1):29–36.
   PubMed Web of Science ®Google Scholar
• Beleggia R, Omranian N, Holtz Y, et al. Comparative analysis based on transcriptomics and metabolomics data reveal differences between emmer and durum wheat in response to nitrogen starvation. bioRxiv. 2020. https://doi.org/10.1101/2020.02.03.931717.
   Google Scholar
• Frainay C, Jourdan F. Computational methods to identify metabolic sub-networks based on metabolomic profiles. Brief Bioinform. 2016;18(1):43–56.
   PubMed Web of Science ®Google Scholar
• Jeong H, Tombor B, Albert R, et al. The large-scale organization of metabolic networks. Nature. 2000 October 01;407(6804):651–654.
   PubMed Web of Science ®Google Scholar
• Barabási A-L, Oltvai ZN. Network biology: understanding the cell’s functional organization. Nat Rev Genet. 2004 Feb 1;5(2):101–113.
   PubMed Web of Science ®Google Scholar
• Arita M. The metabolic world of Escherichia coli is not small. Proc Natl Acad Sci U S A. 2004;101(6):1543–1547.
   PubMed Web of Science ®Google Scholar
• Batushansky A, Matsuzaki S, Newhardt MF, et al. GC–MS metabolic profiling reveals fructose-2,6-bisphosphate regulates branched chain amino acid metabolism in the heart during fasting [journal article]. Metabolomics. 2019 January 28;15(2):18.
   PubMed Web of Science ®Google Scholar
• Faust K, Croes D, van Helden J. Prediction of metabolic pathways from genome-scale metabolic networks. Biosystems. 2011 August 1;105(2):109–121.
   PubMed Web of Science ®Google Scholar
• Yuan J, Bennett BD, Rabinowitz JD. Kinetic flux profiling for quantitation of cellular metabolic fluxes. Nat Protoc. 2008 August 1;3(8):1328–1340.
   PubMed Web of Science ®Google Scholar
• Orth JD, Thiele I, Palsson BØ. What is flux balance analysis? Nat Biotechnol. 2010 March 1;28(3):245–248.
   PubMed Web of Science ®Google Scholar
• Töpfer N, Kleessen S, Nikoloski Z. Integration of metabolomics data into metabolic networks [Review]. Front Plant Sci. 2015 February 17;6(49). DOI:10.3389/fpls.2015.00049
   PubMedGoogle Scholar
• Basler G, Fernie Alisdair R, Nikoloski Z. Advances in metabolic flux analysis toward genome-scale profiling of higher organisms. Biosci Rep. 2018;38(6). DOI:10.1042/BSR20170224
   PubMedGoogle Scholar
• Bordbar A, Monk JM, King ZA, et al. Constraint-based models predict metabolic and associated cellular functions. Nat Rev Genet. 2014 Feb 1;15(2):107–120.
   PubMed Web of Science ®Google Scholar
• Nikoloski Z, Perez-Storey R, Sweetlove LJ. Inference and prediction of metabolic network fluxes. Plant Physiol. 2015;169(3):1443–1455.
   PubMed Web of Science ®Google Scholar
• Lisec J, Schauer N, Kopka J, et al. Gas chromatography mass spectrometry–based metabolite profiling in plants. Nat Protoc. 2006 Jun 01;1(1):387–396.
   PubMed Web of Science ®Google Scholar
• Tohge T, Fernie AR. Combining genetic diversity, informatics and metabolomics to facilitate annotation of plant gene function. Nat Protoc. 2010 Jun 10 online;5(6):1210.
   PubMed Web of Science ®Google Scholar
• Šimura J, Antoniadi I, Široká J, et al. Plant hormonomics: multiple phytohormone profiling by targeted metabolomics. Plant Physiol. 2018;177(2):476–489.
   PubMed Web of Science ®Google Scholar
• Morgenthal K, Weckwerth W, Steuer R. Metabolomic networks in plants: transitions from pattern recognition to biological interpretation. Biosystems. 2006;83(2):108–117.
   PubMed Web of Science ®Google Scholar
• Weckwerth W, Loureiro ME, Wenzel K, et al. Differential metabolic networks unravel the effects of silent plant phenotypes. Proc Natl Acad Sci U S A. 2004;101(20):7809–7814.
   PubMed Web of Science ®Google Scholar
• Angelovici R, Batushansky A, Deason N, et al. Network-guided GWAS improves identification of genes affecting free amino acids. Plant Physiol. 2017;173(1):872–886.
   PubMed Web of Science ®Google Scholar
• Steuer R. Review: on the analysis and interpretation of correlations in metabolomic data. Brief Bioinform. 2006;7(2):151–158.
   PubMed Web of Science ®Google Scholar
• Usadel B, Obayashi T, Mutwil M, et al. Co-expression tools for plant biology: opportunities for hypothesis generation and caveats. Plant Cell Environ. 2009;32(12):1633–1651.
   PubMed Web of Science ®Google Scholar
• Wisecaver JH, Borowsky AT, Tzin V, et al. A global coexpression network approach for connecting genes to specialized metabolic pathways in plants. Plant Cell. 2017;29(5):944–959.
   PubMed Web of Science ®Google Scholar
• Almquist J, Cvijovic M, Hatzimanikatis V, et al. Kinetic models in industrial biotechnology – improving cell factory performance. Metab Eng. 2014;24:38–60.
   PubMed Web of Science ®Google Scholar
• Camacho D, de la Fuente A, Mendes P. The origin of correlations in metabolomics data. Metabolomics. 2005;1(1):53–63.
   Web of Science ®Google Scholar
• Krumsiek J, Suhre K, Illig T, et al. Gaussian graphical modeling reconstructs pathway reactions from high-throughput metabolomics data. BMC Syst Biol. 2011;5(1):21.
   PubMed Web of Science ®Google Scholar
• de la Fuente A, Bing N, Hoeschele I, et al. Discovery of meaningful associations in genomic data using partial correlation coefficients. Bioinformatics. 2004;20(18):3565–3574.
   PubMed Web of Science ®Google Scholar
• Basu S, Duren W, Evans CR, et al. Sparse network modeling and metscape-based visualization methods for the analysis of large-scale metabolomics data. Bioinformatics. 2017;33(10):1545–1553.
   PubMed Web of Science ®Google Scholar
• Schäfer J, Strimmer K. Learning large‐scale graphical gaussian models from genomic data. AIP Conf Proc. 2005;776(1):263–276.
   Google Scholar
• Müller-Linow M, Weckwerth W, Hütt M-T. Consistency analysis of metabolic correlation networks. BMC Syst Biol. 2007 Sep 24;1(1):44.
   PubMedGoogle Scholar
• Friedman J, Hastie T, Tibshirani R. Sparse inverse covariance estimation with the graphical lasso. Biostatistics. 2007;9(3):432–441.
   PubMed Web of Science ®Google Scholar
• Song L, Langfelder P, Horvath S. Comparison of co-expression measures: mutual information, correlation, and model based indices. BMC Bioinformatics. 2012;13(1):328.
   PubMedGoogle Scholar
• Suarez-Diez M, Saccenti E. Effects of sample size and dimensionality on the performance of four algorithms for inference of association networks in metabonomics. J Proteome Res. 2015 Sep 4;14(12):5119–5130.
   PubMed Web of Science ®Google Scholar
• DiLeo MV, Strahan GD, den Bakker M, et al. Weighted correlation network analysis (WGCNA) applied to the tomato fruit metabolome. PloS One. 2011;6(10):e26683–e26683..
   PubMed Web of Science ®Google Scholar
• Saccenti E, Suarez-Diez M, Luchinat C, et al. Probabilistic networks of blood metabolites in healthy subjects as indicators of latent cardiovascular risk. J Proteome Res. 2015 Feb 6;14(2):1101–1111.
   PubMed Web of Science ®Google Scholar
• Fukushima A. DiffCorr: an R package to analyze and visualize differential correlations in biological networks. Gene. 2013;518(1):209–214.
   PubMed Web of Science ®Google Scholar
• Roessner U, Luedemann A, Brust D, et al. Metabolic profiling allows comprehensive phenotyping of genetically or environmentally modified plant systems. Plant Cell. 2001;13(1):11–29.
   PubMed Web of Science ®Google Scholar
• Obata T, Florian A, Timm S, et al. On the metabolic interactions of (photo)respiration. J Exp Bot. 2016;67(10):3003–3014.
   PubMed Web of Science ®Google Scholar
• Perez de Souza L, Garbowicz K, Brotman Y, et al. The acetate pathway supports flavonoid and lipid biosynthesis in Arabidopsis. Plant Physiol. 2020; 182(2):857–869.
   PubMed Web of Science ®Google Scholar
• Szymanski J, Jozefczuk S, Nikoloski Z, et al. Stability of metabolic correlations under changing environmental conditions in Escherichia coli–a systems approach. PloS One. 2009;4(10):e7441–e7441.
   PubMed Web of Science ®Google Scholar
• Rosato A, Tenori L, Cascante M, et al. From correlation to causation: analysis of metabolomics data using systems biology approaches [journal article]. Metabolomics. 2018 Feb 27;14(4):37.
   PubMed Web of Science ®Google Scholar
• Batushansky A, Toubiana D, Fait A. Correlation-based network generation, visualization, and analysis as a powerful tool in biological studies: a case study in cancer cell metabolism. Biomed Res Int. 2016;2016:9.
   Web of Science ®Google Scholar
• Shannon P, Markiel A, Ozier O, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003 Nov 1;13(11):2498–2504.
   PubMed Web of Science ®Google Scholar
• Fukushima A, Kusano M, Redestig H, et al. Metabolomic correlation-network modules in Arabidopsis based on a graph-clustering approach. BMC Syst Biol. 2011;5(1):1.
   PubMedGoogle Scholar
• Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics. 2008;9(1):559.
   PubMed Web of Science ®Google Scholar
• Wen B, Mei Z, Zeng C, et al. metaX: a flexible and comprehensive software for processing metabolomics data. BMC Bioinf. 2017;18(1):183.
   PubMed Web of Science ®Google Scholar
• Ma J, Karnovsky A, Afshinnia F, et al. Differential network enrichment analysis reveals novel lipid pathways in chronic kidney disease. Bioinformatics. 2019;35(18):3441–3452.
   PubMed Web of Science ®Google Scholar
• Faith JJ, Hayete B, Thaden JT, et al. Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles. PLoS Biol. 2007;5(1):e8–e8.
   PubMed Web of Science ®Google Scholar
• Jahagirdar S, Suarez-Diez M, Saccenti E. Simulation and reconstruction of metabolite–metabolite association networks using a metabolic dynamic model and correlation based algorithms. J Proteome Res. 2019 Mar 1;18(3):1099–1113.
   PubMed Web of Science ®Google Scholar
• Vignoli A, Tenori L, Giusti B, et al. Differential network analysis reveals metabolic determinants associated with mortality in acute myocardial infarction patients and suggests potential mechanisms underlying different clinical scores used to predict death. J Proteome Res. 2020;19(2):949–961.
   PubMed Web of Science ®Google Scholar
• Wolfender J-L, Nuzillard J-M, van der Hooft JJJ, et al. accelerating metabolite identification in natural product research: toward an ideal combination of liquid chromatography–high-resolution tandem mass spectrometry and NMR profiling, in silico databases, and chemometrics. Anal Chem. 2019;91(1):704–742.
   PubMed Web of Science ®Google Scholar
• Paglia G, Williams JP, Menikarachchi L, et al. Ion mobility derived collision cross sections to support metabolomics applications. Anal Chem. 2014;86(8):3985–3993.
   PubMed Web of Science ®Google Scholar
• Zhang X, Quinn K, Cruickshank-Quinn C, et al. The application of ion mobility mass spectrometry to metabolomics. Curr Opin Chem Biol. 2018;42:60–66.
   PubMed Web of Science ®Google Scholar
• Fox Ramos AE, Evanno L, Poupon E, et al. Natural products targeting strategies involving molecular networking: different manners, one goal [10.1039/C9NP00006B]. Nat Prod Rep. 2019;36(7):960–980.
   PubMed Web of Science ®Google Scholar
• Watrous J, Roach P, Alexandrov T, et al. Mass spectral molecular networking of living microbial colonies. Proc Nat Acad Sci. 2012;109(26):E1743–E1752.
   PubMed Web of Science ®Google Scholar
• Allard P-M, Péresse T, Bisson J, et al. Integration of molecular networking and in-Silico MS/MS fragmentation for natural products dereplication. Anal Chem. 2016 Mar 15;88(6):3317–3323.
   PubMed Web of Science ®Google Scholar
• Ueoka R, Uria AR, Reiter S, et al. Metabolic and evolutionary origin of actin-binding polyketides from diverse organisms. Nat Chem Biol. 2015 Sep 1;11(9):705–712.
   PubMed Web of Science ®Google Scholar
• Matsuda F, Nakabayashi R, Yang Z, et al. Metabolome-genome-wide association study dissects genetic architecture for generating natural variation in rice secondary metabolism. Plant J. 2015;81(1):13–23.
   PubMed Web of Science ®Google Scholar
• Bouslimani A, Porto C, Rath CM, et al. Molecular cartography of the human skin surface in 3D. Proc Nat Acad Sci. 2015;112(17):E2120–E2129.
   PubMed Web of Science ®Google Scholar
• Kwon Y-K, Ha IJ, Bae H-W, et al. Dose-dependent metabolic alterations in human cells exposed to gamma irradiation. Plos One. 2014;9(11):e113573.
   PubMed Web of Science ®Google Scholar
• Frank AM, Bandeira N, Shen Z, et al. Clustering millions of tandem mass spectra. J Proteome Res. 2008;7(1):113–122.
   PubMed Web of Science ®Google Scholar
• Yang JY, Sanchez LM, Rath CM, et al. Molecular networking as a dereplication strategy. J Nat Prod. 2013 September 27;76(9):1686–1699.
   PubMed Web of Science ®Google Scholar
• Wang M, Carver JJ, Phelan VV, et al. Sharing and community curation of mass spectrometry data with global natural products social molecular networking. Nat Biotechnol. 2016;34(8):828–837.
   PubMed Web of Science ®Google Scholar
• Tsugawa H, Cajka T, Kind T, et al. MS-DIAL: data-independent MS/MS deconvolution for comprehensive metabolome analysis. Nat Methods. 2015 May 4 online;12(6):523.
   PubMed Web of Science ®Google Scholar
• Pluskal T, Castillo S, Villar-Briones A, et al. MZmine 2: modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data. BMC Bioinformatics. 2010;11(1):395.
   PubMed Web of Science ®Google Scholar
• Röst HL, Sachsenberg T, Aiche S, et al. OpenMS: a flexible open-source software platform for mass spectrometry data analysis. Nat Methods. 2016 Sep 1;13(9):741–748.
   PubMed Web of Science ®Google Scholar
• Smith CA, Want EJ, O’Maille G, et al. XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal Chem. 2006 Feb 1;78(3):779–787.
   PubMed Web of Science ®Google Scholar
• Nothias LF, Petras D, Schmid R, et al. Feature-based molecular networking in the GNPS analysis environment. bioRxiv. 2019.DOI: https://doi.org/10.1101/812404
   Google Scholar
• da Silva RR, Wang M, Nothias L-F, et al. Propagating annotations of molecular networks using in silico fragmentation. PLoS Comput Biol. 2018;14(4):e1006089.
   PubMed Web of Science ®Google Scholar
• Ruttkies C, Schymanski EL, Wolf S, et al. MetFrag relaunched: incorporating strategies beyond in silico fragmentation [journal article]. J Cheminform. 2016 January 29;8(1):3.
   PubMedGoogle Scholar
• Gerlich M, Neumann S. MetFusion: integration of compound identification strategies. J Mass Spectrom. 2013;48(3):291–298.
   PubMed Web of Science ®Google Scholar
• van der Hooft JJJ, Wandy J, Barrett MP, et al. Topic modeling for untargeted substructure exploration in metabolomics. Proc Nat Acad Sci. 2016;113(48):13738–13743.
   PubMed Web of Science ®Google Scholar
• Aksenov A, Laponogov I, Zhang Z, et al. Algorithmic Learning for Auto-deconvolution of GC-MS Data to Enable Molecular Networking within GNPS. bioRxiv. 2020. https://doi.org/10.1101/2020.01.13.905091
   Google Scholar
• Perez de Souza L, Naake T, Tohge T, et al. From chromatogram to analyte to metabolite. How to pick horses for courses from the massive web resources for mass spectral plant metabolomics. GigaScience. 2017;6(7). DOI:10.1093/gigascience/gix037.
   PubMed Web of Science ®Google Scholar
• Morreel K, Saeys Y, Dima O, et al. Systematic structural characterization of metabolites in arabidopsis via candidate substrate-product pair networks. Plant Cell. 2014;26(3):929–945.
   PubMed Web of Science ®Google Scholar
• Perez de Souza L, Scossa F, Proost S, et al. Multi-tissue integration of transcriptomic and specialized metabolite profiling provides tools for assessing the common bean (Phaseolus vulgaris) metabolome. Plant J. 2019;97(6):1132–1153.
   PubMed Web of Science ®Google Scholar
• Hartmann AC, Petras D, Quinn RA, et al. Meta-mass shift chemical profiling of metabolomes from coral reefs. Proc Nat Acad Sci. 2017;114(44):11685–11690.
   PubMed Web of Science ®Google Scholar
• Naake T, Fernie AR. MetNet: metabolite network prediction from high-resolution mass spectrometry data in R aiding metabolite annotation. Anal Chem. 2019 Feb 5;91(3):1768–1772.
   PubMed Web of Science ®Google Scholar
• Kuhl C, Tautenhahn R, Böttcher C, et al. CAMERA: an integrated strategy for compound spectra extraction and annotation of liquid chromatography/mass spectrometry data sets. Anal Chem. 2012 Jan 3;84(1):283–289.
   PubMed Web of Science ®Google Scholar
• Forcisi S, Moritz F, Lucio M, et al. Solutions for low and high accuracy mass spectrometric data matching: a data-driven annotation strategy in nontargeted metabolomics. Anal Chem. 2015 Sep 1;87(17):8917–8924.
   PubMed Web of Science ®Google Scholar
• Kankainen M, Gopalacharyulu P, Holm L, et al. MPEA—metabolite pathway enrichment analysis. Bioinformatics. 2011;27(13):1878–1879.
   PubMed Web of Science ®Google Scholar
• Xia J, Wishart DS. MSEA: a web-based tool to identify biologically meaningful patterns in quantitative metabolomic data. Nucleic Acids Res. 2010;38(suppl_2):W71–W77.
   PubMed Web of Science ®Google Scholar
• Chagoyen M, Pazos F. MBRole: enrichment analysis of metabolomic data. Bioinformatics. 2011;27(5):730–731.
   PubMed Web of Science ®Google Scholar
• Marco-Ramell A, Palau-Rodriguez M, Alay A, et al. Evaluation and comparison of bioinformatic tools for the enrichment analysis of metabolomics data [journal article]. BMC Bioinformatics. 2018 Jan 2;19(1):1.
   PubMedGoogle Scholar
• Kanehisa M, Furumichi M, Tanabe M, et al. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 2016;45(D1):D353–D361.
   PubMed Web of Science ®Google Scholar
• Caspi R, Billington R, Keseler IM, et al. The metaCyc database of metabolic pathways and enzymes - a 2019 update. Nucleic Acids Res. 2020;48(D1):D445–D453.
   PubMed Web of Science ®Google Scholar
• Ivanisevic J, Want EJ. From samples to insights into metabolism: uncovering biologically relevant information in LC-HRMS metabolomics data. Metabolites. 2019;9(12):308.
   Web of Science ®Google Scholar
• Stanstrup J, Broeckling CD, Helmus R, et al. The metaRbolomics toolbox in bioconductor and beyond. Metabolites. 2019;9(10):200.
   Web of Science ®Google Scholar
• Aggio RBM, Ruggiero K, Villas-Bôas SG. Pathway activity profiling (PAPi): from the metabolite profile to the metabolic pathway activity. Bioinformatics. 2010;26(23):2969–2976.
   PubMed Web of Science ®Google Scholar
• Chong J, Soufan O, Li C, et al. MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis. Nucleic Acids Res. 2018;46(W1):W486–W494.
   PubMed Web of Science ®Google Scholar
• Cottret L, Frainay C, Chazalviel M, et al. MetExplore: collaborative edition and exploration of metabolic networks. Nucleic Acids Res. 2018;46(W1):W495–W502.
   PubMed Web of Science ®Google Scholar
• Kutmon M, van Iersel MP, Bohler A, et al. PathVisio 3: an extendable pathway analysis toolbox. PLoS Comput Biol. 2015;11(2):e1004085.
   PubMed Web of Science ®Google Scholar
• Barupal DK, Fiehn O. Chemical similarity enrichment analysis (ChemRICH) as alternative to biochemical pathway mapping for metabolomic datasets. Sci Rep. 2017 November 06;7(1):14567.
   PubMedGoogle Scholar
• Schilling CH, Schuster S, Palsson BO, et al. Metabolic pathway analysis: basic concepts and scientific applications in the post-genomic Era. Biotechnol Prog. 1999 Jan 1;15(3):296–303.
   PubMed Web of Science ®Google Scholar
• Ginsburg H. Caveat emptor: limitations of the automated reconstruction of metabolic pathways in plasmodium. Trends Parasitol. 2009;25(1):37–43.
   PubMed Web of Science ®Google Scholar
• Lacroix V, Cottret L, Thébault P, et al. An introduction to metabolic networks and their structural analysis. IEEE/ACM Trans Comput Biol Bioinform. 2008;5(4):594–617.
   PubMed Web of Science ®Google Scholar
• Arita M. Metabolic reconstruction using shortest paths. Simul Pract Theory. 2000 April 15;8(1):109–125.
   Google Scholar
• Holme P. Model validation of simple-graph representations of metabolism. J Royal Soc Interface. 2009;6(40):1027–1034.
   PubMed Web of Science ®Google Scholar
• Croes D, Couche F, Wodak SJ, et al. Inferring meaningful pathways in weighted metabolic networks. J Mol Biol. 2006 Feb 10;356(1):222–236.
   PubMed Web of Science ®Google Scholar
• Blum T, Kohlbacher O. MetaRoute: fast search for relevant metabolic routes for interactive network navigation and visualization. Bioinformatics. 2008;24(18):2108–2109.
   PubMed Web of Science ®Google Scholar
• Latendresse M, Krummenacker M, Karp PD. Optimal metabolic route search based on atom mappings. Bioinformatics. 2014;30(14):2043–2050.
   PubMed Web of Science ®Google Scholar
• Pertusi DA, Stine AE, Broadbelt LJ, et al. Efficient searching and annotation of metabolic networks using chemical similarity. Bioinformatics. 2014;31(7):1016–1024.
   PubMed Web of Science ®Google Scholar
• Frainay C, Aros S, Chazalviel M, et al. MetaboRank: network-based recommendation system to interpret and enrich metabolomics results. Bioinformatics. 2018;35(2):274–283.
   Web of Science ®Google Scholar
• Rahman SA, Torrance G, Baldacci L, et al. Reaction decoder tool (RDT): extracting features from chemical reactions. Bioinformatics. 2016;32(13):2065–2066.
   PubMed Web of Science ®Google Scholar
• Faust K, Dupont P, Callut J, et al. Pathway discovery in metabolic networks by subgraph extraction. Bioinformatics. 2010;26(9):1211–1218.
   PubMed Web of Science ®Google Scholar
• Milreu PV, Klein CC, Cottret L, et al. Telling metabolic stories to explore metabolomics data: a case study on the yeast response to cadmium exposure. Bioinformatics. 2013;30(1):61–70.
   PubMed Web of Science ®Google Scholar
• Weiss N, Barbier Saint Hilaire P, Colsch B, et al. Cerebrospinal fluid metabolomics highlights dysregulation of energy metabolism in overt hepatic encephalopathy. J Hepatol. 2016 Dec 1;65(6):1120–1130.
   PubMed Web of Science ®Google Scholar
• Sridharan VG, Bruinsma GB, Bale SS, et al. Metabolomic modularity analysis (MMA) to quantify human liver perfusion dynamics. Metabolites. 2017;7(4):58.
   PubMed Web of Science ®Google Scholar
• Li S, Park Y, Duraisingham S, et al. Predicting network activity from high throughput metabolomics. PLoS Comput Biol. 2013;9(7):e1003123.
   PubMed Web of Science ®Google Scholar
• Pirhaji L, Milani P, Leidl M, et al. Revealing disease-associated pathways by network integration of untargeted metabolomics. Nat Methods. 2016 Sep 1;13(9):770–776.
   PubMed Web of Science ®Google Scholar
• Alden N, Krishnan S, Porokhin V, et al. Biologically consistent annotation of metabolomics data. Anal Chem. 2017 December 19;89(24):13097–13104.
   PubMed Web of Science ®Google Scholar
• Allen F, Greiner R, Wishart D. Competitive fragmentation modeling of ESI-MS/MS spectra for putative metabolite identification [journal article]. Metabolomics. 2015 Feb 1;11(1):98–110.
   Web of Science ®Google Scholar
• Tautenhahn R, Cho K, Uritboonthai W, et al. An accelerated workflow for untargeted metabolomics using the METLIN database. Nat Biotechnol. 2012 Sep 1;30(9):826–828.
   PubMed Web of Science ®Google Scholar
• Wishart DS, Feunang YD, Marcu A, et al. HMDB 4.0: the human metabolome database for 2018. Nucleic Acids Res. 2017;46(D1):D608–D617.
   Web of Science ®Google Scholar
• Shen X, Wang R, Xiong X, et al. Metabolic reaction network-based recursivemetabolite annotation for untargeted metabolomics. Nat Commun. 2019 April 3;10(1):1516.
   PubMedGoogle Scholar
• Moreno P, Beisken S, Harsha B, et al. BiNChE: A web tool and library for chemical enrichment analysis based on the ChEBI ontology. BMC Bioinformatics. 2015 Feb 21;16(1):56.
   PubMedGoogle Scholar
• Djoumbou Feunang Y, Eisner R, Knox C, et al. ClassyFire: automated chemical classification with a comprehensive, computable taxonomy. J Cheminform. 2016 Nov 4;8(1):61.
   PubMedGoogle Scholar
• Ernst M, Kang KB, Caraballo-Rodríguez AM, et al. MolNetEnhancer: enhanced molecular networks by integrating metabolome mining and annotation tools. Metabolites. 2019;9(7):144.
   Web of Science ®Google Scholar
• Schwahn K, Beleggia R, Omranian N, et al. Stoichiometric correlation analysis: principles of metabolic functionality from metabolomics data [Original research]. Front Plant Sci. 2017 Dec 18;8(2152). DOI:10.3389/fpls.2017.02152.
   PubMedGoogle Scholar
• Öksüz M, Sadıkoğlu H, Çakır T. Sparsity as cellular objective to infer directed metabolic networks from steady-state metabolome data: a theoretical analysis. PloS One. 2013;8(12):e84505–e84505.
   PubMed Web of Science ®Google Scholar
• Sontag E, Kiyatkin A, Kholodenko BN. Inferring dynamic architecture of cellular networks using time series of gene expression, protein and metabolite data. Bioinformatics. 2004;20(12):1877–1886.
   PubMed Web of Science ®Google Scholar
• Alseekh S, Fernie AR. Metabolomics 20 years on: what have we learned and what hurdles remain? Plant J. 2018;94(6):933–942.
   PubMed Web of Science ®Google Scholar
• Nguyen DH, Nguyen CH, Mamitsuka H. ADAPTIVE: leArning DAta-dePendenT, concIse molecular VEctors for fast, accurate metabolite identification from tandem mass spectra. Bioinformatics. 2019;35(14):i164–i172.
   PubMed Web of Science ®Google Scholar
• Ji H, Xu Y, Lu H, et al. Deep MS/MS-aided structural-similarity scoring for unknown metabolite identification. Anal Chem. 2019 May 7;91(9):5629–5637.
   PubMed Web of Science ®Google Scholar
• Gabelica V, Marklund E. Fundamentals of ion mobility spectrometry. Curr Opin Chem Biol. 2018 Feb 1;42:51–59.
   PubMed Web of Science ®Google Scholar