Metabolomics: A High-Throughput Platform for Metabolite Profile Exploration

  • Jing Cheng
  • Wenxian Lan
  • Guangyong Zheng
  • Xianfu Gao
Part of the Methods in Molecular Biology book series (MIMB, volume 1754)


Metabolomics aims to quantitatively measure small-molecule metabolites in biological samples, such as bodily fluids (e.g., urine, blood, and saliva), tissues, and breathe exhalation, which reflects metabolic responses of a living system to pathophysiological stimuli or genetic modification. In the past decade, metabolomics has made notable progresses in providing useful systematic insights into the underlying mechanisms and offering potential biomarkers of many diseases. Metabolomics is a complementary manner of genomics and transcriptomics, and bridges the gap between genotype and phenotype, which reflects the functional output of a biological system interplaying with environmental factors. Recently, the technology of metabolomics study has been developed quickly. This review will discuss the whole pipeline of metabolomics study, including experimental design, sample collection and preparation, sample detection and data analysis, as well as mechanism interpretation, which can help understand metabolic effects and metabolite function for living organism in system level.

Key words

Metabolomics Metabolite profile Metabolic response Biomarker Underlying mechanism 


  1. 1.
    Fiehn O (2002) Metabolomics—the link between genotypes and phenotypes. Plant Mol Biol 48(1–2):155–171CrossRefGoogle Scholar
  2. 2.
    Nicholson JK, Lindon JC, Holmes E (1999) ‘Metabonomics’: understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica 29(11):1181–1189. Scholar
  3. 3.
    Cacciatore S, Loda M (2015) Innovation in metabolomics to improve personalized healthcare. Ann N Y Acad Sci 1346(1):57–62. Scholar
  4. 4.
    Naz S, Garcia A, Barbas C (2013) Multiplatform analytical methodology for metabolic fingerprinting of lung tissue. Anal Chem 85(22):10941–10948CrossRefGoogle Scholar
  5. 5.
    Gowda GA, Djukovic D (2014) Overview of mass spectrometry-based metabolomics: opportunities and challenges. Methods Mol Biol 1198:3–12. Scholar
  6. 6.
    Naz S, Vallejo M, Garcia A, Barbas C (2014) Method validation strategies involved in non-targeted metabolomics. J Chromatogr A 1353:99–105. Scholar
  7. 7.
    Gao X, Zhao A, Zhou M, Lin J, Qiu Y, Su M, Jia W (2011) GC/MS-based urinary metabolomics reveals systematic differences in metabolism and ethanol response between Sprague–Dawley and Wistar rats. Metabolomics 7(3):363–374. Scholar
  8. 8.
    Sumner LW, Amberg A, Barrett D, Beale MH, Beger R, Daykin CA, Fan TW, Fiehn O, Goodacre R, Griffin JL, Hankemeier T, Hardy N, Harnly J, Higashi R, Kopka J, Lane AN, Lindon JC, Marriott P, Nicholls AW, Reily MD, Thaden JJ, Viant MR (2007) Proposed minimum reporting standards for chemical analysis Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI). Metabolomics 3(3):211–221. Scholar
  9. 9.
    Hart CD, Vignoli A, Tenori L, Uy GL, Van To T, Adebamowo C, Hossain SM, Biganzoli L, Risi E, Love RR, Luchinat C, Di Leo A (2017) Serum metabolomic profiles identify ER-positive early breast cancer patients at increased risk of disease recurrence in a multicenter population. Clin Cancer Res 23(6):1422–1431. Scholar
  10. 10.
    Westerhuis JA, van Velzen EJ, Hoefsloot HC, Smilde AK (2010) Multivariate paired data analysis: multilevel PLSDA versus OPLSDA. Metabolomics 6(1):119–128. Scholar
  11. 11.
    Gregory JF III, Park Y, Lamers Y, Bandyopadhyay N, Chi YY, Lee K, Kim S, da Silva V, Hove N, Ranka S, Kahveci T, Muller KE, Stevens RD, Newgard CB, Stacpoole PW, Jones DP (2013) Metabolomic analysis reveals extended metabolic consequences of marginal vitamin B-6 deficiency in healthy human subjects. PLoS One 8(6):e63544. Scholar
  12. 12.
    Ghini V, Unger FT, Tenori L, Turano P, Juhl H, David KA (2015) Metabolomics profiling of pre- and post-anesthesia plasma samples of colorectal patients obtained via Ficoll separation. Metabolomics 11(6):1769–1778. Scholar
  13. 13.
    van Velzen EJ, Westerhuis JA, van Duynhoven JP, van Dorsten FA, Hoefsloot HC, Jacobs DM, Smit S, Draijer R, Kroner CI, Smilde AK (2008) Multilevel data analysis of a crossover designed human nutritional intervention study. J Proteome Res 7(10):4483–4491. Scholar
  14. 14.
    Vuckovic D (2012) Current trends and challenges in sample preparation for global metabolomics using liquid chromatography-mass spectrometry. Anal Bioanal Chem 403(6):1523–1548. Scholar
  15. 15.
    Hund E, Vander Heyden Y, Massart DL, Smeyers-Verbeke J (2002) Derivation of system suitability test limits from a robustness test on an LC assay with complex antibiotic samples. J Pharmaceut Biomed 30(4):1197–1206CrossRefGoogle Scholar
  16. 16.
    t’Kindt R, Morreel K, Deforce D, Boerjan W, Van Bocxlaer J (2009) Joint GC-MS and LC-MS platforms for comprehensive plant metabolomics: repeatability and sample pre-treatment. J Chromatogr B 877(29):3572–3580CrossRefGoogle Scholar
  17. 17.
    Álvarez-Sánchez B, Priego-Capote F, Luque de Castro MD (2010) Metabolomics analysis II. Preparation of biological samples prior to detection. TrAC Trends Anal Chem 29(2):120–127. Scholar
  18. 18.
    Naz S, Moreira dos Santos DC, García A, Barbas C (2014) Analytical protocols based on LC–MS, GC–MS and CE–MS for nontargeted metabolomics of biological tissues. Bioanalysis 6(12):1657–1677CrossRefGoogle Scholar
  19. 19.
    Want EJ, Masson P, Michopoulos F, Wilson ID, Theodoridis G, Plumb RS, Shockcor J, Loftus N, Holmes E, Nicholson JK (2013) Global metabolic profiling of animal and human tissues via UPLC-MS. Nat Protoc 8(1):17–32. Scholar
  20. 20.
    Ly-Verdu S, Schaefer A, Kahle M, Groeger T, Neschen S, Arteaga-Salas JM, Ueffing M, de Angelis MH, Zimmermann R (2014) The impact of blood on liver metabolite profiling - a combined metabolomic and proteomic approach. Biomed Chromatogr 28(2):231–240. Scholar
  21. 21.
    Dunn WB, Broadhurst D, Ellis DI, Brown M, Halsall A, O’Hagan S, Spasic I, Tseng A, Kell DB (2008) A GC-TOF-MS study of the stability of serum and urine metabolomes during the UK Biobank sample collection and preparation protocols. Int J Epidemiol 37(Suppl 1):i23–i30. Scholar
  22. 22.
    Fernández-Peralbo MA, Luque de Castro MD (2012) Preparation of urine samples prior to targeted or untargeted metabolomics mass-spectrometry analysis. TrAC Trends Anal Chem 41:75–85. Scholar
  23. 23.
    Want EJ, Wilson ID, Gika H, Theodoridis G, Plumb RS, Shockcor J, Holmes E, Nicholson JK (2010) Global metabolic profiling procedures for urine using UPLC-MS. Nat Protoc 5(6):1005–1018CrossRefGoogle Scholar
  24. 24.
    Gika HG, Theodoridis GA, Wilson ID (2008) Liquid chromatography and ultra-performance liquid chromatography–mass spectrometry fingerprinting of human urine. J Chromatogr A 1189(1):314–322. Scholar
  25. 25.
    Winder CL, Dunn WB, Schuler S, Broadhurst D, Jarvis R, Stephens GM, Goodacre R (2008) Global metabolic profiling of Escherichia coli cultures: an evaluation of methods for quenching and extraction of intracellular metabolites. Anal Chem 80(8):2939–2948. Scholar
  26. 26.
    Meyer H, Weidmann H, Lalk M (2013) Methodological approaches to help unravel the intracellular metabolome of Bacillus subtilis. Microb Cell Factories 12:69. Scholar
  27. 27.
    Gao X, Pujos-Guillot E, Martin JF, Galan P, Juste C, Jia W, Sebedio JL (2009) Metabolite analysis of human fecal water by gas chromatography/mass spectrometry with ethyl chloroformate derivatization. Anal Biochem 393(2):163–175. Scholar
  28. 28.
    Gratton J, Phetcharaburanin J, Mullish BH, Williams HR, Thursz M, Nicholson JK, Holmes E, Marchesi JR, Li JV (2016) Optimized sample handling strategy for metabolic profiling of human feces. Anal Chem 88(9):4661–4668. Scholar
  29. 29.
    Zhou B, Xiao JF, Tuli L, Ressom HW (2012) LC-MS-based metabolomics. Mol BioSyst 8(2):470–481. Scholar
  30. 30.
    Raterink R-J, Lindenburg PW, Vreeken RJ, Ramautar R, Hankemeier T (2014) Recent developments in sample-pretreatment techniques for mass spectrometry-based metabolomics. TrAC Trends Anal Chem 61:157–167. Scholar
  31. 31.
    Gowda GAN, Raftery D (2014) Quantitating metabolites in protein precipitated serum using NMR spectroscopy. Anal Chem 86(11):5433–5440CrossRefGoogle Scholar
  32. 32.
    Michopoulos F, Lai L, Gika H, Theodoridis G, Wilson I (2009) UPLC-MS-based analysis of human plasma for metabonomics using solvent precipitation or solid phase extraction. J Proteome Res 8(4):2114–2121CrossRefGoogle Scholar
  33. 33.
    Masson P, Alves AC, Ebbels TM, Nicholson JK, Want EJ (2010) Optimization and evaluation of metabolite extraction protocols for untargeted metabolic profiling of liver samples by UPLC-MS. Anal Chem 82(18):7779–7786. Scholar
  34. 34.
    Gao X, Chen W, Li R, Wang M, Chen C, Zeng R, Deng Y (2012) Systematic variations associated with renal disease uncovered by parallel metabolomics of urine and serum. BMC Syst Biol 6(Suppl 1):S14. Scholar
  35. 35.
    Liang X, Ubhayakar S, Liederer BM, Dean B, Ran-Ran Qin A, Shahidi-Latham S, Deng Y (2011) Evaluation of homogenization techniques for the preparation of mouse tissue samples to support drug discovery. Bioanalysis 3(17):1923–1933. Scholar
  36. 36.
    Han J, Lin K, Sequeira C, Borchers CH (2015) An isotope-labeled chemical derivatization method for the quantitation of short-chain fatty acids in human feces by liquid chromatography-tandem mass spectrometry. Anal Chim Acta 854:86–94. Scholar
  37. 37.
    Gao X, Pujos-Guillot E, Sebedio JL (2010) Development of a quantitative metabolomic approach to study clinical human fecal water metabolome based on trimethylsilylation derivatization and GC/MS analysis. Anal Chem 82(15):6447–6456. Scholar
  38. 38.
    A J, Trygg J, Gullberg J, Johansson AI, Jonsson P, Antti H, Marklund SL, Moritz T (2005) Extraction and GC/MS analysis of the human blood plasma metabolome. Anal Chem 77(24):8086–8094. Scholar
  39. 39.
    Dunn WB, Broadhurst D, Begley P, Zelena E, Francis-McIntyre S, Anderson N, Brown M, Knowles JD, Halsall A, Haselden JN, Nicholls AW, Wilson ID, Kell DB, Goodacre R, Human Serum Metabolome Consortium (2011) Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nat Protoc 6(7):1060–1083. Scholar
  40. 40.
    Husek P (1991) Amino acid derivatization and analysis in five minutes. FEBS Lett 280(2):354–356CrossRefGoogle Scholar
  41. 41.
    Husek P (1998) Chloroformates in gas chromatography as general purpose derivatizing agents. J Chromatogr B Biomed Sci Appl 717(1–2):57–91CrossRefGoogle Scholar
  42. 42.
    Zhao L, Ni Y, Su M, Li H, Dong F, Chen W, Wei R, Zhang L, Guiraud SP, Martin FP, Rajani C, Xie G, Jia W (2017) High throughput and quantitative measurement of microbial metabolome by gas chromatography/mass spectrometry using automated alkyl chloroformate derivatization. Anal Chem 89(10):5565–5577. Scholar
  43. 43.
    Villas-Boas SG, Smart KF, Sivakumaran S, Lane GA (2011) Alkylation or silylation for analysis of amino and non-amino organic acids by GC-MS? Metabolites 1(1):3–20. Scholar
  44. 44.
    Guo K, Li L (2009) Differential 12C-/13C-isotope dansylation labeling and fast liquid chromatography/mass spectrometry for absolute and relative quantification of the metabolome. Anal Chem 81(10):3919–3932. Scholar
  45. 45.
    Chen D, Su X, Wang N, Li Y, Yin H, Li L, Li L (2017) Chemical isotope labeling LC-MS for monitoring disease progression and treatment in animal models: plasma metabolomics study of osteoarthritis rat model. Sci Rep 7:40543. Scholar
  46. 46.
    Wu Y, Streijger F, Wang Y, Lin G, Christie S, Mac-Thiong JM, Parent S, Bailey CS, Paquette S, Boyd MC, Ailon T, Street J, Fisher CG, Dvorak MF, Kwon BK, Li L (2016) Parallel metabolomic profiling of cerebrospinal fluid and serum for identifying biomarkers of injury severity after acute human spinal cord injury. Sci Rep 6:38718. Scholar
  47. 47.
    Zhao S, Luo X, Li L (2016) Chemical isotope labeling LC-MS for high coverage and quantitative profiling of the hydroxyl submetabolome in metabolomics. Anal Chem 88(21):10617–10623. Scholar
  48. 48.
    Su X, Wang N, Chen D, Li Y, Lu Y, Huan T, Xu W, Li L, Li L (2016) Dansylation isotope labeling liquid chromatography mass spectrometry for parallel profiling of human urinary and fecal submetabolomes. Anal Chim Acta 903:100–109. Scholar
  49. 49.
    Song P, Mabrouk OS, Hershey ND, Kennedy RT (2012) In vivo neurochemical monitoring using benzoyl chloride derivatization and liquid chromatography-mass spectrometry. Anal Chem 84(1):412–419. Scholar
  50. 50.
    Wong JM, Malec PA, Mabrouk OS, Ro J, Dus M, Kennedy RT (2016) Benzoyl chloride derivatization with liquid chromatography-mass spectrometry for targeted metabolomics of neurochemicals in biological samples. J Chromatogr A 1446:78–90. Scholar
  51. 51.
    Issaq HJ, Van QN, Waybright TJ, Muschik GM, Veenstra TD (2009) Analytical and statistical approaches to metabolomics research. J Sep Sci 32(13):2183–2199CrossRefGoogle Scholar
  52. 52.
    Beckonert O, Keun HC, Ebbels TM, Bundy J, Holmes E, Lindon JC, Nicholson JK (2007) Metabolic profiling, metabolomic and metabonomic procedures for NMR spectroscopy of urine, plasma, serum and tissue extracts. Nat Protoc 2(11):2692–2703. Scholar
  53. 53.
    Sitter B, Bathen TF, Tessem M-B, Gribbestad IS (2009) High-resolution magic angle spinning (HR MAS) MR spectroscopy in metabolic characterization of human cancer. Prog Nucl Magn Reson Spectrosc 54(3–4):239–254. Scholar
  54. 54.
    Beltran A, Suarez M, Rodriguez MA, Vinaixa M, Samino S, Arola L, Correig X, Yanes O (2012) Assessment of compatibility between extraction methods for NMR- and LC/MS-based metabolomics. Anal Chem 84(14):5838–5844. Scholar
  55. 55.
    Li N, Song Y, Tang H, Wang Y (2016) Recent developments in sample preparation and data pre-treatment in metabonomics research. Arch Biochem Biophys 589:4–9. Scholar
  56. 56.
    Le Gall G (2015) Sample collection and preparation of biofluids and extracts for NMR spectroscopy. Methods Mol Biol 1277:15–28. Scholar
  57. 57.
    Pasikanti KK, Ho PC, Chan EC (2008) Gas chromatography/mass spectrometry in metabolic profiling of biological fluids. J Chromatogr B Analyt Technol Biomed Life Sci 871(2):202–211. Scholar
  58. 58.
    Halket JM, Waterman D, Przyborowska AM, Patel RK, Fraser PD, Bramley PM (2005) Chemical derivatization and mass spectral libraries in metabolic profiling by GC/MS and LC/MS/MS. J Exp Bot 56(410):219–243. Scholar
  59. 59.
    Lisec J, Schauer N, Kopka J, Willmitzer L, Fernie AR (2006) Gas chromatography mass spectrometry-based metabolite profiling in plants. Nat Protoc 1(1):387–396. Scholar
  60. 60.
    Welthagen W, Shellie RA, Spranger J, Ristow M, Zimmermann R, Fiehn O (2005) Comprehensive two-dimensional gas chromatography–time-of-flight mass spectrometry (GC × GC-TOF) for high resolution metabolomics: biomarker discovery on spleen tissue extracts of obese NZO compared to lean C57BL/6 mice. Metabolomics 1(1):65–73. Scholar
  61. 61.
    Adahchour M, Beens J, Brinkman UA (2008) Recent developments in the application of comprehensive two-dimensional gas chromatography. J Chromatogr A 1186(1–2):67–108. Scholar
  62. 62.
    Weinert CH, Egert B, Kulling SE (2015) On the applicability of comprehensive two-dimensional gas chromatography combined with a fast-scanning quadrupole mass spectrometer for untargeted large-scale metabolomics. J Chromatogr A 1405:156–167. Scholar
  63. 63.
    Almstetter MF, Oefner PJ, Dettmer K (2012) Comprehensive two-dimensional gas chromatography in metabolomics. Anal Bioanal Chem 402(6):1993–2013. Scholar
  64. 64.
    Khamis MM, Adamko DJ, El-Aneed A (2017) Mass spectrometric based approaches in urine metabolomics and biomarker discovery. Mass Spectrom Rev 36(2):115–134. Scholar
  65. 65.
    Theodoridis GA, Gika HG, Want EJ, Wilson ID (2012) Liquid chromatography-mass spectrometry based global metabolite profiling: a review. Anal Chim Acta 711:7–16. Scholar
  66. 66.
    Wilson ID, Nicholson JK, Castro-Perez J, Granger JH, Johnson KA, Smith BW, Plumb RS (2005) High resolution “ultra performance” liquid chromatography coupled to oa-TOF mass spectrometry as a tool for differential metabolic pathway profiling in functional genomic studies. J Proteome Res 4(2):591–598. Scholar
  67. 67.
    Tang DQ, Zou L, Yin XX, Ong CN (2016) HILIC-MS for metabolomics: an attractive and complementary approach to RPLC-MS. Mass Spectrom Rev 35(5):574–600. Scholar
  68. 68.
    Chen J, Wang W, Lv S, Yin P, Zhao X, Lu X, Zhang F, Xu G (2009) Metabonomics study of liver cancer based on ultra performance liquid chromatography coupled to mass spectrometry with HILIC and RPLC separations. Anal Chim Acta 650(1):3–9. Scholar
  69. 69.
    Dunn WB, Ellis DI (2005) Metabolomics: current analytical platforms and methodologies. TrAC Trends Anal Chem 24(4):285–294. Scholar
  70. 70.
    Bothwell JHF, Griffin JL (2011) An introduction to biological nuclear magnetic resonance spectroscopy. Biol Rev 86(2):493–510. Scholar
  71. 71.
    Grimes JH, O’Connell TM (2011) The application of micro-coil NMR probe technology to metabolomics of urine and serum. J Biomol NMR 49(3–4):297–305. Scholar
  72. 72.
    Le Guennec A, Tayyari F, Edison AS (2017) Alternatives to nuclear overhauser enhancement spectroscopy presat and Carr-Purcell-Meiboom-Gill presat for NMR-based metabolomics. Anal Chem.
  73. 73.
    Chaudhry V, Bhatia A, Bharti SK, Mishra SK, Chauhan PS, Mishra A, Sidhu OP, Nautiyal CS (2015) Metabolite profiling reveals abiotic stress tolerance in Tn5 mutant of Pseudomonas putida. PLoS One 10(1):e0113487. Scholar
  74. 74.
    Wan Q, Wang Y, Tang H (2017) Quantitative 13C traces of glucose fate in hepatitis B virus-infected hepatocytes. Anal Chem 89(6):3293–3299. Scholar
  75. 75.
    Hollinshead KE, Williams DS, Tennant DA, Ludwig C (2016) Probing cancer cell metabolism using NMR spectroscopy. Adv Exp Med Biol 899:89–111. Scholar
  76. 76.
    Lommen A (2009) MetAlign: interface-driven, versatile metabolomics tool for hyphenated full-scan mass spectrometry data preprocessing. Anal Chem 81(8):3079–3086. Scholar
  77. 77.
    Wehrens R, Weingart G, Mattivi F (2014) metaMS: an open-source pipeline for GC-MS-based untargeted metabolomics. J Chromatogr B Analyt Technol Biomed Life Sci 966:109–116. Scholar
  78. 78.
    Cuadros-Inostroza A, Caldana C, Redestig H, Kusano M, Lisec J, Pena-Cortes H, Willmitzer L, Hannah MA (2009) TargetSearch—a Bioconductor package for the efficient preprocessing of GC-MS metabolite profiling data. BMC Bioinformatics 10:428. Scholar
  79. 79.
    Carroll AJ, Badger MR, Harvey Millar A (2010) The MetabolomeExpress Project: enabling web-based processing, analysis and transparent dissemination of GC/MS metabolomics datasets. BMC Bioinformatics 11:376. Scholar
  80. 80.
    Luedemann A, von Malotky L, Erban A, Kopka J (2012) TagFinder: preprocessing software for the fingerprinting and the profiling of gas chromatography-mass spectrometry based metabolome analyses. Methods Mol Biol 860:255–286. Scholar
  81. 81.
    Smith CA, Want EJ, O’Maille G, Abagyan R, Siuzdak G (2006) XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal Chem 78(3):779–787. Scholar
  82. 82.
    Katajamaa M, Miettinen J, Oresic M (2006) MZmine: toolbox for processing and visualization of mass spectrometry based molecular profile data. Bioinformatics 22(5):634–636. Scholar
  83. 83.
    Pluskal T, Castillo S, Villar-Briones A, Oresic M (2010) MZmine 2: modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data. BMC Bioinformatics 11:395. Scholar
  84. 84.
    Luedemann A, Strassburg K, Erban A, Kopka J (2008) TagFinder for the quantitative analysis of gas chromatography—mass spectrometry (GC-MS)-based metabolite profiling experiments. Bioinformatics 24(5):732–737. Scholar
  85. 85.
    Tautenhahn R, Patti GJ, Rinehart D, Siuzdak G (2012) XCMS Online: a web-based platform to process untargeted metabolomic data. Anal Chem 84(11):5035–5039. Scholar
  86. 86.
    Libiseller G, Dvorzak M, Kleb U, Gander E, Eisenberg T, Madeo F, Neumann S, Trausinger G, Sinner F, Pieber T, Magnes C (2015) IPO: a tool for automated optimization of XCMS parameters. BMC Bioinformatics 16:118. Scholar
  87. 87.
    Tsugawa H, Cajka T, Kind T, Ma Y, Higgins B, Ikeda K, Kanazawa M, Van der Gheynst J, Fiehn O, Arita M (2015) MS-DIAL: data-independent MS/MS deconvolution for comprehensive metabolome analysis. Nat Methods 12(6):523–526. Scholar
  88. 88.
    Chen G, Walmsley S, Cheung GCM, Chen L, Cheng CY, Beuerman RW, Wong TY, Zhou L, Choi H (2017) Customized consensus spectral library building for untargeted quantitative metabolomics analysis with data independent acquisition mass spectrometry and MetaboDIA workflow. Anal Chem 89(9):4897–4906. Scholar
  89. 89.
    Li H, Cai Y, Guo Y, Chen F, Zhu ZJ (2016) MetDIA: targeted metabolite extraction of multiplexed MS/MS spectra generated by data-independent acquisition. Anal Chem 88(17):8757–8764. Scholar
  90. 90.
    Jansen BC, Reiding KR, Bondt A, Ederveen ALH, Palmblad M, Falck D, Wuhrer M (2015) MassyTools: a high-throughput targeted data processing tool for relative quantitation and quality control developed for glycomic and glycoproteomic MALDI-MS. J Proteome Res 14(12):5088–5098CrossRefGoogle Scholar
  91. 91.
    Savorani F, Tomasi G, Engelsen SB (2010) icoshift: a versatile tool for the rapid alignment of 1D NMR spectra. J Magn Reson 202(2):190–202. Scholar
  92. 92.
    Christensen JH, Tomasi G, Hansen AB (2005) Chemical fingerprinting of petroleum biomarkers using time warping and PCA. Environ Sci Technol 39(1):255–260CrossRefGoogle Scholar
  93. 93.
    Vu TN, Valkenborg D, Smets K, Verwaest KA, Dommisse R, Lemiere F, Verschoren A, Goethals B, Laukens K (2011) An integrated workflow for robust alignment and simplified quantitative analysis of NMR spectrometry data. BMC Bioinformatics 12:405. Scholar
  94. 94.
    De Meyer T, Sinnaeve D, Van Gasse B, Tsiporkova E, Rietzschel ER, De Buyzere ML, Gillebert TC, Bekaert S, Martins JC, Van Criekinge W (2008) NMR-based characterization of metabolic alterations in hypertension using an adaptive, intelligent binning algorithm. Anal Chem 80(10):3783–3790. Scholar
  95. 95.
    Jacob D, Deborde C, Moing A (2013) An efficient spectra processing method for metabolite identification from 1H-NMR metabolomics data. Anal Bioanal Chem 405(15):5049–5061. Scholar
  96. 96.
    Worley B, Powers R (2015) Generalized adaptive intelligent binning of multiway data. Chemom Intell Lab Syst 146:42–46. Scholar
  97. 97.
    Wishart DS, Jewison T, Guo AC, Wilson M, Knox C, Liu Y, Djoumbou Y, Mandal R, Aziat F, Dong E, Bouatra S, Sinelnikov I, Arndt D, Xia J, Liu P, Yallou F, Bjorndahl T, Perez-Pineiro R, Eisner R, Allen F, Neveu V, Greiner R, Scalbert A (2013) HMDB 3.0—the human metabolome database in 2013. Nucleic Acids Res 41(Database issue):D801–D807. Scholar
  98. 98.
    Wishart DS, Tzur D, Knox C, Eisner R, Guo AC, Young N, Cheng D, Jewell K, Arndt D, Sawhney S, Fung C, Nikolai L, Lewis M, Coutouly MA, Forsythe I, Tang P, Shrivastava S, Jeroncic K, Stothard P, Amegbey G, Block D, Hau DD, Wagner J, Miniaci J, Clements M, Gebremedhin M, Guo N, Zhang Y, Duggan GE, Macinnis GD, Weljie AM, Dowlatabadi R, Bamforth F, Clive D, Greiner R, Li L, Marrie T, Sykes BD, Vogel HJ, Querengesser L (2007) HMDB: the human metabolome database. Nucleic Acids Res 35(Database issue):D521–D526. Scholar
  99. 99.
    Markley JL, Anderson ME, Cui Q, Eghbalnia HR, Lewis IA, Hegeman AD, Li J, Schulte CF, Sussman MR, Westler WM, Ulrich EL, Zolnai Z (2007) New bioinformatics resources for metabolomics. Pacific Symposium on Biocomputing. Pac Symp Biocomput 12:157–168Google Scholar
  100. 100.
    Cui Q, Lewis IA, Hegeman AD, Anderson ME, Li J, Schulte CF, Westler WM, Eghbalnia HR, Sussman MR, Markley JL (2008) Metabolite identification via the Madison Metabolomics Consortium Database. Nat Biotechnol 26(2):162–164. Scholar
  101. 101.
    Kale NS, Haug K, Conesa P, Jayseelan K, Moreno P, Rocca-Serra P, Nainala VC, Spicer RA, Williams M, Li X, Salek RM, Griffin JL, Steinbeck C (2016) MetaboLights: an open-access database repository for metabolomics data. Curr Protoc Bioinformatics 53:14.13.11–14.13.18. Scholar
  102. 102.
    Ellinger JJ, Chylla RA, Ulrich EL, Markley JL (2013) Databases and software for NMR-based metabolomics. Curr Metabolomics 1(1).
  103. 103.
    Bingol K, Zhang F, Bruschweiler-Li L, Bruschweiler R (2012) TOCCATA: a customized carbon total correlation spectroscopy NMR metabolomics database. Anal Chem 84(21):9395–9401. Scholar
  104. 104.
    Kwan EE, Huang SG (2008) Structural elucidation with NMR spectroscopy: practical strategies for organic chemists. Eur J Org Chem 2008(16):2671–2688. Scholar
  105. 105.
    Bingol K, Bruschweiler R (2011) Deconvolution of chemical mixtures with high complexity by NMR consensus trace clustering. Anal Chem 83(19):7412–7417. Scholar
  106. 106.
    Leek JT, Scharpf RB, Bravo HC, Simcha D, Langmead B, Johnson WE, Geman D, Baggerly K, Irizarry RA (2010) Tackling the widespread and critical impact of batch effects in high-throughput data. Nat Rev Genet 11(10):733–739. Scholar
  107. 107.
    Burton L, Ivosev G, Tate S, Impey G, Wingate J, Bonner R (2008) Instrumental and experimental effects in LC-MS-based metabolomics. J Chromatogr B Analyt Technol Biomed Life Sci 871(2):227–235. Scholar
  108. 108.
    De Livera AM, Sysi-Aho M, Jacob L, Gagnon-Bartsch JA, Castillo S, Simpson JA, Speed TP (2015) Statistical methods for handling unwanted variation in metabolomics data. Anal Chem 87(7):3606–3615. Scholar
  109. 109.
    Hendriks MMWB, van FA E, Jellema RH, Westerhuis JA, Reijmers TH, Hoefsloot HCJ, Smilde AK (2011) Data-processing strategies for metabolomics studies. TrAC Trends Anal Chem 30(10):1685–1698. Scholar
  110. 110.
    Wehrens R, Hageman JA, van Eeuwijk F, Kooke R, Flood PJ, Wijnker E, Keurentjes JJ, Lommen A, van Eekelen HD, Hall RD, Mumm R, de Vos RC (2016) Improved batch correction in untargeted MS-based metabolomics. Metabolomics 12:88. Scholar
  111. 111.
    Brunius C, Shi L, Landberg R (2016) Large-scale untargeted LC-MS metabolomics data correction using between-batch feature alignment and cluster-based within-batch signal intensity drift correction. Metabolomics 12(11):173. Scholar
  112. 112.
    Shen X, Gong X, Cai Y, Guo Y, Tu J, Li H, Zhang T, Wang J, Xue F, Zhu Z-J (2016) Normalization and integration of large-scale metabolomics data using support vector regression. Metabolomics 12(5):89. Scholar
  113. 113.
    Li B, Tang J, Yang Q, Li S, Cui X, Li Y, Chen Y, Xue W, Li X, Zhu F (2017) NOREVA: normalization and evaluation of MS-based metabolomics data. Nucleic Acids Res. Scholar
  114. 114.
    Hochrein J, Zacharias HU, Taruttis F, Samol C, Engelmann JC, Spang R, Oefner PJ, Gronwald W (2015) Data normalization of 1H NMR metabolite fingerprinting data sets in the presence of unbalanced metabolite regulation. J Proteome Res 14(8):3217–3228. Scholar
  115. 115.
    Chen J, Zhang P, Lv M, Guo H, Huang Y, Zhang Z, Xu F (2017) Influences of normalization method on biomarker discovery in gas chromatography-mass spectrometry-based untargeted metabolomics: what should be considered? Anal Chem 89(10):5342–5348. Scholar
  116. 116.
    Li B, Tang J, Yang Q, Cui X, Li S, Chen S, Cao Q, Xue W, Chen N, Zhu F (2016) Performance evaluation and online realization of data-driven normalization methods used in LC/MS based untargeted metabolomics analysis. Sci Rep 6:38881. Scholar
  117. 117.
    Putri SP, Yamamoto S, Tsugawa H, Fukusaki E (2013) Current metabolomics: technological advances. J Biosci Bioeng 116(1):9–16. Scholar
  118. 118.
    Boccard J, Veuthey JL, Rudaz S (2010) Knowledge discovery in metabolomics: an overview of MS data handling. J Sep Sci 33(3):290–304CrossRefGoogle Scholar
  119. 119.
    Tagore S, Chowdhury N, De RK (2014) Analyzing methods for path mining with applications in metabolomics. Gene 534(2):125–138CrossRefGoogle Scholar
  120. 120.
    Chagoyen M, Pazos F (2013) Tools for the functional interpretation of metabolomic experiments. Brief Bioinform 14(6):737–744CrossRefGoogle Scholar
  121. 121.
    Johnson CH, Ivanisevic J, Siuzdak G (2016) Metabolomics: beyond biomarkers and towards mechanisms. Nat Rev Mol Cell Biol 17(7):451–459CrossRefGoogle Scholar
  122. 122.
    Xia JG, Wishart DS (2010) MSEA: a web-based tool to identify biologically meaningful patterns in quantitative metabolomic data. Nucleic Acids Res 38:W71–W77CrossRefGoogle Scholar
  123. 123.
    Chagoyen M, Pazos F (2011) MBRole: enrichment analysis of metabolomic data. Bioinformatics 27(5):730–731CrossRefGoogle Scholar
  124. 124.
    Kankainen M, Gopalacharyulu P, Holm L, Oresic M (2011) MPEA-metabolite pathway enrichment analysis. Bioinformatics 27(13):1878–1879CrossRefGoogle Scholar
  125. 125.
    Kamburov A, Cavill R, Ebbels TMD, Herwig R, Keun HC (2011) Integrated pathway-level analysis of transcriptomics and metabolomics data with IMPaLA. Bioinformatics 27(20):2917–2918CrossRefGoogle Scholar
  126. 126.
    Okuda S, Yamada T, Hamajima M, Itoh M, Katayama T, Bork P, Goto S, Kanehisa M (2008) KEGG Atlas mapping for global analysis of metabolic pathways. Nucleic Acids Res 36:W423–W426CrossRefGoogle Scholar
  127. 127.
    Paley SM, Karp PD (2006) The Pathway Tools cellular overview diagram and Omics Viewer. Nucleic Acids Res 34(13):3771–3778CrossRefGoogle Scholar
  128. 128.
    Letunic I, Yamada T, Kanehisa M, Bork P (2008) iPath: interactive exploration of biochemical pathways and networks. Trends Biochem Sci 33(3):101–103CrossRefGoogle Scholar
  129. 129.
    Tokimatsu T, Sakurai N, Suzuki H, Ohta H, Nishitani K, Koyama T, Umezawa T, Misawa N, Saito K, Shibata D (2005) KaPPA-View. A web-based analysis tool for integration of transcript and metabolite data on plant metabolic pathway maps. Plant Physiol 138(3):1289–1300. Scholar
  130. 130.
    Thimm O, Blasing O, Gibon Y, Nagel A, Meyer S, Kruger P, Selbig J, Muller LA, Rhee SY, Stitt M (2004) MAPMAN: a user-driven tool to display genomics data sets onto diagrams of metabolic pathways and other biological processes. Plant J 37(6):914–939. Scholar
  131. 131.
    Xia JG, Wishart DS (2010) MetPA: a web-based metabolomics tool for pathway analysis and visualization. Bioinformatics 26(18):2342–2344. Scholar
  132. 132.
    Gao J, Tarcea VG, Karnovsky A, Mirel BR, Weymouth TE, Beecher CW, Cavalcoli JD, Athey BD, Omenn GS, Burant CF, Jagadish HV (2010) Metscape: a Cytoscape plug-in for visualizing and interpreting metabolomic data in the context of human metabolic networks. Bioinformatics 26(7):971–973CrossRefGoogle Scholar
  133. 133.
    Symons S, Nieselt K (2011) MGV: a generic graph viewer for comparative omics data. Bioinformatics 27(16):2248–2255. Scholar
  134. 134.
    Garcia-Alcalde F, Garcia-Lopez F, Dopazo J, Conesa A (2011) Paintomics: a web based tool for the joint visualization of transcriptomics and metabolomics data. Bioinformatics 27(1):137–139. Scholar
  135. 135.
    Leader DP, Burgess K, Creek D, Barrett MP (2011) Pathos: a web facility that uses metabolic maps to display experimental changes in metabolites identified by mass spectrometry. Rapid Commun Mass Spectrom 25(22):3422–3426. Scholar
  136. 136.
    van Iersel MP, Kelder T, Pico AR, Hanspers K, Coort S, Conklin BR, Evelo C (2008) Presenting and exploring biological pathways with PathVisio. BMC Bioinformatics 9:399. Scholar
  137. 137.
    Neuweger H, Persicke M, Albaum SP, Bekel T, Dondrup M, Huser AT, Winnebald J, Schneider J, Kalinowski J, Goesmann A (2009) Visualizing post genomics data-sets on customized pathway maps by ProMeTra-aeration-dependent gene expression and metabolism of Corynebacterium glutamicum as an example. BMC Syst Biol 3:82. Scholar
  138. 138.
    Joshi-Tope G, Gillespie M, Vastrik I, D’Eustachio P, Schmidt E, de Bono B, Jassal B, Gopinath GR, Wu GR, Matthews L, Lewis S, Birney E, Stein L (2005) Reactome: a knowledgebase of biological pathways. Nucleic Acids Res 33:D428–D432CrossRefGoogle Scholar
  139. 139.
    Junker BH, Klukas C, Schreiber F (2006) VANTED: a system for advanced data analysis and visualization in the context of biological networks. BMC Bioinformatics 7:109CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Medical InstrumentShanghai University of Medicine and Health SciencesShanghaiChina
  2. 2.State Key Laboratory of Bio-Organic and Natural Product Chemistry, Shanghai Institute of Organic ChemistryChinese Academy of SciencesShanghaiChina
  3. 3.Bio-Med Big Data Center, Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological SciencesChinese Academy of SciencesShanghaiChina
  4. 4.Key Laboratory of Systems Biology, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological SciencesChinese Academy of SciencesShanghaiChina

Personalised recommendations