Skip to main content
SpringerLink
Log in

Metabolomics

  • Chapter
  • First Online:
Essentials of Bioinformatics, Volume I

  • 3070 Accesses

Abstract

Metabolomics is a robust and comprehensive investigation of metabolites, generated from the substrates and products of metabolism, within cells, tissues, organisms, and biological fluids. Metabolomics helps to get a panoramic view of an array of metabolites that are implicated in diverse and intricate cellular, molecular, and physiological processes in living systems. More importance has been ascribed to the metabolomics-related research in academia, industry, and government bodies worldwide. It is clearly evident by just two publications in the year 2000 to nearly 27,000 documents till the year 2018 in metabolomics research. Investigating the metabolome is necessary for understanding any subtle changes in metabolites and the subsequent impact on molecular networks or pathways in health and disease states. The rapid emergence of “systems biology” helps to integrate the massive wealth of data derived from these “multi-omics” platforms with metabolomics approach to further interpret or characterize the complex biological processes since the metabolomics is an integral and pivotal part of the central dogma (CD) of molecular biology and characterizes the molecular phenotype. In this chapter, I will discuss the analytical methods in metabolomics such as mass spectrometry (MS) and nuclear magnetic resonance spectroscopy (NMR), coupled or hyphenated techniques with liquid chromatography (LC), gas chromatography (GC) and capillary electrophoresis (LC-MS, GC-MS, CE-NMR, MS-NMR, etc.), experimental pipeline, univariate and multivariate analysis, data visualization strategies, metabolomics databases, pathway analysis servers, and potential applications in health and disease.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Abbreviations

CD:

Central Dogma

CE:

Capillary Electrophoresis

CP:

Chronic Pancreatitis

EI:

Electron Impact Ionization

ESI:

Electrospray Ionization

GC:

Gas Chromatography

KEGG:

Kyoto Encyclopedia of Genes and Genomes

LC:

Liquid Chromatography

MALDI:

Matrix-Assisted Laser Desorption and Ionization

MS:

Mass Spectrometry

NMR:

Nuclear Magnetic Resonance Spectroscopy

OPLS-DA:

Orthogonal Partial Least Squares Discriminant Analysis

PCA:

Principal Component Analysis

PDAC:

Pancreatic Ductal Adenocarcinoma

QTOF:

Quadrupole Time of Flight

TW-IMS:

Traveling Wave Ion Mobility Spectrometry

References

  • Abdel-Rehim M (2011) Microextraction by packed sorbent (MEPS): a tutorial. Anal Chim Acta 701(2):119–128

    Article  CAS  PubMed  Google Scholar 

  • Aoki KF, Kanehisa M (2005) Using the KEGG database resource. Curr Protoc Bioinformatics Chapter 1:Unit 1 12

    Google Scholar 

  • Bartlett MG, Chen B (2016) Editor-in-chief editorial and introduction to ‘Metabolomics and biomarkers’ special issue. Biomed Chromatogr 30(1):5–6

    Article  CAS  PubMed  Google Scholar 

  • Bingol K, Bruschweiler R (2015a) Two elephants in the room: new hybrid nuclear magnetic resonance and mass spectrometry approaches for metabolomics. Curr Opin Clin Nutr Metab Care 18(5):471–477

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Bingol K, Bruschweiler R (2015b) NMR/MS translator for the enhanced simultaneous analysis of metabolomics mixtures by NMR spectroscopy and mass spectrometry: application to human urine. J Proteome Res 14(6):2642–2648

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Bingol K, Bruschweiler-Li L, Yu C, Somogyi A, Zhang F, Bruschweiler R (2015a) Metabolomics beyond spectroscopic databases: a combined MS/NMR strategy for the rapid identification of new metabolites in complex mixtures. Anal Chem 87(7):3864–3870

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Bingol K, Li DW, Bruschweiler-Li L, Cabrera OA, Megraw T, Zhang F et al (2015b) Unified and isomer-specific NMR metabolomics database for the accurate analysis of (13)C-(1)H HSQC spectra. ACS Chem Biol 10(2):452–459

    Article  CAS  PubMed  Google Scholar 

  • Bohler A, Wu G, Kutmon M, Pradhana LA, Coort SL, Hanspers K et al (2016) Reactome from a WikiPathways perspective. PLoS Comput Biol 12(5):e1004941

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Caspi R, Karp PD (2007) Using the MetaCyc pathway database and the BioCyc database collection. Curr Protoc Bioinformatics Chapter 1:Unit1 17

    Google Scholar 

  • Caspi R, Foerster H, Fulcher CA, Hopkinson R, Ingraham J, Kaipa P et al (2006) MetaCyc: a multiorganism database of metabolic pathways and enzymes. Nucleic Acids Res 34(Database issue):D511–D516

    Article  CAS  PubMed  Google Scholar 

  • Caspi R, Foerster H, Fulcher CA, Kaipa P, Krummenacker M, Latendresse M et al (2008) The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res 36(Database issue):D623–D631

    CAS  PubMed  Google Scholar 

  • Caspi R, Altman T, Dreher K, Fulcher CA, Subhraveti P, Keseler IM et al (2012) The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res 40(Database issue):D742–D753

    Article  CAS  PubMed  Google Scholar 

  • Caspi R, Altman T, Billington R, Dreher K, Foerster H, Fulcher CA et al (2014) The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res 42(Database issue):D459–D471

    Article  CAS  PubMed  Google Scholar 

  • Caspi R, Billington R, Ferrer L, Foerster H, Fulcher CA, Keseler IM et al (2016) The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res 44(D1):D471–D480

    Article  CAS  PubMed  Google Scholar 

  • Chaleckis R, Murakami I, Takada J, Kondoh H, Yanagida M (2016) Individual variability in human blood metabolites identifies age-related differences. Proc Natl Acad Sci U S A 113(16):4252–4259

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Chong J, Soufan O, Li C, Caraus I, Li S, Bourque G et al (2018) MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis. Nucleic Acids Res 46(W1):W486–WW94

    Article  PubMed  PubMed Central  Google Scholar 

  • Cook KD (2002) ASMS members John Fenn and Koichi Tanaka share Nobel: the world learns our “secret”. American Society for Mass Spectrometry. J Am Soc Mass Spectrom 13(12):1359

    Article  CAS  PubMed  Google Scholar 

  • Croft D, Mundo AF, Haw R, Milacic M, Weiser J, Wu G et al (2014) The Reactome pathway knowledgebase. Nucleic Acids Res 42(Database issue):D472–D477

    Article  CAS  PubMed  Google Scholar 

  • Cui Q, Lewis IA, Hegeman AD, Anderson ME, Li J, Schulte CF et al (2008) Metabolite identification via the Madison Metabolomics Consortium Database. Nat Biotechnol 26(2):162–164

    Article  CAS  PubMed  Google Scholar 

  • Davis VW, Schiller DE, Eurich D, Bathe OF, Sawyer MB (2013) Pancreatic ductal adenocarcinoma is associated with a distinct urinary metabolomic signature. Ann Surg Oncol 20(Suppl 3):S415–S423

    Article  PubMed  Google Scholar 

  • Drouin N, Rudaz S, Schappler J (2017) Sample preparation for polar metabolites in bioanalysis. Analyst 143(1):16–20

    Article  PubMed  Google Scholar 

  • Ellinger JJ, Chylla RA, Ulrich EL, Markley JL (2013) Databases and software for NMR-based metabolomics. Curr Metabolomics (1):1, 28–40

    Google Scholar 

  • Fabregat A, Sidiropoulos K, Garapati P, Gillespie M, Hausmann K, Haw R et al (2016) The Reactome pathway knowledgebase. Nucleic Acids Res 44(D1):D481–D487

    Article  CAS  PubMed  Google Scholar 

  • Fabregat A, Jupe S, Matthews L, Sidiropoulos K, Gillespie M, Garapati P et al (2018) The Reactome pathway knowledgebase. Nucleic Acids Res 46(D1):D649–DD55

    Article  CAS  PubMed  Google Scholar 

  • Fan TW, Lane AN, Higashi RM, Farag MA, Gao H, Bousamra M et al (2009) Altered regulation of metabolic pathways in human lung cancer discerned by (13)C stable isotope-resolved metabolomics (SIRM). Mol Cancer 8:41

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Forsberg EM, Huan T, Rinehart D, Benton HP, Warth B, Hilmers B et al (2018) Data processing, multi-omic pathway mapping, and metabolite activity analysis using XCMS online. Nat Protoc 13(4):633–651

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Gil de la Fuente A, Grace Armitage E, Otero A, Barbas C, Godzien J (2017) Differentiating signals to make biological sense – a guide through databases for MS-based non-targeted metabolomics. Electrophoresis 38(18):2242–2256

    Article  CAS  PubMed  Google Scholar 

  • Gowda H, Ivanisevic J, Johnson CH, Kurczy ME, Benton HP, Rinehart D et al (2014) Interactive XCMS online: simplifying advanced metabolomic data processing and subsequent statistical analyses. Anal Chem 86(14):6931–6939

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Guijas C, Montenegro-Burke JR, Domingo-Almenara X, Palermo A, Warth B, Hermann G et al (2018) METLIN: a technology platform for identifying knowns and unknowns. Anal Chem 90(5):3156–3164

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Haug K, Salek RM, Conesa P, Hastings J, de Matos P, Rijnbeek M et al (2013) MetaboLights–an open-access general-purpose repository for metabolomics studies and associated meta-data. Nucleic Acids Res 41(Database issue):D781–D786

    Article  CAS  PubMed  Google Scholar 

  • Heinonen M, Rantanen A, Mielikainen T, Kokkonen J, Kiuru J, Ketola RA et al (2008) FiD: a software for ab initio structural identification of product ions from tandem mass spectrometric data. Rapid Commun Mass Spectrom 22(19):3043–3052

    Article  CAS  PubMed  Google Scholar 

  • Huan T, Forsberg EM, Rinehart D, Johnson CH, Ivanisevic J, Benton HP et al (2017) Systems biology guided by XCMS online metabolomics. Nat Methods 14(5):461–462

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Jauhiainen A, Madhu B, Narita M, Narita M, Griffiths J, Tavare S (2014) Normalization of metabolomics data with applications to correlation maps. Bioinformatics 30(15):2155–2161

    Article  CAS  PubMed  Google Scholar 

  • Jeffryes JG, Colastani RL, Elbadawi-Sidhu M, Kind T, Niehaus TD, Broadbelt LJ et al (2015) MINEs: open access databases of computationally predicted enzyme promiscuity products for untargeted metabolomics. J Cheminform 7:44

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Johnson SR, Lange BM (2015) Open-access metabolomics databases for natural product research: present capabilities and future potential. Front Bioeng Biotechnol 3:22

    Article  PubMed  PubMed Central  Google Scholar 

  • Johnson CH, Ivanisevic J, Siuzdak G (2016) Metabolomics: beyond biomarkers and towards mechanisms. Nat Rev Mol Cell Biol 17(7):451–459

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Jones OA, Cheung VL (2007) An introduction to metabolomics and its potential application in veterinary science. Comp Med 57(5):436–442

    CAS  PubMed  Google Scholar 

  • Kale NS, Haug K, Conesa P, Jayseelan K, Moreno P, Rocca-Serra P et al (2016) MetaboLights: an open-access database repository for metabolomics data. Curr Protoc Bioinformatics 53:14 3 1–8

    PubMed  Google Scholar 

  • Kanehisa M (2013) Molecular network analysis of diseases and drugs in KEGG. Methods Mol Biol 939:263–275

    Article  CAS  PubMed  Google Scholar 

  • Kanehisa M (2016) KEGG bioinformatics resource for plant genomics and metabolomics. Methods Mol Biol 1374:55–70

    Article  CAS  PubMed  Google Scholar 

  • Kanehisa M, Goto S, Furumichi M, Tanabe M, Hirakawa M (2010) KEGG for representation and analysis of molecular networks involving diseases and drugs. Nucleic Acids Res 38(Database issue):D355–D360

    Article  CAS  PubMed  Google Scholar 

  • Kanehisa M, Goto S, Sato Y, Kawashima M, Furumichi M, Tanabe M (2014) Data, information, knowledge and principle: back to metabolism in KEGG. Nucleic Acids Res 42(Database issue):D199–D205

    Article  CAS  PubMed  Google Scholar 

  • Kanehisa M, Furumichi M, Tanabe M, Sato Y, Morishima K (2017) KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res 45(D1):D353–DD61

    Article  CAS  PubMed  Google Scholar 

  • Karp PD, Caspi R (2011) A survey of metabolic databases emphasizing the MetaCyc family. Arch Toxicol 85(9):1015–1033

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Karp PD, Riley M, Paley SM, Pellegrini-Toole A (2002) The MetaCyc database. Nucleic Acids Res 30(1):59–61

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Karp PD, Paley S, Altman T (2013) Data mining in the MetaCyc family of pathway databases. Methods Mol Biol 939:183–200

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Kelder T, Pico AR, Hanspers K, van Iersel MP, Evelo C, Conklin BR (2009) Mining biological pathways using WikiPathways web services. PLoS One 4(7):e6447

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Kelder T, van Iersel MP, Hanspers K, Kutmon M, Conklin BR, Evelo CT et al (2012) WikiPathways: building research communities on biological pathways. Nucleic Acids Res 40(Database issue):D1301–D1307

    Article  CAS  PubMed  Google Scholar 

  • Kessler N, Neuweger H, Bonte A, Langenkamper G, Niehaus K, Nattkemper TW et al (2013) MeltDB 2.0-advances of the metabolomics software system. Bioinformatics 29(19):2452–2459

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Kessler N, Bonte A, Albaum SP, Mader P, Messmer M, Goesmann A et al (2015) Learning to classify organic and conventional wheat – a machine learning driven approach using the MeltDB 2.0 metabolomics analysis platform. Front Bioeng Biotechnol 3:35

    Article  PubMed  PubMed Central  Google Scholar 

  • Kotera M, Hirakawa M, Tokimatsu T, Goto S, Kanehisa M (2012) The KEGG databases and tools facilitating omics analysis: latest developments involving human diseases and pharmaceuticals. Methods Mol Biol 802:19–39

    Article  CAS  PubMed  Google Scholar 

  • Krieger CJ, Zhang P, Mueller LA, Wang A, Paley S, Arnaud M et al (2004) MetaCyc: a multiorganism database of metabolic pathways and enzymes. Nucleic Acids Res 32(Database issue):D438–D442

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Krummenacker M, Paley S, Mueller L, Yan T, Karp PD (2005) Querying and computing with BioCyc databases. Bioinformatics 21(16):3454–3455

    Article  CAS  PubMed  Google Scholar 

  • Kutmon M, Riutta A, Nunes N, Hanspers K, Willighagen EL, Bohler A et al (2016) WikiPathways: capturing the full diversity of pathway knowledge. Nucleic Acids Res 44(D1):D488–D494

    Article  CAS  PubMed  Google Scholar 

  • Lane AN, Fan TW, Xie Z, Moseley HN, Higashi RM (2009a) Isotopomer analysis of lipid biosynthesis by high resolution mass spectrometry and NMR. Anal Chim Acta 651(2):201–208

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Lane AN, Fan TW, Higashi RM, Tan J, Bousamra M, Miller DM (2009b) Prospects for clinical cancer metabolomics using stable isotope tracers. Exp Mol Pathol 86(3):165–173

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Latendresse M, Paley S, Karp PD (2012) Browsing metabolic and regulatory networks with BioCyc. Methods Mol Biol 804:197–216

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Lin Y, Schiavo S, Orjala J, Vouros P, Kautz R (2008) Microscale LC-MS-NMR platform applied to the identification of active cyanobacterial metabolites. Anal Chem 80(21):8045–8054

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Lindahl A, Saaf S, Lehtio J, Nordstrom A (2017a) Tuning metabolome coverage in reversed phase LC-MS metabolomics of MeOH extracted samples using the reconstitution solvent composition. Anal Chem 89(14):7356–7364

    Article  CAS  PubMed  Google Scholar 

  • Lindahl A, Heuchel R, Forshed J, Lehtio J, Lohr M, Nordstrom A (2017b) Discrimination of pancreatic cancer and pancreatitis by LC-MS metabolomics. Metabolomics 13(5):61

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Ma X, Ouyang Z (2016) Ambient ionization and miniature mass spectrometry system for chemical and biological analysis. Trends Anal Chem 85(A):10–19

    Article  CAS  Google Scholar 

  • Madhu B, Jauhiainen A, McGuire S, Griffiths JR (2017) Exploration of human brain tumour metabolism using pairwise metabolite-metabolite correlation analysis (MMCA) of HR-MAS 1H NMR spectra. PLoS One 12(10):e0185980

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Mahieu NG, Genenbacher JL, Patti GJ (2016) A roadmap for the XCMS family of software solutions in metabolomics. Curr Opin Chem Biol 30:87–93

    Article  CAS  PubMed  Google Scholar 

  • Marshall DD, Powers R (2017) Beyond the paradigm: combining mass spectrometry and nuclear magnetic resonance for metabolomics. Prog Nucl Magn Reson Spectrosc 100:1–16

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Marshall DD, Lei S, Worley B, Huang Y, Garcia-Garcia A, Franco R et al (2015) Combining DI-ESI-MS and NMR datasets for metabolic profiling. Metabolomics 11(2):391–402

    Article  CAS  PubMed  Google Scholar 

  • Members MSIB, Sansone SA, Fan T, Goodacre R, Griffin JL, Hardy NW et al (2007) The metabolomics standards initiative. Nat Biotechnol 25(8):846–848

    Article  CAS  Google Scholar 

  • Menikarachchi LC, Cawley S, Hill DW, Hall LM, Hall L, Lai S et al (2012) MolFind: a software package enabling HPLC/MS-based identification of unknown chemical structures. Anal Chem 84(21):9388–9394

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Neuweger H, Albaum SP, Dondrup M, Persicke M, Watt T, Niehaus K et al (2008) MeltDB: a software platform for the analysis and integration of metabolomics experiment data. Bioinformatics 24(23):2726–2732

    Article  CAS  PubMed  Google Scholar 

  • Okuda S, Yamada T, Hamajima M, Itoh M, Katayama T, Bork P et al (2008) KEGG Atlas mapping for global analysis of metabolic pathways. Nucleic Acids Res 36(Web Server issue):W423–W426

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Palmer AG, Patel DJ (2002) Kurt Wuthrich and NMR of biological macromolecules. Structure 10(12):1603–1604

    Article  CAS  PubMed  Google Scholar 

  • Pico AR, Kelder T, van Iersel MP, Hanspers K, Conklin BR, Evelo C (2008) WikiPathways: pathway editing for the people. PLoS Biol 6(7):e184

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Robinette SL, Zhang F, Bruschweiler-Li L, Bruschweiler R (2008) Web server based complex mixture analysis by NMR. Anal Chem 80(10):3606–3611

    Article  CAS  PubMed  Google Scholar 

  • Romero P, Wagg J, Green ML, Kaiser D, Krummenacker M, Karp PD (2005) Computational prediction of human metabolic pathways from the complete human genome. Genome Biol 6(1):R2

    Article  PubMed  Google Scholar 

  • Salek RM, Haug K, Conesa P, Hastings J, Williams M, Mahendraker T et al (2013a) The MetaboLights repository: curation challenges in metabolomics. Database (Oxford) 2013:bat029

    Article  CAS  Google Scholar 

  • Salek RM, Haug K, Steinbeck C (2013b) Dissemination of metabolomics results: role of MetaboLights and COSMOS. Gigascience 2(1):8

    Article  PubMed  PubMed Central  Google Scholar 

  • Salek RM, Neumann S, Schober D, Hummel J, Billiau K, Kopka J et al (2015) COordination of Standards in MetabOlomicS (COSMOS): facilitating integrated metabolomics data access. Metabolomics 11(6):1587–1597

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Sana TR, Roark JC, Li X, Waddell K, Fischer SM (2008) Molecular formula and METLIN personal metabolite database matching applied to the identification of compounds generated by LC/TOF-MS. J Biomol Tech 19(4):258–266

    PubMed  PubMed Central  Google Scholar 

  • Sansone SA, Rocca-Serra P, Field D, Maguire E, Taylor C, Hofmann O et al (2012) Toward interoperable bioscience data. Nat Genet 44(2):121–126

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Schnackenberg LK (2006) Metabolomics special focus: an introduction. Pharmacogenomics 7(7):1053–1054

    Article  PubMed  Google Scholar 

  • Slenter DN, Kutmon M, Hanspers K, Riutta A, Windsor J, Nunes N et al (2018) WikiPathways: a multifaceted pathway database bridging metabolomics to other omics research. Nucleic Acids Res 46(D1):D661–D6D7

    Article  CAS  PubMed  Google Scholar 

  • Smelter A, Astra M, Moseley HN (2017) A fast and efficient python library for interfacing with the Biological Magnetic Resonance Data Bank. BMC Bioinformatics 18(1):175

    Article  PubMed  PubMed Central  Google Scholar 

  • Smith CA, O'Maille G, Want EJ, Qin C, Trauger SA, Brandon TR et al (2005) METLIN: a metabolite mass spectral database. Ther Drug Monit 27(6):747–751

    Article  CAS  PubMed  Google Scholar 

  • Steinbeck C, Conesa P, Haug K, Mahendraker T, Williams M, Maguire E et al (2012) MetaboLights: towards a new COSMOS of metabolomics data management. Metabolomics 8(5):757–760

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Tan SZ, Begley P, Mullard G, Hollywood KA, Bishop PN (2016) Introduction to metabolomics and its applications in ophthalmology. Eye (Lond) 30(6):773–783

    Article  CAS  Google Scholar 

  • Tanabe M, Kanehisa M (2012) Using the KEGG database resource. Curr Protoc Bioinformatics Chapter 1:Unit1 12

    Google Scholar 

  • Tautenhahn R, Cho K, Uritboonthai W, Zhu Z, Patti GJ, Siuzdak G (2012) An accelerated workflow for untargeted metabolomics using the METLIN database. Nat Biotechnol 30(9):826–828

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Tsugawa H (2018) Advances in computational metabolomics and databases deepen the understanding of metabolisms. Curr Opin Biotechnol 54:10–17

    Article  CAS  PubMed  Google Scholar 

  • Ulrich EL, Akutsu H, Doreleijers JF, Harano Y, Ioannidis YE, Lin J et al (2008) BioMagResBank. Nucleic Acids Res 36(Database issue):D402–D408

    CAS  PubMed  Google Scholar 

  • Waagmeester A, Kutmon M, Riutta A, Miller R, Willighagen EL, Evelo CT et al (2016) Using the semantic web for rapid integration of WikiPathways with other biological online data resources. PLoS Comput Biol 12(6):e1004989

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Walsh JR, Sen TZ, Dickerson JA (2014) A computational platform to maintain and migrate manual functional annotations for BioCyc databases. BMC Syst Biol 8:115

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Wishart DS, Watson MS, Boyko RF, Sykes BD (1997) Automated 1H and 13C chemical shift prediction using the BioMagResBank. J Biomol NMR 10(4):329–336

    Article  CAS  PubMed  Google Scholar 

  • Wishart DS, Tzur D, Knox C, Eisner R, Guo AC, Young N et al (2007) HMDB: the human metabolome database. Nucleic Acids Res 35(Database issue):D521–D526

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Wishart DS, Knox C, Guo AC, Eisner R, Young N, Gautam B et al (2009) HMDB: a knowledgebase for the human metabolome. Nucleic Acids Res 37(Database issue):D603–D610

    Article  CAS  PubMed  Google Scholar 

  • Wishart DS, Jewison T, Guo AC, Wilson M, Knox C, Liu Y et al (2013) HMDB 3.0–the human metabolome database in 2013. Nucleic Acids Res 41(Database issue):D801–D807

    CAS  PubMed  Google Scholar 

  • Wishart DS, Feunang YD, Marcu A, Guo AC, Liang K, Vazquez-Fresno R et al (2018) HMDB 4.0: the human metabolome database for 2018. Nucleic Acids Res 46(D1):D608–DD17

    Article  CAS  PubMed  Google Scholar 

  • Worley B, Powers R (2014) MVAPACK: a complete data handling package for NMR metabolomics. ACS Chem Biol 9(5):1138–1144

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Xia J, Wishart DS (2011a) Web-based inference of biological patterns, functions and pathways from metabolomic data using MetaboAnalyst. Nat Protoc 6(6):743–760

    Article  CAS  PubMed  Google Scholar 

  • Xia J, Wishart DS (2011b) Metabolomic data processing, analysis, and interpretation using MetaboAnalyst. Curr Protoc Bioinformatics Chapter 14:Unit 14 0

    Google Scholar 

  • Xia J, Wishart DS (2016) Using MetaboAnalyst 3.0 for comprehensive metabolomics data analysis. Curr Protoc Bioinformatics 55:14 0 1–0 91

    Article  PubMed  Google Scholar 

  • Xia J, Bjorndahl TC, Tang P, Wishart DS (2008) MetaboMiner–semi-automated identification of metabolites from 2D NMR spectra of complex biofluids. BMC Bioinformatics 9:507

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Xia J, Psychogios N, Young N, Wishart DS (2009) MetaboAnalyst: a web server for metabolomic data analysis and interpretation. Nucleic Acids Res 37(Web Server issue):W652–W660

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Xia J, Mandal R, Sinelnikov IV, Broadhurst D, Wishart DS (2012) MetaboAnalyst 2.0–a comprehensive server for metabolomic data analysis. Nucleic Acids Res 40(Web Server issue):W127–W133

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Xia J, Sinelnikov IV, Han B, Wishart DS (2015) MetaboAnalyst 3.0–making metabolomics more meaningful. Nucleic Acids Res 43(W1):W251–W257

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Zhang F, Bruschweiler-Li L, Robinette SL, Bruschweiler R (2008) Self-consistent metabolic mixture analysis by heteronuclear NMR. Application to a human cancer cell line. Anal Chem 80(19):7549–7553

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Zhang F, Robinette SL, Bruschweiler-Li L, Bruschweiler R (2009) Web server suite for complex mixture analysis by covariance NMR. Magn Reson Chem 47(Suppl 1):S118–S122

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Zhu ZJ, Schultz AW, Wang J, Johnson CH, Yannone SM, Patti GJ et al (2013) Liquid chromatography quadrupole time-of-flight mass spectrometry characterization of metabolites guided by the METLIN database. Nat Protoc 8(3):451–460

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgments

This work is funded by the National Plan for Science, Technology, and Innovation (MAARIFAH), King Abdulaziz City for Science and Technology, the Kingdom of Saudi Arabia, award number 12-BIO2267-03. The author also acknowledges with thanks the Science and Technology Unit (STU), King Abdulaziz University, for their excellent technical support.

Author information

Authors and Affiliations

  1. Center of Excellence in Genomic Medicine Research, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Saudi Arabia

    Peter Natesan Pushparaj

Authors
  1. Peter Natesan Pushparaj
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Genetic Medicine, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia

    Noor Ahmad Shaik

  2. Department of Biological Sciences, King Abdulaziz University, Jeddah, Saudi Arabia

    Khalid Rehman Hakeem

  3. Princess Al-Jawhara Center of Excellence in Research of Hereditary Disorders, Department of Genetic Medicine, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia

    Babajan Banaganapalli

  4. Princess Al-Jawhara Center of Excellence in Research of Hereditary Disorders, Department of Genetic Medicine, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia

    Ramu Elango

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Pushparaj, P.N. (2019). Metabolomics. In: Shaik, N., Hakeem, K., Banaganapalli, B., Elango, R. (eds) Essentials of Bioinformatics, Volume I. Springer, Cham. https://doi.org/10.1007/978-3-030-02634-9_13

Download citation

Publish with us

Policies and ethics

Search

Navigation