Skip to main content
Log in

Dolphin: a tool for automatic targeted metabolite profiling using 1D and 2D 1H-NMR data

  • Research Paper
  • Published:
Analytical and Bioanalytical Chemistry Aims and scope Submit manuscript


One of the main challenges in nuclear magnetic resonance (NMR) metabolomics is to obtain valuable metabolic information from large datasets of raw NMR spectra in a high throughput, automatic, and reproducible way. To date, established software packages used to match and quantify metabolites in NMR spectra remain mostly manually operated, leading to low resolution results and subject to inconsistencies not attributable to the NMR technique itself. Here, we introduce a new software package, called Dolphin, able to automatically quantify a set of target metabolites in multiple sample measurements using an approach based on 1D and 2D NMR techniques to overcome the inherent limitations of 1D 1H-NMR spectra in metabolomics. Dolphin takes advantage of the 2D J-resolved NMR spectroscopy signal dispersion to avoid inconsistencies in signal position detection, enhancing the reliability and confidence in metabolite matching. Furthermore, in order to improve accuracy in quantification, Dolphin uses 2D NMR spectra to obtain additional information on all neighboring signals surrounding the target metabolite. We have compared the targeted profiling results of Dolphin, recorded from standard biological mixtures, with those of two well established approaches in NMR metabolomics. Overall, Dolphin produced more accurate results with the added advantage of being a fully automated and high throughput processing package.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others


  1. Amix software has other quantification approaches, but in this work we only performed a comparison with its integration based quantification option.


  1. 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

    Article  CAS  Google Scholar 

  2. Chen HW, Pan ZZ, Talaty N, Raftery D, Cooks RG (2006) Combining desorption electrospray ionization mass spectrometry and nuclear magnetic resonance for differential metabolomics without sample preparation. Rapid Commun Mass Spectrom 20(10):1577–1584

    Article  CAS  Google Scholar 

  3. Beckonert O, Keun HC, Ebbels TMD, Bundy JG, 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

    Article  CAS  Google Scholar 

  4. Fiehn O (2002) Metabolomics—the link between genotypes and phenotypes. Plant Mol Biol 48(1/2):155–171

    Article  CAS  Google Scholar 

  5. 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

    Article  Google Scholar 

  6. Alves AC, Rantalainen M, Holmes E, Nicholson JK, Ebbels TMD (2009) Analytic properties of statistical total correlation spectroscopy based information recovery in H-1 NMR metabolic data sets. Anal Chem 81(6):2075–2084

    Article  Google Scholar 

  7. Anderson PE, Reo NV, DelRaso NJ, Doom TE, Raymer ML (2008) Gaussian binning: a new kernel-based method for processing NMR spectroscopic data for metabolomics. Metabolomics 4(3):261–272

    Article  CAS  Google Scholar 

  8. Jacob D, Deborde C, Moing A (2013) An efficient spectra processing method for metabolite identification from H-1-NMR metabolomics data. Anal Bioanal Chem 405(15):5049–5061

    Article  CAS  Google Scholar 

  9. Eads CD, Furnish CM, Noda I, Juhlin KD, Cooper DA, Morrall SW (2004) Molecular factor analysis applied to collections of NMR spectra. Anal Chem 76(7):1982–1990

    Article  CAS  Google Scholar 

  10. Ochs MF, Stoyanova RS, Arias-Mendoza F, Brown TR (1999) A new method for spectral decomposition using a bilinear Bayesian approach. J Magn Reson 137(1):161–176

    Article  CAS  Google Scholar 

  11. Stoyanova R, Nicholson JK, Lindon JC, Brown TR (2004) Sample classification based on Bayesian spectral decomposition of metabonomic NMR data sets. Anal Chem 76(13):3666–3674

    Article  CAS  Google Scholar 

  12. Hao J, Astle W, De Iorio M, Ebbels TMD (2012) BATMAN—an R package for the automated quantification of metabolites from nuclear magnetic resonance spectra using a Bayesian model. Bioinformatics 28(15):2088–2090

    Article  CAS  Google Scholar 

  13. Hao J, Liebeke M, Astle W, De Iorio M, Bundy JG, Ebbels TMD (2014) Bayesian deconvolution and quantification of metabolites in complex 1D NMR spectra using BATMAN. Nat Protoc 9(6):1416–1427

    Article  CAS  Google Scholar 

  14. Laatikainen R, Niemitz M, Malaisse WJ, Biesemans M, Willem R (1996) A computational strategy for the deconvolution of NMR spectra with multiplet structures and constraints: Analysis of overlapping C-13-H-2 multiplets of C-13 enriched metabolites from cell suspensions incubated in deuterated media. Magn Reson Med 36(3):359–365

    Article  CAS  Google Scholar 

  15. Soininen P, Haarala J, Vepsalainen J, Niemitz M, Laatikainen R (2005) Strategies for organic impurity quantification by H-1 NMR spectroscopy: constrained total-line-shape fitting. Anal Chim Acta 542(2):178–185

    Article  CAS  Google Scholar 

  16. Jukarainen NM, Korhonen SP, Laakso MP, Korolainen MA, Niemitz M, Soininen PP, Tuppurainen K, Vepsalainen J, Pirttila T, Laatikainen R (2008) Quantification of H-1 NMR spectra of human cerebrospinal fluid: a protocol based on constrained total-line-shape analysis. Metabolomics 4(2):150–160

    Article  CAS  Google Scholar 

  17. Mihaleva VV, Korhonen S-P, van Duynhoven J, Niemitz M, Vervoort J, Jacobs DM (2014) Automated quantum mechanical total line shape fitting model for quantitative NMR-based profiling of human serum metabolites. Anal Bioanal Chem 406(13):3091–3102

    Article  CAS  Google Scholar 

  18. Wishart DS, Querengesser LMM, Lefebvre BA, Epstein NA, Greiner R, Newton JB (2001) Magnetic resonance diagnostics: a new technology for high-throughput clinical diagnostics. Clin Chem 47(10):1918–1921

    Google Scholar 

  19. Weljie AM, Newton J, Mercier P, Carlson E, Slupsky CM (2006) Targeted profiling: quantitative analysis of H-1 NMR metabolomics data. Anal Chem 78(13):4430–4442

    Article  CAS  Google Scholar 

  20. Tredwell GD, Behrends V, Geier FM, Liebeke M, Bundy JG (2011) Between-person comparison of metabolite fitting for NMR-based quantitative metabolomics. Anal Chem 83(22):8683–8687

    Article  CAS  Google Scholar 

  21. Nicholson JK, Foxall PJD, Spraul M, Farrant RD, Lindon JC (1995) 750-MHz H-1 and H-1-C-13 NMR-Spectroscopy of human blood-plasma. Anal Chem 67(5):793–811

  22. Bingol K, Bruschweiler R (2014) Multidimensional approaches to NMR-based metabolomics. Anal Chem 86(1):47–57

    Article  CAS  Google Scholar 

  23. Aue WP, Karhan J, Ernst RR (1976) Homonuclear broad-band decoupling and 2-dimensional J-resolved NMR-spectroscopy. J Chem Phys 64(10):4226–4227

    Article  CAS  Google Scholar 

  24. Ludwig C, Viant MR (2010) Two-dimensional J-resolved NMR spectroscopy: review of a key methodology in the metabolomics toolbox. Phytochem Anal 21(1):22–32

    Article  CAS  Google Scholar 

  25. Huang Y, Cal S, Zhang Z, Chen Z (2014) High-resolution two-dimensional J-resolved NMR spectroscopy for biological systems. Biophys J 106(9):2061–2070

    Article  CAS  Google Scholar 

  26. Tang HR, Wang YL, Nicholson JK, Lindon JC (2004) Use of relaxation-edited one-dimensional and two dimensional nuclear magnetic resonance spectroscopy to improve detection of small metabolites in blood plasma. Anal Bioch 325(2):260–272

    Article  CAS  Google Scholar 

  27. Pearce JTM, Athersuch TJ, Ebbels TMD, Lindon JC, Nicholson JK, Keun HC (2008) Robust algorithms for automated chemical shift calibration of 1D H-1 NMR spectra of blood serum. Anal Chem 80(18):7158–7162

    Article  CAS  Google Scholar 

  28. 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(D1):D801–D807

    Article  CAS  Google Scholar 

  29. Ludwig C, Easton JM, Lodi A, Tiziani S, Manzoor SE, Southam AD, Byrne JJ, Bishop LM, He S, Arvanitis TN, Guenther UL, Viant MR (2012) Birmingham Metabolite Library: a publicly accessible database of 1-D H-1 and 2-D H-1 J-resolved NMR spectra of authentic metabolite standards (BML-NMR). Metabolomics 8(1):8–18

  30. Ulrich EL, Akutsu H, Doreleijers JF, Harano Y, Ioannidis YE, Lin J, Livny M, Mading S, Maziuk D, Miller Z, Nakatani E, Schulte CF, Tolmie DE, Wenger RK, Yao H, Markley JL (2008) BioMagResBank. Nucleic Acids Res 36:D402–D408

    Article  CAS  Google Scholar 

  31. Tiziani S, Lodi A, Ludwig C, Parsons HM, Viant MR (2008) Effects of the application of different window functions and projection methods on processing of (1)H J-resolved nuclear magnetic resonance spectra for metabolomics. Analy Chim Acta 610(1):80–88

  32. Vinaixa M, Rodriguez MA, Rull A, Beltran R, Blade C, Brezmes J, Canellas N, Joven J, Correig X (2010) Metabolomic assessment of the effect of dietary cholesterol in the progressive development of fatty liver disease. J Proteome Res 9(5):2527–2538

    Article  CAS  Google Scholar 

  33. Vitols C, Mercier P (2006) Correcting line shapes in NMR Spectra. CHENOMX. Available at: Accessed 1 Sept 2014

  34. Alam TM, Alam MK, McIntyre SK, Volk DE, Neerathilingam M, Luxon BA (2009) Investigation of chemometric instrumental transfer methods for high-resolution NMR. Anal Chem 81(11):4433–4443

    Article  CAS  Google Scholar 

  35. Saude EJ, Slupsky CM, Sykes BD (2006) Optimization of NMR analysis of biological fluids for quantitative accuracy. Metabolomics 2(3):113–123

    Article  CAS  Google Scholar 

Download references


The authors acknowledge CIBER de Diabetes y Enfermedades Metabólicas, an initiative of ISCIII (Ministerio de Ciencia e Innovación), and MINECO grant TEC2012-31074 for partially funding this work.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Josep Gómez.

Electronic supplementary material

Below is the link to the electronic supplementary material.


(PDF 863 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gómez, J., Brezmes, J., Mallol, R. et al. Dolphin: a tool for automatic targeted metabolite profiling using 1D and 2D 1H-NMR data. Anal Bioanal Chem 406, 7967–7976 (2014).

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: