LC-MS Spectra Processing

  • Rune Matthiesen
Part of the Methods in Molecular Biology book series (MIMB, volume 2051)


Peak extraction from raw data is the first step in LC-MS data analysis. The quality of this procedure can have dramatic effects on the quality and accuracy of all subsequent data analysis steps such as database searches and peak quantitation. The most important and most accurately measured physical entity provided by mass spectrometers is m/z. Peak processing algorithms must extract m/z values unaffected from overlapping peaks to avoid confusing downstream algorithms. The aim of this chapter is to provide a discussion of peak processing methods and furthermore discuss some of the yet unresolved or neglected issues. The chapter mainly discusses possible software developed in R for spectra processing and free software to generate Mascot generic files (mgf—see Chapter  1).

Key words

Noise filtering Peak extraction Deisotoping Decharging 



R.M. is supported by Fundação para a Ciência e a Tecnologia (CEEC position, 2019–2025 investigator), iNOVA4Health—UID/Multi/04462/2013, a program financially supported by Fundação para a Ciência e Tecnologia/Ministério da Educação e Ciência, through national funds and is cofunded by FEDER under the PT2020 Partnership Agreement. This work is also funded by FEDER funds through the COMPETE 2020 Programme and National Funds through FCT - Portuguese Foundation for Science and Technology under the projects number PTDC/BTM-TEC/30087/2017 and PTDC/BTM-TEC/30088/2017.


  1. 1.
    Washburn MP, Wolters D, Yates JR 3rd (2001) Large-scale analysis of the yeast proteome by multidimensional protein identification technology. Nat Biotechnol 19(3):242–247. 85686CrossRefGoogle Scholar
  2. 2.
    Fenn JB, Mann M, Meng CK, Wong SF, Whitehouse CM (1989) Electrospray ionization for mass spectrometry of large biomolecules. Science 246(4926):64–71CrossRefGoogle Scholar
  3. 3.
    Kieser R, Reynisson P, Mulligan TJ (2005) Definition of signal-to-noise ratio and its critical role in split-beam measurements. ICES J Mar Sci 62(1):123–130CrossRefGoogle Scholar
  4. 4.
    Fredriksson M, Petersson P, Jornten-Karlsson M, Axelsson BO, Bylund D (2007) An objective comparison of pre-processing methods for enhancement of liquid chromatography-mass spectrometry data. J Chromatogr A 1172(2):135–150. S0021-9673(07)01710-4CrossRefPubMedGoogle Scholar
  5. 5.
    Press WH, Teukolsky SA, Vetterling WT, Flannery BP (1988–1992) Numerical recipes in C: the art of scientific computing. Cambridge University Press, CambridgeGoogle Scholar
  6. 6.
    Savitzky A, Golay JEM (1964) Smoothing and differentiation of data by simplified least squares procedures. Anal Chem 36:1627–1639CrossRefGoogle Scholar
  7. 7.
    Chatfield C (1989) The analysis of time series, an introduction. Chapman & Hall/CRCGoogle Scholar
  8. 8.
    Eilers PH (2003) A perfect smoother. Anal Chem 75(14):3631–3636CrossRefGoogle Scholar
  9. 9.
    Kast J, Gentzel M, Wilm M, Richardson K (2003) Noise filtering techniques for electrospray quadrupole time of flight mass spectra. J Am Soc Mass Spectrom 14(7):766–776. S1044030503002642CrossRefPubMedGoogle Scholar
  10. 10.
    Morris JS, Coombes KR, Koomen J, Baggerly KA, Kobayashi R (2005) Feature extraction and quantification for mass spectrometry in biomedical applications using the mean spectrum. Bioinformatics 21(9):1764–1775. bti254CrossRefPubMedGoogle Scholar
  11. 11.
    Carvalho AS, Ribeiro H, Voabil P, Penque D, Jensen ON, Molina H, Matthiesen R (2014) Global mass spectrometry and transcriptomics array based drug profiling provides novel insight into glucosamine induced endoplasmic reticulum stress. Mol Cell Proteomics 13(12):3294–3307. Scholar
  12. 12.
    Vivo-Truyols G, Schoenmakers PJ (2006) Automatic selection of optimal Savitzky-Golay smoothing. Anal Chem 78(13):4598–4608. Scholar
  13. 13.
    Chernushevich IV, Loboda AV, Thomson BA (2001) An introduction to quadrupole-time-of-flight mass spectrometry. J Mass Spectrom 36(8):849–865. Scholar
  14. 14.
    Bylund D (2001) Chemometrics tools for enhanced performance in liquid chromatography-mass spectrometry. Uppsala University, Sweden, UppsalaGoogle Scholar
  15. 15.
    Cox J, Mann M (2008) MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat Biotechnol 26(12):1367–1372. nbt.1511CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Sheppard WF (1898) On the geometrical treatment of the ‘normal curve’ of statistics, with especial reference to correlation and to the theory of error. Proc Roy Soc 62:170–173CrossRefGoogle Scholar
  17. 17.
    Schlosser A, Volkmer-Engert R (2003) Volatile polydimethylcyclosiloxanes in the ambient laboratory air identified as source of extreme background signals in nanoelectrospray mass spectrometry. J Mass Spectrom 38(5):523–525. Scholar
  18. 18.
    Olsen JV, de Godoy LM, Li G, Macek B, Mortensen P, Pesch R, Makarov A, Lange O, Horning S, Mann M (2005) Parts per million mass accuracy on an Orbitrap mass spectrometer via lock mass injection into a C-trap. Mol Cell Proteomics 4(12):2010–2021. T500030-MCP200CrossRefPubMedGoogle Scholar
  19. 19.
    Matthiesen R, Trelle MB, Hojrup P, Bunkenborg J, Jensen ON (2005) VEMS 3.0: algorithms and computational tools for tandem mass spectrometry based identification of post-translational modifications in proteins. J Proteome Res 4(6):2338–2347. Scholar
  20. 20.
    Cox J, Mann M (2009) Computational principles of determining and improving mass precision and accuracy for proteome measurements in an Orbitrap. J Am Soc Mass Spectrom 20(8):1477–1485. S1044-0305(09)00378-XCrossRefPubMedGoogle Scholar
  21. 21.
    Zubarev R, Mann M (2007) On the proper use of mass accuracy in proteomics. Mol Cell Proteomics 6(3):377–381. Scholar
  22. 22.
    Wehofsky M, Hoffmann R (2002) Automated deconvolution and deisotoping of electrospray mass spectra. J Mass Spectrom 37(2):223–229. Scholar
  23. 23.
    Zhang Z, Marshall AG (1998) A universal algorithm for fast and automated charge state deconvolution of electrospray mass-to-charge ratio spectra. J Am Soc Mass Spectrom 9(3):225–233. S1044-0305(97)00284-5CrossRefPubMedGoogle Scholar
  24. 24.
    Senko MW, Beu SC, McLafferty FW (1995) Automated assignment of charge states from resolved isotopic peaks for multiply charged ions. J Am Soc Mass Spectrom 6:52–56CrossRefGoogle Scholar
  25. 25.
    Kaur P, O’Connor PB (2006) Algorithms for automatic interpretation of high resolution mass spectra. J Am Soc Mass Spectrom 17(3):459–468. S1044-0305(05)00984-0CrossRefPubMedGoogle Scholar
  26. 26.
    Senko MW, Beru SC, McLafferty FW (1995) Determination of monoisotopic masses and ion populations for large biomolecules from resolved isotopic distributions. J Am Soc Mass Spectrom 6:229–233CrossRefGoogle Scholar
  27. 27.
    Matthiesen R (2013) LC-MS spectra processing. Methods Mol Biol 1007:47–63. Scholar
  28. 28.
    Rodriguez-Suarez E, Gubb E, Alzueta IF, Falcon-Perez JM, Amorim A, Elortza F, Matthiesen R (2010) Virtual expert mass spectrometrist: iTRAQ tool for database-dependent search, quantitation and result storage. Proteomics 10(8):1545–1556. Scholar
  29. 29.
    Vizcaino JA, Csordas A, Del-Toro N, Dianes JA, Griss J, Lavidas I, Mayer G, Perez-Riverol Y, Reisinger F, Ternent T, Xu QW, Wang R, Hermjakob H (2016) 2016 update of the PRIDE database and its related tools. Nucleic Acids Res 44(22):11033. Scholar
  30. 30.
    Hermjakob H, Apweiler R (2006) The proteomics identifications database (PRIDE) and the ProteomExchange consortium: making proteomics data accessible. Expert Rev Proteomics 3(1):1–3. Scholar
  31. 31.
    Adusumilli R, Mallick P (2017) Data conversion with ProteoWizard msConvert. Methods Mol Biol 1550:339–368. Scholar
  32. 32.
    French WR, Zimmerman LJ, Schilling B, Gibson BW, Miller CA, Townsend RR, Sherrod SD, Goodwin CR, McLean JA, Tabb DL (2015) Wavelet-based peak detection and a new charge inference procedure for MS/MS implemented in ProteoWizard’s msConvert. J Proteome Res 14(2):1299–1307. Scholar
  33. 33.
    He L, Diedrich J, Chu YY, Yates JR 3rd (2015) Extracting accurate precursor information for tandem mass spectra by RawConverter. Anal Chem 87(22):11361–11367. Scholar
  34. 34.
    Gatto L, Lilley KS (2012) MSnbase-an R/Bioconductor package for isobaric tagged mass spectrometry data visualization, processing and quantitation. Bioinformatics 28(2):288–289. Scholar
  35. 35.
    Loos M, Gerber C, Corona F, Hollender J, Singer H (2015) Accelerated isotope fine structure calculation using pruned transition trees. Anal Chem 87(11):5738–5744CrossRefGoogle Scholar
  36. 36.
    Panse C, Grossmann J (2012) protViz: visualizing and analyzing mass spectrometry related data in proteomics. R packageGoogle Scholar
  37. 37.
    Startek MKŁaM (2017) IsoSpecR: the IsoSpec algorithm. R package version 103Google Scholar
  38. 38.
    Levnberg K (1944) A method for the solution of certain non-linear problems in least squares. Q Appl Math 2:164–168CrossRefGoogle Scholar
  39. 39.
    Brereton RG (2003) Data analysis for the laboratory and chemical plant. Wiley, ChichesterGoogle Scholar
  40. 40.
    Wehofsky M, Hoffmann R, Hubert M, Spengler B (2001) Isotopic deconvolution of matrix-assisted laser desorption/ionization mass spectra for substances-class specific analysis of complex samples. Eur J Mass Spectrom 7:39–46CrossRefGoogle Scholar
  41. 41.
    Horn DM, Zubarev RA, McLafferty FW (2000) Automated reduction and interpretation of high resolution electrospray mass spectra of large molecules. J Am Soc Mass Spectrom 11(4):320–332CrossRefGoogle Scholar
  42. 42.
    Meija J, Caruso JA (2004) Deconvolution of isobaric interferences in mass spectra. J Am Soc Mass Spectrom 15(5):654–658. S1044030504000169CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2020

Authors and Affiliations

  • Rune Matthiesen
    • 1
  1. 1.Computational and Experimental Biology Group, CEDOC, Chronic Diseases Research Centre, NOVA Medical School, Faculdade de Ciências MédicasUniversidade NOVA de LisboaLisboaPortugal

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