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63. Doerr A (2014) DIA mass spectrometry. Nat Methods 2014:121
64. Venable JD, Dong MQ, Wohlschlegel J, Dillin A, Yates JR (2004) Automated approach for
quantitative analysis of complex peptide mixtures from tandem mass spectra. Nat Methods
1(1):39–45. https://doi.org/10.1038/nmeth705
65. Bilbao A, Varesio E, Luban J, Strambio-De-Castillia C, Hopfgartner G, Müller M, Lisacek F
(2015) Processing strategies and software solutions for data-independent acquisition in mass
spectrometry. Proteomics 15(5–6):964–980. https://doi.org/10.1002/pmic.201400323
66. Kondrat RW, McClusky GA, Cooks RG (1978) Multiple reaction monitoring in mass spectrometry/mass spectrometry for direct analysis of complex mixtures. Anal Chem 50(14):2017–2021.
https://doi.org/10.1021/ac50036a020
67. Rathahao-Paris E, Alves S, Junot C, Tabet JC (2016) High resolution mass spectrometry for
structural identification of metabolites in metabolomics. Metabolomics:1–15. https://doi.
org/10.1007/s11306-015-0882-8
68. Michalski A, Damoc E, Lange O, Denisov E, Nolting D, Müller M, Viner R, Schwartz J,
Remes P, Belford M et al (2012) Ultra high resolution linear ion trap orbitrap mass spectrometer (Orbitrap elite) facilitates top down LC MS/MS and versatile peptide fragmentation
modes. Mol Cell Proteomics 11(3). https://doi.org/10.1074/mcp.O111.013698
69. Ghaste M, Mistrik R, Shulaev V (2016) Applications of Fourier transform ion cyclotron resonance (FT-ICR) and Orbitrap based high resolution mass spectrometry in metabolomics and
Lipidomics. Int J Mol Sci. https://doi.org/10.3390/ijms17060816
70. Mahieu NG, Patti GJ (2017) Systems-level annotation of a metabolomics data set reduces
25 000 features to fewer than 1000 unique metabolites. Anal Chem 89(19):10397–10406.
https://doi.org/10.1021/acs.analchem.7b02380
71. Blaženović I, Kind T, Ji J, Fiehn O (2018) Software tools and approaches for compound
identification of LC-MS/MS data in metabolomics. Meta 8(2):31. https://doi.org/10.3390/
metabo8020031
72. LCQuan. Thermo Fisher Scientific: Hemel Hempstead, Hetfordshire, UK
73. Depke T, Franke R, Brönstrup M (2017) Clustering of MS2 spectra using unsupervised
methods to aid the identification of secondary metabolites from Pseudomonas Aeruginosa. J
Chromatogr B 1071:19–28. https://doi.org/10.1016/j.jchromb.2017.06.002
74. MassHunter. Agilent: Santa Clara, CA, USA
75. Profile Analysis. Bruker
76. SIEVE. Thermo Fisher Scientific
77. Progenesis. Waters, Milford, MA, USA
78. 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. https://doi.org/10.1021/
ac300698c
79. Tsugawa H, Cajka T, Kind T, Ma Y, Higgins B, Ikeda K, Kanazawa M, VanderGheynst J, Fiehn
O, Arita M (2015) MS-DIAL: data-independent MS/MS deconvolution for comprehensive
metabolome analysis. Nat Methods 12(6):523–526. https://doi.org/10.1038/nmeth.3393
80. 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. https://doi.
org/10.1093/bioinformatics/btk039
81. MassBank of North America(2019). https://mona.fiehnlab.ucdavis.edu/. Accessed Sep
12, 2019
82. Wishart DS, Feunang YD, Marcu A, Guo AC, Liang K, Ázquez-Fresno RV, Sajed T, Johnson
D, Li C, Karu N et al (2018) HMDB 4.0: the human metabolome database for 2018. Nucleic
Acids Res 46. https://doi.org/10.1093/nar/gkx1089
83. NIST Standard Reference Database 1A v17|NIST (2019). https://www.nist.gov/srd/nist-standard-reference-database-1a-v17. Accessed Sep 12, 2019
84. Horai H, Arita M, Kanaya S, Nihei Y, Ikeda T, Suwa K, Ojima Y, Tanaka K, Tanaka S, Aoshima
K et al (2010) MassBank: A public repository for sharing mass spectral data for life sciences.
J Mass Spectrom 45(7):703–714. https://doi.org/10.1002/jms.1777
E. S. Rivera et al.