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  • Review Article
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Global chemical analysis of biology by mass spectrometry

Nature Reviews Chemistry volume 1, Article number: 0054 (2017) Cite this article

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Abstract

Mass spectrometry instruments measure the mass to charge ratio of ions, from which we infer the molecular structures. They are key tools for investigating the incredibly diverse chemistry that is associated with biological systems. Typically, when one thinks about the chemistry of biology, one thinks of biochemical pathways, structural lipids or carbohydrates. However, numerous additional chemistries are part of various biological systems. These include molecules that originate from diet, water treatment, personal care, medications, pollutants and environmental exposures including plastics, clothes and furniture. These principles apply not only to people but to all of biology, from the worms at the bottom of the ocean, to the bacteria in our belly buttons and to the birds that fly over Mount Everest. In the past decade, our capacity to inventory the chemistry of biological systems using mass spectrometry at a global level has been revolutionized. In this Review, we discuss the informatics and hardware tools that are available for small-molecule analysis and provide an overview of the tools that could transform how we study the chemistry of biological systems; perhaps in the future this will be as easy as taking a photograph with a smartphone.

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Figure 1: The chemistry of biology.
Figure 2: A generalized description of a mass spectrometry data processing to knowledge workflow.
Figure 3: Mass spectrometric data can be analysed using various methods to elucidate the structures of organic molecules.
Figure 4: Trends in the estimated cost for mass spectrometry in relation to DNA sequencing.
Figure 5: Mass spectrometry projects in the public domain for which the raw data can be downloaded.

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References

  1. Gohlke, R. S. & McLafferty, F. W. Early gas chromatography/mass spectrometry. J. Am. Soc. Mass Spectrom. 4, 367–371 (1993).

    Article  CAS  PubMed  Google Scholar 

  2. Lesch, M. & Nyhan, W. L. A familial disorder of uric acid metabolism and central nervous system function. Am. J. Med. 36, 561–570 (1964).

    Article  CAS  PubMed  Google Scholar 

  3. Pauling, L., Robinson, A. B., Teranishi, R. & Cary, P. Quantitative analysis of urine vapor and breath by gas–liquid partition chromatography. Proc. Natl Acad. Sci. USA 68, 2374–2376 (1971).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Yamashita, M. & Fenn, J. B. Electrospray ion source. Another variation on the free-jet theme. J. Phys. Chem. 88, 4451–4459 (1984).

    Article  CAS  Google Scholar 

  5. Karas, M., Bachmann, D. & Hillenkamp, F. Influence of the wavelength in high-irradiance ultraviolet laser desorption mass spectrometry of organic molecules. Anal. Chem. 57, 2935–2939 (1985).

    Article  CAS  Google Scholar 

  6. Tanaka, K. et al. Protein and polymer analyses up to m/z 100 000 by laser ionization time-of-flight mass spectrometry. Rapid Commun. Mass Spectrom. 2, 151–153 (1988).

    Article  CAS  Google Scholar 

  7. Takada, Y. et al. High-throughput walkthrough detection portal for counter terrorism: detection of triacetone triperoxide (TATP) vapor by atmospheric-pressure chemical ionization ion trap mass spectrometry. Rapid Commun. Mass Spectrom. 25, 2448–2452 (2011).

    Article  CAS  PubMed  Google Scholar 

  8. Jannetto, P. J. & Fitzgerald, R. L. Effective use of mass spectrometry in the clinical laboratory. Clin. Chem. 62, 92–98 (2016).

    Article  CAS  PubMed  Google Scholar 

  9. Botrè, F. Mass spectrometry and illicit drug testing: analytical challenges of the anti-doping laboratories. Expert Rev. Proteomics 5, 535–539 (2008).

    Article  PubMed  Google Scholar 

  10. Pico, Y. Advanced Mass Spectrometry for Food Safety and Quality (Elsevier, 2015).

    Google Scholar 

  11. de Lima Moreira, F. et al. Metabolic profile and safety of piperlongumine. Sci. Rep. 6, 33646 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Rocha, B. A. et al. In vitro metabolism of monensin A: microbial and human liver microsomes models. Xenobiotica 44, 326–335 (2014).

    Article  CAS  PubMed  Google Scholar 

  13. Darst, C. R., Menéndez-Guerrero, P. A., Coloma, L. A. & Cannatella, D. C. Evolution of dietary specialization and chemical defense in poison frogs (Dendrobatidae): a comparative analysis. Am. Nat. 165, 56–69 (2005).

    Article  PubMed  Google Scholar 

  14. Teuten, E. L. Two abundant bioaccumulated halogenated compounds are natural products. Science 307, 917–920 (2005).

    Article  CAS  PubMed  Google Scholar 

  15. Agarwal, V. et al. Biosynthesis of polybrominated aromatic organic compounds by marine bacteria. Nat. Chem. Biol. 10, 640–647 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Agarwal, V. et al. Complexity of naturally produced polybrominated diphenyl ethers revealed via mass spectrometry. Environ. Sci. Technol. 49, 1339–1346 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Rodrigues Hoffmann, A. et al. The skin microbiome in healthy and allergic dogs. PLoS ONE 9, e83197 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Song, S. J. et al. Cohabiting family members share microbiota with one another and with their dogs. eLife 2, e00458 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Waldron, A. C. & Naber, E. C. Importance of feed as an unavoidable source of pesticide contamination in poultry meat and eggs. 1. Residues in feedstuff. Poult. Sci. 53, 1359–1371 (1974).

    Article  CAS  PubMed  Google Scholar 

  20. Nair, D. N. & Padmavathy, S. Impact of endophytic microorganisms on plants, environment and humans. ScientificWorldJournal 2014, 250693 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  21. Seger, C. & Vogeser, M. in LC-MS in Drug Bioanalysis (eds Xu, Q. A. & Madden, T. L. )109–126 (Springer, 2012).

    Book  Google Scholar 

  22. Ganna, A. et al. A workflow for UPLC-MS non-targeted metabolomic profiling in large human population-based studies. Preprint at bioRxivhttp://dx.doi.org/10.1101/002782 (2014).

  23. da Silva, R. R., Dorrestein, P. C. & Quinn, R. A. Illuminating the dark matter in metabolomics. Proc. Natl Acad. Sci. USA 112, 12549–12550 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Creek, D. J. et al. Metabolite identification: are you sure? And how do your peers gauge your confidence? Metabolomics 10, 350–353 (2014).

    Article  CAS  Google Scholar 

  25. Wagner, C., El Omari, M. & König, G. M. Biohalogenation: nature's way to synthesize halogenated metabolites. J. Nat. Prod. 72, 540–553 (2009).

    Article  CAS  PubMed  Google Scholar 

  26. Hatfield, D. L., Berry, M. J. & Gladyshev, V. N. (eds) Selenium: its Molecular Biology and Role in Human Health (Springer Science & Business Media, 2011).

    Google Scholar 

  27. Gaspar, A., Lucio, M., Harir, M. & Schmitt-Kopplin, P. Targeted and non-targeted boron complex formation followed by electrospray Fourier transform ion cyclotron mass spectrometry: a novel approach for identifying boron esters with natural organic matter. Eur. J. Mass Spectrom. 17, 113–123 (2011).

    Article  CAS  Google Scholar 

  28. Knight, M. J., Senior, L., Nancolas, B., Ratcliffe, S. & Curnow, P. Direct evidence of the molecular basis for biological silicon transport. Nat. Commun. 7, 11926 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Kind, T. & Fiehn, O. Seven golden rules for heuristic filtering of molecular formulas obtained by accurate mass spectrometry. BMC Bioinformatics 8, 105 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Kind, T. & Fiehn, O. Advances in structure elucidation of small molecules using mass spectrometry. Bioanal. Rev. 2, 23–60 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Pence, H. E. & Williams, A. ChemSpider: an online chemical information resource. J. Chem. Educ. 87, 1123–1124 (2010).

    Article  CAS  Google Scholar 

  32. Caboche, S. et al. NORINE: a database of nonribosomal peptides. Nucleic Acids Res. 36, D326–D331 (2008).

    Article  CAS  PubMed  Google Scholar 

  33. Wishart, D. S. et al. HMDB 3.0 — The Human Metabolome Database in 2013. Nucleic Acids Res. 41, D801–D807 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Wishart, D. et al. T3DB: the toxic exposome database. Nucleic Acids Res. 43, D928–D934 (2015).

    Article  CAS  PubMed  Google Scholar 

  35. Psychogios, N. et al. The human serum metabolome. PLoS ONE 6, e16957 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. McEachran, A. D., Sobus, J. R. & Williams, A. J. Identifying known unknowns using the US EPA's CompTox Chemistry Dashboard. Anal. Bioanal. Chem. 409, 1729–1735 (2017).

    Article  CAS  PubMed  Google Scholar 

  37. Ntie-Kang, F. et al. AfroDb: a select highly potent and diverse natural product library from African medicinal plants. PLoS ONE 8, e78085 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Sanderson, K. Databases aim to bridge the East–West divide of drug discovery. Nat. Med. 17, 1531–1531 (2011).

    Article  CAS  PubMed  Google Scholar 

  39. Jeffryes, J. G. et al. MINEs: open access databases of computationally predicted enzyme promiscuity products for untargeted metabolomics. J. Cheminform. 7, 44 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Huan, T. et al. MyCompoundID MS/MS Search: metabolite identification using a library of predicted fragment-ion-spectra of 383,830 possible human metabolites. Anal. Chem. 87, 10619–10626 (2015).

    Article  CAS  PubMed  Google Scholar 

  41. Villas-Bôas, S. G., Roessner, U., Hansen, M. A. E., Smedsgaard, J. & Nielsen, J. Metabolome Analysis: An Introduction (Wiley, 2007).

    Book  Google Scholar 

  42. Dunn, W. B., Wilson, I. D., Nicholls, A. W. & Broadhurst, D. The importance of experimental design and QC samples in large-scale and MS-driven untargeted metabolomic studies of humans. Bioanalysis 4, 2249–2264 (2012).

    Article  CAS  PubMed  Google Scholar 

  43. Johnson, C. H., Ivanisevic, J., Benton, H. P. & Siuzdak, G. Bioinformatics: the next frontier of metabolomics. Anal. Chem. 87, 147–156 (2015).

    Article  CAS  PubMed  Google Scholar 

  44. Griss, J. et al. The mzTab data exchange format: communicating mass-spectrometry-based proteomics and metabolomics experimental results to a wider audience. Mol. Cell. Proteomics 13, 2765–2775 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Field, D. et al. The minimum information about a genome sequence (MIGS) specification. Nat. Biotechnol. 26, 541–547 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Yilmaz, P. et al. Minimum information about a marker gene sequence (MIMARKS) and minimum information about any (x) sequence (MIxS) specifications. Nat. Biotechnol. 29, 415–420 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Röst, H. L. et al. OpenMS: a flexible open-source software platform for mass spectrometry data analysis. Nat. Methods 13, 741–748 (2016).

    Article  CAS  PubMed  Google Scholar 

  48. Misra, B. B. & van der Hooft, J. J. J. Updates in metabolomics tools and resources: 2014–2015. Electrophoresis 37, 86–110 (2016).

    Article  CAS  PubMed  Google Scholar 

  49. Xia, J. & Wishart, D. S. in Current Protocols in Bioinformatics 14.10.1–14.10.91 (Wiley, 2016).

    Google Scholar 

  50. Xia, J. et al. MetaboAnalyst: a web server for metabolomic data analysis and interpretation. Nucleic Acids Res. 37, W652–W660 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Tautenhahn, R., Patti, G. J., Rinehart, D. & Siuzdak, G. E. XCMS Online: a web-based platform to process untargeted metabolomic data. Anal. Chem. 84, 5035–5039 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Barnes, S. et al. Training in metabolomics research. II. Processing and statistical analysis of metabolomics data, metabolite identification, pathway analysis, applications of metabolomics and its future. J. Mass Spectrom. 51, 535–548 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Chambers, M. C. et al. A cross-platform toolkit for mass spectrometry and proteomics. Nat. Biotechnol. 30, 918–920 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Vinaixa, M. et al. A guideline to univariate statistical analysis for LC/MS-based untargeted metabolomics-derived data. Metabolites 2, 775–795 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. van den Berg, R. A., Hoefsloot, H. C. J., Westerhuis, J. A., Smilde, A. K. & van Der Werf, M. J. Centering, scaling, and transformations: improving the biological information content of metabolomics data. BMC Genomics 7, 142 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Ejigu, B. A. et al. Evaluation of normalization methods to pave the way towards large-scale LC-MS-based metabolomics profiling experiments. OMICS 17, 473–485 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Di Guida, R. et al. Non-targeted UHPLC-MS metabolomic data processing methods: a comparative investigation of normalisation, missing value imputation, transformation and scaling. Metabolomics 12, 93 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Armitage, E. G., Godzien, J., Alonso-Herranz, V., López-Gonzálvez, Á. & Barbas, C. Missing value imputation strategies for metabolomics data. Electrophoresis 36, 3050–3060 (2015).

    Article  CAS  PubMed  Google Scholar 

  59. Libbrecht, M. W. & Noble, W. S. Machine learning applications in genetics and genomics. Nat. Rev. Genet. 16, 321–332 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Wold, S., Sjöström, M. & Eriksson, L. PLS-regression: a basic tool of chemometrics. Chemometr Intell. Lab. Syst. 58, 109–130 (2001).

    Article  CAS  Google Scholar 

  61. Hung, J.-H., Yang, T.-H., Hu, Z., Weng, Z. & DeLisi, C. Gene set enrichment analysis: performance evaluation and usage guidelines. Brief. Bioinform. 13, 281–291 (2012).

    Article  PubMed  Google Scholar 

  62. Chagoyen, M. & Pazos, F. Tools for the functional interpretation of metabolomic experiments. Brief. Bioinform. 14, 737–744 (2012).

    Article  PubMed  Google Scholar 

  63. Khatri, P., Sirota, M. & Butte, A. J. Ten years of pathway analysis: current approaches and outstanding challenges. PLoS Comput. Biol. 8, e1002375 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Bouslimani, A. et al. Molecular cartography of the human skin surface in 3D. Proc. Natl Acad. Sci. USA 112, E2120–E2129 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Johnson, S. R. & Lange, B. M. Open-access metabolomics databases for natural product research: present capabilities and future potential. Front. Bioeng. Biotechnol. 3, 22 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  66. Kind, T. et al. Identification of small molecules using accurate mass MS/MS search. Mass Spectrom. Rev.http://dx.doi.org/10.1002/mas.21535 (2017).

  67. Heller, S., McNaught, A., Stein, S., Tchekhovskoi, D. & Pletnev, I. InChI — the worldwide chemical structure identifier standard. J. Cheminform. 5, 7 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Weininger, D. SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. J. Chem. Inf. Comput. Sci. 28, 31–36 (1988).

    Article  CAS  Google Scholar 

  69. Wohlgemuth, G. et al. SPLASH, a hashed identifier for mass spectra. Nat. Biotechnol. 34, 1099–1101 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Feng, Q. et al. Integrated metabolomics and metagenomics analysis of plasma and urine identified microbial metabolites associated with coronary heart disease. Sci. Rep. 6, 22525 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Bowen, B. P. & Northen, T. R. Dealing with the unknown: metabolomics and metabolite atlases. J. Am. Soc. Mass Spectrom. 21, 1471–1476 (2010).

    Article  CAS  PubMed  Google Scholar 

  72. Brown, M. et al. Mass spectrometry tools and metabolite-specific databases for molecular identification in metabolomics. Analyst 134, 1322–1332 (2009).

    Article  CAS  PubMed  Google Scholar 

  73. Sumner, L. W. et al. Proposed minimum reporting standards for chemical analysis Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI). Metabolomics 3, 211–221 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Dias, D. et al. Current and future perspectives on the structural identification of small molecules in biological systems. Metabolites 6, 46 (2016).

    Article  CAS  PubMed Central  Google Scholar 

  75. Lawson, T. N. et al. msPurity: automated evaluation of precursor ion purity for mass spectrometry-based fragmentation in metabolomics. Anal. Chem. 89, 2432–2439 (2017).

    Article  CAS  PubMed  Google Scholar 

  76. Wang, M. et al. Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking. Nat. Biotechnol. 34, 828–837 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Dhanasekaran, A. R., Pearson, J. L., Ganesan, B. & Weimer, B. C. Metabolome searcher: a high throughput tool for metabolite identification and metabolic pathway mapping directly from mass spectrometry and using genome restriction. BMC Bioinformatics 16, 62 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Grapov, D., Wanichthanarak, K. & Fiehn, O. MetaMapR: pathway independent metabolomic network analysis incorporating unknowns. Bioinformatics 31, 2757–2760 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Hufsky, F., Scheubert, K. & Böcker, S. New kids on the block: novel informatics methods for natural product discovery. Nat. Prod. Rep. 31, 807–817 (2014).

    Article  CAS  PubMed  Google Scholar 

  80. Allwood, J. et al. CASMI — the small molecule identification process from a Birmingham perspective. Metabolites 3, 397–411 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Schymanski, E. L. et al. Critical assessment of small molecule identification 2016: automated methods. J. Cheminform. 9, 22 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  82. Dührkop, K., Shen, H., Meusel, M., Rousu, J. & Böcker, S. Searching molecular structure databases with tandem mass spectra using CSI:FingerID. Proc. Natl Acad. Sci. USA 112, 12580–12585 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Brouard, C. et al. Fast metabolite identification with input output kernel regression. Bioinformatics 32, i28–i36 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Ruttkies, C., Schymanski, E. L., Wolf, S., Hollender, J. & Neumann, S. MetFrag relaunched: incorporating strategies beyond in silico fragmentation. J. Cheminform. 8, 3 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Allen, F., Greiner, R. & Wishart, D. Competitive fragmentation modeling of ESI-MS/MS spectra for putative metabolite identification. Metabolomics 11, 98–110 (2014).

    Article  CAS  Google Scholar 

  86. van der Hooft, J. J. J., Wandy, J., Barrett, M. P., Burgess, K. E. V. & Rogers, S. Topic modeling for untargeted substructure exploration in metabolomics. Proc. Natl Acad. Sci. USA 113, 13738–13743 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Kind, T. et al. LipidBlast in silico tandem mass spectrometry database for lipid identification. Nat. Methods 10, 755–758 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Mohimani, H. et al. Dereplication of peptidic natural products through database search of mass spectra. Nat. Chem. Biol. 13, 30–37 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Böcker, S. Searching molecular structure databases using tandem MS data: are we there yet? Curr. Opin. Chem. Biol. 36, 1–6 (2017).

    Article  CAS  PubMed  Google Scholar 

  90. Kind, T. & Fiehn, O. Strategies for dereplication of natural compounds using high-resolution tandem mass spectrometry. Phytochem. Lett.http://dx.doi.org/10.1016/j.phytol.2016.11.006 (2016).

  91. Watrous, J. et al. Mass spectral molecular networking of living microbial colonies. Proc. Natl Acad. Sci. USA 109, E1743–E1752 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  92. Allard, P.-M. et al. Integration of molecular networking and in-silico MS/MS fragmentation for natural products dereplication. Anal. Chem. 88, 3317–3323 (2016).

    Article  CAS  PubMed  Google Scholar 

  93. Bartlett, R. J. & Musiał, M. Coupled-cluster theory in quantum chemistry. Rev. Mod. Phys. 79, 291–352 (2007).

    Article  CAS  Google Scholar 

  94. Bauer, C. A. & Grimme, S. How to compute electron ionization mass spectra from first principles. J. Phys. Chem. A 120, 3755–3766 (2016).

    Article  CAS  PubMed  Google Scholar 

  95. Poater, J., Duran, M. & Solà, M. Parametrization of the Becke3-LYP hybrid functional for a series of small molecules using quantum molecular similarity techniques. J. Comput. Chem. 22, 1666–1678 (2001).

    Article  CAS  Google Scholar 

  96. Peverati, R. & Truhlar, D. G. Quest for a universal density functional: the accuracy of density functionals across a broad spectrum of databases in chemistry and physics. Phil. Trans. R. Soc. A 372, 20120476 (2014).

    Article  CAS  PubMed  Google Scholar 

  97. Sun, J. et al. Accurate first-principles structures and energies of diversely bonded systems from an efficient density functional. Nat. Chem. 8, 831–836 (2016).

    Article  CAS  PubMed  Google Scholar 

  98. Wang, H. et al. High-efficiency multiphoton boson sampling. Nat. Photonics 11, 361–365 (2017).

    Article  CAS  Google Scholar 

  99. Denchev, V. S. et al. What is the computational value of finite-range tunneling? Phys. Rev. X 6, 31015 (2016).

    Google Scholar 

  100. Aguilar-Mogas, A., Sales-Pardo, M., Navarro, M., Guimerà, R. & Yanes, O. iMet: a network-based computational tool to assist in the annotation of metabolites from tandem mass spectra. Anal. Chem. 89, 3474–3482 (2017).

    Article  CAS  PubMed  Google Scholar 

  101. Hastings, J. et al. The ChEBI reference database and ontology for biologically relevant chemistry: enhancements for 2013. Nucleic Acids Res. 41, D456–D463 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Djoumbou Feunang, Y. et al. ClassyFire: automated chemical classification with a comprehensive, computable taxonomy. J. Cheminform. 8, 61 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  103. Quinn, R. A. et al. Molecular networking as a drug discovery, drug metabolism, and precision medicine strategy. Trends Pharmacol. Sci. 38, 143–154 (2017).

    Article  CAS  PubMed  Google Scholar 

  104. Ridder, L. & Wagener, M. SyGMa: combining expert knowledge and empirical scoring in the prediction of metabolites. ChemMedChem 3, 821–832 (2008).

    Article  CAS  PubMed  Google Scholar 

  105. Ziemert, N., Alanjary, M. & Weber, T. The evolution of genome mining in microbes — a review. Nat. Prod. Rep. 33, 988–1005 (2016).

    Article  CAS  PubMed  Google Scholar 

  106. Kersten, R. D. et al. A mass spectrometry-guided genome mining approach for natural product peptidogenomics. Nat. Chem. Biol. 7, 794–802 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. Kersten, R. D. et al. Glycogenomics as a mass spectrometry-guided genome-mining method for microbial glycosylated molecules. Proc. Natl Acad. Sci. USA 110, E4407–E4416 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  108. Duncan, K. R. et al. Molecular networking and pattern-based genome mining improves discovery of biosynthetic gene clusters and their products from Salinispora species. Chem. Biol. 22, 460–471 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. Mohimani, H. et al. NRPquest: coupling mass spectrometry and genome mining for nonribosomal peptide discovery. J. Nat. Prod. 77, 1902–1909 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  110. Mohimani, H. et al. Automated genome mining of ribosomal peptide natural products. ACS Chem. Biol. 9, 1545–1551 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. Zhang, Q. et al. Structural investigation of ribosomally synthesized natural products by hypothetical structure enumeration and evaluation using tandem MS. Proc. Natl Acad. Sci. USA 111, 12031–12036 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  112. Ibrahim, A. et al. Dereplicating nonribosomal peptides using an informatic search algorithm for natural products (iSNAP) discovery. Proc. Natl Acad. Sci. USA 109, 19196–19201 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  113. Johnston, C. W. et al. An automated Genomes-to-Natural Products platform (GNP) for the discovery of modular natural products. Nat. Commun. 6, 8421 (2015).

    Article  CAS  PubMed  Google Scholar 

  114. Steuer, R., Kurths, J., Fiehn, O. & Weckwerth, W. Observing and interpreting correlations in metabolomic networks. Bioinformatics 19, 1019–1026 (2003).

    Article  CAS  PubMed  Google Scholar 

  115. Lai, Z. & Fiehn, O. Mass spectral fragmentation of trimethylsilylated small molecules. Mass Spectrom. Rev.http://dx.doi.org/10.1002/mas.21518 (2016).

  116. Chibucos, M. C. et al. Standardized description of scientific evidence using the Evidence Ontology (ECO). Database (Oxford) 2014, bau075 (2014).

    Article  Google Scholar 

  117. Meringer, M. & Schymanski, E. L. Small molecule identification with MOLGEN and mass spectrometry. Metabolites 3, 440–462 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  118. Lindsay, R. K., Buchanan, B. G., Feigenbaum, E. A. & Lederberg, J. Applications of Artificial Intelligence for Organic Chemistry: the DENDRAL Project (McGraw-Hill, 1980).

    Google Scholar 

  119. Kerber, A., Laue, R., Meringer, M. & Varmuza, K. MOLGEN-MS: evaluation of low resolution electron impact mass spectra with MS classification and exhaustive structure generation. Adv. Mass Spectrom. 15, 22 (2001).

    Google Scholar 

  120. Böcker, S., Letzel, M. C., Lipták, Z. & Pervukhin, A. SIRIUS: decomposing isotope patterns for metabolite identification. Bioinformatics 25, 218–224 (2009).

    Article  CAS  PubMed  Google Scholar 

  121. Awad, H., Khamis, M. M. & El-Aneed, A. Mass spectrometry, review of the basics: ionization. Appl. Spectrosc. Rev. 50, 158–175 (2015).

    Article  CAS  Google Scholar 

  122. El-Aneed, A., Cohen, A. & Banoub, J. Mass spectrometry, review of the basics: electrospray, MALDI, and commonly used mass analyzers. Appl. Spectrosc. Rev. 44, 210–230 (2009).

    Article  CAS  Google Scholar 

  123. Marshall, A. G., Hendrickson, C. L. & Jackson, G. S. Fourier transform ion cyclotron resonance mass spectrometry: a primer. Mass Spectrom. Rev. 17, 1–35 (1998).

    Article  CAS  PubMed  Google Scholar 

  124. Nikolaev, E. N., Boldin, I. A., Jertz, R. & Baykut, G. Initial experimental characterization of a new ultra-high resolution FTICR cell with dynamic harmonization. J. Am. Soc. Mass Spectrom. 22, 1125–1133 (2011).

    Article  CAS  PubMed  Google Scholar 

  125. Hoffmann, E.de. & Stroobant, V. Mass Spectrometry: Principles and Applications (Wiley, 2007).

    Google Scholar 

  126. Purves, R. W., Guevremont, R., Day, S., Pipich, C. W. & Matyjaszczyk, M. S. Mass spectrometric characterization of a high-field asymmetric waveform ion mobility spectrometer. Rev. Sci. Instrum. 69, 4094 (1998).

    Article  CAS  Google Scholar 

  127. Bicchi, C. et al. Direct resistively heated column gas chromatography (Ultrafast module-GC) for high-speed analysis of essential oils of differing complexities. J. Chromatogr. A 1024, 195–207 (2004).

    Article  CAS  PubMed  Google Scholar 

  128. Cutillas, P. Principles of nanoflow liquid chromatography and applications to proteomics. Curr. Nanosci. 1, 65–71 (2005).

    Article  CAS  Google Scholar 

  129. Servick, K. Scientists reveal proposal to build human genome from scratch. Sciencehttp://dx.doi.org/10.1126/science.aag0588 (2016).

  130. Human Microbiome Project Consortium. Structure, function and diversity of the healthy human microbiome. Nature 486, 207–214 (2012).

    Article  CAS  Google Scholar 

  131. Gilbert, J. A. et al. Meeting report: The Terabase Metagenomics Workshop and the Vision of an Earth Microbiome Project. Stand. Genomic Sci. 3, 243–248 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  132. Telenti, A. et al. Deep sequencing of 10,000 human genomes. Proc. Natl Acad. Sci. USA 113, 11901–11906 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  133. White, R. A. III, Callister, S. J., Moore, R. J., Baker, E. S. & Jansson, J. K. The past, present and future of microbiome analyses. Nat. Protoc. 11, 2049–2053 (2016).

    Article  CAS  Google Scholar 

  134. Shendure, J. & Ji, H. Next-generation DNA sequencing. Nat. Biotechnol. 26, 1135–1145 (2008).

    Article  CAS  PubMed  Google Scholar 

  135. Makarov, A. Electrostatic axially harmonic orbital trapping: a high-performance technique of mass analysis. Anal. Chem. 72, 1156–1162 (2000).

    Article  CAS  PubMed  Google Scholar 

  136. Comisarow, M. B. & Marshall, A. G. Fourier transform ion cyclotron resonance spectroscopy. Chem. Phys. Lett. 25, 282–283 (1974).

    Article  CAS  Google Scholar 

  137. Stein, S. Mass spectral reference libraries: an ever-expanding resource for chemical identification. Anal. Chem. 84, 7274–7282 (2012).

    Article  CAS  PubMed  Google Scholar 

  138. Haug, K. et al. MetaboLights — an open-access general-purpose repository for metabolomics studies and associated meta-data. Nucleic Acids Res. 41, D781–D786 (2013).

    Article  CAS  PubMed  Google Scholar 

  139. Sud, M. et al. Metabolomics Workbench: an international repository for metabolomics data and metadata, metabolite standards, protocols, tutorials and training, and analysis tools. Nucleic Acids Res. 44, D463–D470 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  140. Schauer, N. et al. GC–MS libraries for the rapid identification of metabolites in complex biological samples. FEBS Lett. 579, 1332–1337 (2005).

    Article  CAS  PubMed  Google Scholar 

  141. Ferry-Dumazet, H. et al. MeRy-B: a web knowledgebase for the storage, visualization, analysis and annotation of plant NMR metabolomic profiles. BMC Plant Biol. 11, 104 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  142. Scholz, M. & Fiehn, O. SetupX — a public study design database for metabolomic projects. Pac. Symp. Biocomput. 2007, 169–180 (2007).

    Google Scholar 

  143. Skogerson, K., Wohlgemuth, G., Barupal, D. K. & Fiehn, O. The volatile compound BinBase mass spectral database. BMC Bioinformatics 12, 321 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  144. Myint, L., Kleensang, A., Zhao, L., Hartung, T. & Hansen, K. D. Joint bounding of peaks across samples improves differential analysis in mass spectrometry-based metabolomics. Anal. Chem. 89, 3517–3523 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  145. Defelice, B. C. et al. Mass Spectral Feature List Optimizer (MS-FLO): a tool to minimize false positive peak reports in untargeted liquid chroamtography–mass spectrometry (LC–MS) data processing. Anal. Chem. 89, 3250–3255 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  146. David, P. A. Understanding the emergence of ‘open science’ institutions: functionalist economics in historical context. Ind. Corp. Chang. 13, 571–589 (2004).

    Article  Google Scholar 

  147. Томилин, К.А . in Физика XIX-XX вв. в общенаучном и социокультурном контекстах [Russian] Vol. 3 264–304 (Янус, 1997).

  148. Peters, B. How Not to Network a Nation: The Uneasy History of the Soviet Internet (MIT Press, 2016).

    Book  Google Scholar 

  149. [No authors listed.] Where are the data? Nat. Meth. 13, 799 (2016).

  150. Baker, M. 1,500 scientists lift the lid on reproducibility. Nature 533, 452–454 (2016).

    Article  CAS  PubMed  Google Scholar 

  151. Ogungbeni, J. I., Obiamalu, A. R., Ssemambo, S. & Bazibu, C. M. The roles of academic libraries in propagating open science: a qualitative literature review. Inf. Dev.http://dx.doi.org/10.1177/0266666916678444 (2016).

  152. Surowiecki, J. The Wisdom of Crowds: Why the Many Are Smarter Than the Few and How Collective Wisdom Shapes Business, Economics, Societies and Nations (Doubleday, 2006).

    Google Scholar 

  153. Lozupone, C. A. et al. Meta-analyses of studies of the human microbiota. Genome Res. 23, 1704–1714 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  154. Debelius, J. et al. Tiny microbes, enormous impacts: what matters in gut microbiome studies? Genome Biol. 17, 217 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  155. Gilbert, J. A. et al. Microbiome-wide association studies link dynamic microbial consortia to disease. Nature 535, 94–103 (2016).

    Article  CAS  PubMed  Google Scholar 

  156. Bazsó, F. L. et al. Quantitative comparison of tandem mass spectra obtained on various instruments. J. Am. Soc. Mass Spectrom. 27, 1357–1365 (2016).

    Article  CAS  PubMed  Google Scholar 

  157. McDonald, R. S. & Wilks, P. A. JCAMP-DX: a standard form for exchange of infrared spectra in computer readable form. Appl. Spectrosc. 42, 151–162 (1988).

    Article  CAS  Google Scholar 

  158. Martens, L. et al. mzML — a community standard for mass spectrometry data. Mol. Cell. Proteomics 10, R110.000133 (2011).

    Article  PubMed  Google Scholar 

  159. Pedrioli, P. G. A. et al. A common open representation of mass spectrometry data and its application to proteomics research. Nat. Biotechnol. 22, 1459–1466 (2004).

    Article  CAS  PubMed  Google Scholar 

  160. Dougherty, M. T. et al. Unifying biological image formats with HDF5. Commun. ACM 52, 42–47 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  161. Deutsch, E. W. File formats commonly used in mass spectrometry proteomics. Mol. Cell. Proteomics 11, 1612–1621 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  162. Alfassi, Z. B. et al. Vector analysis of multi-measurements identification. J. Radioanal. Nucl. Chem. 266, 245–250 (2005).

    Article  CAS  Google Scholar 

  163. ASTM International. Standard specification for analytical data interchange protocol for chromatographic data. ASTMhttp://dx.doi.org/10.1520/E1947-98R14 (2014).

  164. Salek, R. M. et al. COordination of Standards in MetabOlomicS (COSMOS): facilitating integrated metabolomics data access. Metabolomics 11, 1587–1597 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  165. Medema, M. H. et al. Minimum information about a biosynthetic gene cluster. Nat. Chem. Biol. 11, 625–631 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  166. Diminic, J. et al. Databases of the thiotemplate modular systems (CSDB) and their in silico recombinants (r-CSDB). J. Ind. Microbiol. Biotechnol. 40, 653–659 (2013).

    Article  CAS  PubMed  Google Scholar 

  167. Perkel, J. M. Life science technologies: miniaturizing mass spectrometry. Science 343, 928–930 (2014).

    Article  Google Scholar 

  168. Cacciatore, S. & Loda, M. Innovation in metabolomics to improve personalized healthcare. Ann. NY Acad. Sci. 1346, 57–62 (2015).

    Article  CAS  PubMed  Google Scholar 

  169. Montenegro-Burke, J. R. et al. Data streaming for metabolomics: accelerating data processing and analysis from days to minutes. Anal. Chem. 89, 1254–1259 (2016).

    Article  CAS  PubMed Central  Google Scholar 

  170. Cartwright, J. Technology: smartphone science. Nature 531, 669–671 (2016).

    Article  PubMed  Google Scholar 

  171. Warth, B. et al. Metabolizing data in the cloud. Trends Biotechnol. 35, 481–483 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  172. Rinehart, D. et al. Metabolomic data streaming for biology-dependent data acquisition. Nat. Biotechnol. 32, 524–527 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  173. Montenegro-Burke, J. R. et al. Smartphone analytics: mobilizing the lab into the cloud for omic-scale analyses. Anal. Chem. 88, 9753–9758 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  174. Li, D., Heiling, S., Baldwin, I. T. & Gaquerel, E. Illuminating a plant's tissue-specific metabolic diversity using computational metabolomics and information theory. Proc. Natl Acad. Sci. USA 113, E7610–E7618 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  175. Chong, E. Y. et al. Local false discovery rate estimation using feature reliability in LC/MS metabolomics data. Sci. Rep. 5, 17221 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  176. Scheubert, K. et al. Significance estimation for large scale untargeted metabolomics annotations. Preprint at bioRxivhttp://dx.doi.org/10.1101/109389 (2017).

  177. Bandodkar, A. J., Jeerapan, I. & Wang, J. Wearable chemical sensors: present challenges and future prospects. ACS Sensors 1, 464–482 (2016).

    Article  CAS  Google Scholar 

  178. Azzarelli, J. M., Mirica, K. A., Ravnsbæk, J. B. & Swager, T. M. Wireless gas detection with a smartphone via rf communication. Proc. Natl Acad. Sci. USA 111, 18162–18166 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  179. Peng, G. et al. Detection of lung, breast, colorectal, and prostate cancers from exhaled breath using a single array of nanosensors. Br. J. Cancer 103, 542–551 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  180. Clement, R. E. Mass spectral and GC data of drugs, poisons, pesticides, pollutants and their metabolites (Parts 1, 2, 3). Environ. Sci. Pollut. Res. 1, 58–58 (1994).

    Article  Google Scholar 

  181. Hummel, J., Selbig, J., Walther, D. & Kopka, J. in Topics in Current Genetics Vol. 18 75–95 (Springer, 2007).

    Google Scholar 

  182. Horai, H. et al. MassBank: a public repository for sharing mass spectral data for life sciences. J. Mass Spectrom. 45, 703–714 (2010).

    Article  CAS  PubMed  Google Scholar 

  183. Tautenhahn, R. et al. An accelerated workflow for untargeted metabolomics using the METLIN database. Nat. Biotechnol. 30, 826–828 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  184. El-Elimat, T. et al. High-resolution MS, MS/MS, and UV database of fungal secondary metabolites as a dereplication protocol for bioactive natural products. J. Nat. Prod. 76, 1709–1716 (2013).

    Article  CAS  PubMed  Google Scholar 

  185. Dresen, S., Gergov, M., Politi, L., Halter, C. & Weinmann, W. ESI-MS/MS library of 1,253 compounds for application in forensic and clinical toxicology. Anal. Bioanal. Chem. 395, 2521–2526 (2009).

    Article  CAS  PubMed  Google Scholar 

  186. Shahaf, N. et al. The WEIZMASS spectral library for high-confidence metabolite identification. Nat. Commun. 7, 12423 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  187. Sawada, Y. et al. RIKEN tandem mass spectral database (ReSpect) for phytochemicals: a plant-specific MS/MS-based data resource and database. Phytochemistry 82, 38–45 (2012).

    Article  CAS  PubMed  Google Scholar 

  188. Oberacher, H., Weinmann, W. & Dresen, S. Quality evaluation of tandem mass spectral libraries. Anal. Bioanal. Chem. 400, 2641–2648 (2011).

    Article  CAS  PubMed  Google Scholar 

  189. Vinaixa, M. et al. Mass spectral databases for LC/MS and GC/MS-based metabolomics: state of the field and future prospects. TrAC Trends Anal. Chem. 78, 23–35 (2015).

    Article  CAS  Google Scholar 

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Acknowledgements

The authors' work on mass spectrometry and informatics is supported by the US National Institutes of Health (P41 GM103484, 1U01AI124316-01 and R03 CA211211), the US Office of Naval Research (MURI N00014-15-1-2809) and the Sloan Foundation.

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Authors and Affiliations

  1. Center for Microbiome Innovation, University of California, San Diego

    Alexander A. Aksenov, Ricardo da Silva, Rob Knight & Pieter C. Dorrestein

  2. Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego

    Alexander A. Aksenov, Ricardo da Silva & Pieter C. Dorrestein

  3. Department of Pediatrics, University of California, San Diego

    Rob Knight & Pieter C. Dorrestein

  4. Department of Computer Science and Engineering, University of California, San Diego, 92093-0751, California, USA

    Rob Knight

  5. Department of Physics and Chemistry, Núcleo de Pesquisa em Produtos Naturais e Sintéticos, School of Pharmaceutical Sciences of Ribeirão Preto, University of São Paulo, Ribeirão Preto — SP, 14040-903, Brazil

    Norberto P. Lopes

  6. Department of Pharmacology, University of California, San Diego, 92093-0636, California, USA

    Pieter C. Dorrestein

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  1. Alexander A. Aksenov
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Correspondence to Norberto P. Lopes or Pieter C. Dorrestein.

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Aksenov, A., da Silva, R., Knight, R. et al. Global chemical analysis of biology by mass spectrometry. Nat Rev Chem 1, 0054 (2017). https://doi.org/10.1038/s41570-017-0054

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