Display options
Cite
Schymanski EL, Ruttkies C, Krauss M, et al. Critical Assessment of Small Molecule Identification 2016: automated methods. J Cheminform. 2017;9(1):22doi: 10.1186/s13321-017-0207-1.
Schymanski, E. L., Ruttkies, C., Krauss, M., Brouard, C., Kind, T., Dührkop, K., Allen, F., Vaniya, A., Verdegem, D., Böcker, S., Rousu, J., Shen, H., Tsugawa, H., Sajed, T., Fiehn, O., Ghesquière, B., & Neumann, S. (2017). Critical Assessment of Small Molecule Identification 2016: automated methods. Journal of cheminformatics, 9(1), 22. https://doi.org/10.1186/s13321-017-0207-1
Schymanski, Emma L, et al. "Critical Assessment of Small Molecule Identification 2016: automated methods." Journal of cheminformatics vol. 9,1 (2017): 22. doi: https://doi.org/10.1186/s13321-017-0207-1
Schymanski EL, Ruttkies C, Krauss M, Brouard C, Kind T, Dührkop K, Allen F, Vaniya A, Verdegem D, Böcker S, Rousu J, Shen H, Tsugawa H, Sajed T, Fiehn O, Ghesquière B, Neumann S. Critical Assessment of Small Molecule Identification 2016: automated methods. J Cheminform. 2017 Mar 27;9(1):22. doi: 10.1186/s13321-017-0207-1. PMID: 29086042; PMCID: PMC5368104.
Copy Download .nbib Format: NLM
Share it on

J Cheminform. 2017 Mar 27;9(1):22. doi: 10.1186/s13321-017-0207-1.

Critical Assessment of Small Molecule Identification 2016: automated methods.

Journal of cheminformatics

Emma L Schymanski, Christoph Ruttkies, Martin Krauss, Céline Brouard, Tobias Kind, Kai Dührkop, Felicity Allen, Arpana Vaniya, Dries Verdegem, Sebastian Böcker, Juho Rousu, Huibin Shen, Hiroshi Tsugawa, Tanvir Sajed, Oliver Fiehn, Bart Ghesquière, Steffen Neumann

Affiliations

  1. Eawag: Swiss Federal Institute for Aquatic Science and Technology, Überlandstrasse 133, 8600, Dübendorf, Switzerland. [email protected].
  2. Department of Stress and Developmental Biology, Leibniz Institute of Plant Biochemistry, Weinberg 3, 06120, Halle, Germany.
  3. Department of Effect-Directed Analysis, UFZ: Helmholtz Centre for Environmental Research, Permoserstrasse 15, 04318, Leipzig, Germany.
  4. Department of Computer Science, Aalto University, Konemiehentie 2, 02150, Espoo, Finland.
  5. Helsinki Institute for Information Technology, Tekniikantie 14, 02150, Espoo, Finland.
  6. West Coast Metabolomics Center and Genome Center, University of California Davis, 451 Health Sciences Drive, Davis, CA, 95616, USA.
  7. Chair of Bioinformatics, Friedrich-Schiller-University, Jena, Ernst-Abbe-Platz 2, 07743, Jena, Germany.
  8. Department of Computing Science, University of Alberta, Edmonton, AB, T6G 2E9, Canada.
  9. Department of Chemistry, University of California Davis, One Shields Avenue, Davis, CA, 95616, USA.
  10. Metabolomics Expertise Center, Vesalius Research Center (VRC), VIB, KU Leuven - University of Leuven, 3000, Louvain, Belgium.
  11. RIKEN Center for Sustainable Resource Science (CSRS), 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan.
  12. Department of Biochemistry, Faculty of Sciences, King Abdulaziz University, Jeddah, Saudi Arabia.

PMID: 29086042 PMCID: PMC5368104 DOI: 10.1186/s13321-017-0207-1

Abstract

BACKGROUND: The fourth round of the Critical Assessment of Small Molecule Identification (CASMI) Contest ( www.casmi-contest.org ) was held in 2016, with two new categories for automated methods. This article covers the 208 challenges in Categories 2 and 3, without and with metadata, from organization, participation, results and post-contest evaluation of CASMI 2016 through to perspectives for future contests and small molecule annotation/identification.

RESULTS: The Input Output Kernel Regression (CSI:IOKR) machine learning approach performed best in "Category 2: Best Automatic Structural Identification-In Silico Fragmentation Only", won by Team Brouard with 41% challenge wins. The winner of "Category 3: Best Automatic Structural Identification-Full Information" was Team Kind (MS-FINDER), with 76% challenge wins. The best methods were able to achieve over 30% Top 1 ranks in Category 2, with all methods ranking the correct candidate in the Top 10 in around 50% of challenges. This success rate rose to 70% Top 1 ranks in Category 3, with candidates in the Top 10 in over 80% of the challenges. The machine learning and chemistry-based approaches are shown to perform in complementary ways.

CONCLUSIONS: The improvement in (semi-)automated fragmentation methods for small molecule identification has been substantial. The achieved high rates of correct candidates in the Top 1 and Top 10, despite large candidate numbers, open up great possibilities for high-throughput annotation of untargeted analysis for "known unknowns". As more high quality training data becomes available, the improvements in machine learning methods will likely continue, but the alternative approaches still provide valuable complementary information. Improved integration of experimental context will also improve identification success further for "real life" annotations. The true "unknown unknowns" remain to be evaluated in future CASMI contests. Graphical abstract .

Keywords: Compound identification; High resolution mass spectrometry; In silico fragmentation; Metabolomics; Structure elucidation

References

  1. J Mass Spectrom. 2013 Mar;48(3):291-8 - PubMed
  2. Rapid Commun Mass Spectrom. 2012 Oct 30;26(20):2461-71 - PubMed
  3. Anal Chem. 2016 Aug 16;88(16):7946-58 - PubMed
  4. Environ Health Perspect. 2016 Jul;124(7):1023-33 - PubMed
  5. Nucleic Acids Res. 2013 Jan;41(Database issue):D801-7 - PubMed
  6. Nat Biotechnol. 2016 Nov 8;34(11):1099-1101 - PubMed
  7. Nucleic Acids Res. 2008 Jan;36(Database issue):D901-6 - PubMed
  8. J Chem Inf Comput Sci. 2003 Mar-Apr;43(2):493-500 - PubMed
  9. Bioinformatics. 2016 Jun 15;32(12 ):i28-i36 - PubMed
  10. Nucleic Acids Res. 2008 Jan;36(Database issue):D344-50 - PubMed
  11. J Cheminform. 2016 Jan 29;8:3 - PubMed
  12. J Cheminform. 2016 Feb 01;8:5 - PubMed
  13. Proc Natl Acad Sci U S A. 2015 Oct 13;112(41):12580-5 - PubMed
  14. Anal Chem. 2012 Apr 3;84(7):3287-95 - PubMed
  15. Metabolites. 2013 May 24;3(2):412-39 - PubMed
  16. Anal Bioanal Chem. 2017 Mar;409(7):1729-1735 - PubMed
  17. J Mass Spectrom. 2010 Jul;45(7):703-14 - PubMed
  18. J Mass Spectrom. 2013 Jan;48(1):89-99 - PubMed
  19. J Cheminform. 2015 Aug 28;7:44 - PubMed
  20. J Cheminform. 2011 Oct 07;3:33 - PubMed
  21. Ther Drug Monit. 2005 Dec;27(6):747-51 - PubMed
  22. Phytochemistry. 2012 Oct;82:38-45 - PubMed
  23. Metabolites. 2013 Jun 25;3(3):517-38 - PubMed
  24. Anal Chem. 2016 Aug 2;88(15):7556-66 - PubMed
  25. Nat Biotechnol. 2016 Aug 9;34(8):828-837 - PubMed
  26. Curr Pharm Des. 2006;12(17):2111-20 - PubMed
  27. Bioinformatics. 2014 Jun 15;30(12):i157-64 - PubMed

Publication Types