282
Views
4
CrossRef citations to date
0
Altmetric
Articles

The Compound Characteristics Comparison (CCC) approach: a tool for improving confidence in natural compound identification

ORCID Icon, , &
Pages 2145-2157 | Received 07 May 2018, Accepted 23 Aug 2018, Published online: 23 Oct 2018

References

  • Arapitsas P, Corte A Della, Gika H, Narduzzi L, Mattivi F, Theodoridis G. 2016. Studying the effect of storage conditions on the metabolite content of red wine using HILIC LC-MS based metabolomics. Food Chem. 197:1331–1340.
  • Arapitsas P, Speri G, Angeli A, Perenzoni D, Mattivi F. 2014. The influence of storage on the “chemical age” of red wines. Metabolomics. 5:816–832.
  • Blaženović I, Kind T, Torbašinović H, Obrenović S, Mehta SS, Tsugawa H, Wermuth T, Schauer N, Jahn M, Biedendieck R, et al. 2017. Comprehensive comparison of in silico MS/MS fragmentation tools of the CASMI contest: database boosting is needed to achieve 93% accuracy. In: J Cheminform. p. 1–12.
  • Brockman SA, E V R, Hegeman AD. 2018. Van Krevelen diagram visualization of high resolution-mass spectrometry metabolomics data with OpenVanKrevelen. Metabolomics. 14:1–5.
  • Creek DJ, Jankevics A, Breitling R, Watson DG, Barrett MP, Burgess KEV. 2011. Identification by retention time prediction. Anal Chem. 83:8703–8710.
  • Della Corte A, Chitarrini G, Di Gangi IM, Masuero D, Soini E, Mattivi F, Vrhovsek U. 2015. A rapid LC–MS/MS method for quantitative profiling of fatty acids, sterols, glycerolipids, glycerophospholipids and sphingolipids in grapes. Talanta. 140:52–61.
  • Dührkop K, Shen H, Meusel M, Rousu J, Böcker S. 2015. Searching molecular structure databases with tandem mass spectra using CSI:fingerID. Proc Natl Acad Sci U S A. 112:12580–12585.
  • Fiehn O. 2002. Metabolomics – the link between genotypes and phenotypes. Plant Mol Biol. 48:155–171.
  • Franceschi P, Mylonas R, Shahaf N, Scholz M, Arapitsas P, Masuero D, Weingart G, Carlin S, Vrhovsek U, Mattivi F, et al. 2014. MetaDB a data processing workflow in untargeted MS-based metabolomics experiments. Front Bioeng Biotechnol. 2:1–12.
  • Friedman J, Hastie T, Tibshirani R. 2010. Regularization paths for generalized linear models via coordinate descent. J Stat Softw. 33(1):1–22.
  • Ghaste M, Narduzzi L, Carlin S, Vrhovsek U, Shulaev V, Mattivi F. 2015. Chemical composition of volatile aroma metabolites and their glycosylated precursors that can uniquely differentiate individual grape cultivars. Food Chem. 188:309–319.
  • Grapov D, Wanichthanarak K, Fiehn O. 2015. MetaMapR: pathway independent metabolomic network analysis incorporating unknowns. Bioinformatics. 31:2757–2760.
  • Guha R. 2007. Chemical Informatics Functionality in R. J Stat Softw [Internet]. 18:1–16. Available from http://www.jstatsoft.org/v18/i05
  • Guijas C, Montenegro-Burke JR, Domingo-Almenara X, Palermo A, Warth B, Hermann G, Koellensperger G, Huan T, Uritboonthai W, Aisporna AE, et al. 2018. METLIN: A Technology Platform for Identifying Knowns and Unknowns. Anal Chem. 90:3156–3164.
  • Kaliszan R. 1992. Quantitative structure –(chromatographic) retention relationships. Anal Chem. 64:619–631.
  • Kind T, Fiehn O. 2006. Metabolomic database annotations via query of elemental compositions: mass accuracy is insufficient even at less than 1 ppm. BMC Bioinformatics. 7:234.
  • Kind T, Fiehn O. 2007. Seven Golden Rules for heuristic filtering of molecular formulas obtained by accurate mass spectrometry. BMC Bioinformatics. 8:105.
  • Knolhoff AM, Callahan JH, Croley TR. 2014. Mass accuracy and isotopic abundance measurements for HR-MS instrumentation: capabilities for non-targeted analyses. J Am Soc Mass Spectrom. 25:1285–1294.
  • Kraemer N, Boulesteix AL, Tutz G. 2008. Penalized Partial Least Squares with Applications to B-Spline Transformations and Functional Data. Chemom Intell Lab Syst. 94:60–69.
  • Lindgren F, Hansen B, Karcher W. 1996. Model validation by permutation tests : applications to variable selection. J Chemom. 10:521–532.
  • Mattivi F, Arapitsas P, Perenzoni D, Guella G. 2015. Influence of storage conditions on the composition of red wines. Advances in Wine Chemistry. Chapter. 3:29–49.
  • Mattivi F, Vrhovsek U, Malacarne G, Masuero D, Zulini L, Stefanini M, Mose C, Velasco R, Guella G. 2011. Profiling of resveratrol oligomers, important stress metabolites, accumulating in the leaves of hybrid Vitis vinifera (Merzling ?? Teroldego) genotypes infected with Plasmopara viticola. J Agric Food Chem. 59:5364–5375.
  • Meusel M, Hufsky F, Panter F, Krug D, Mu R, Bo S. 2016. Predicting the presence of uncommon elements in unknown biomolecules from isotope patterns. Anal Chem. 88:7556–7566.
  • Narduzzi L, Stanstrup J, Mattivi F. 2015. Comparing wild American grapes with Vitis vinifera : A metabolomics study of grape composition. J Agric Food Chem. 63:6823–6834.
  • R W K, Kujawinski EB, Zang X, Green-Church KB, Jones B, Freitas MA, Hatcher PG. 2001. Studies of the structure of humic substances by electrospray ionization coupled to a quadrupole-time of flight (QQ-TOF) mass spectrometer. SpecPubl - RSocChem. 273:95.
  • Roullier-Gall C, Witting M, Gougeon RD, Schmitt-Kopplin P. 2014. High precision mass measurements for wine metabolomics. Front Chem. 2:1–9.
  • Ruttkies C, Schymanski EL, Wolf S, Hollender J, Neumann S. 2016. MetFrag relaunched: incorporating strategies beyond in silico fragmentation. J Cheminform. 8:3–13.
  • Salek RM, Steinbeck C, Viant MR, Goodacre R, Dunn WB. 2013. The role of reporting standards for metabolite annotation and identification in metabolomic studies. Gigascience. 2:2–4.
  • Shahaf N, Franceschi P, Arapitsas P, Rogachev I, Vrhovsek U, Wehrens R. 2013. Constructing a mass measurement error surface to improve automatic annotations in liquid chromatography/mass spectrometry based metabolomics. Rapid Commun Mass Spectrom. [Internet]. [cited 2014 Dec 15] 27:2425–2431. Available from http://www.ncbi.nlm.nih.gov/pubmed/24097399
  • Shahaf N, Rogachev I, Heinig U, Meir S, Malitsky S, Battat M, Wyner H, Zheng S, Wehrens R, Aharoni A. 2016. The WEIZMASS spectral library for high-confidence metabolite identification. Nat Commun. 7:12423.
  • Shen H, Dührkop K, Böcker S, Rousu J. 2014. Metabolite identification through multiple kernel learning on fragmentation trees. Bioinformatics. 30:157–164.
  • Sleno L. 2012. The use of mass defect in modern mass spectrometry. J Mass Spectrom. 47:226–236.
  • Sonni F, Moore EG, Clark AC, Chinnici F, Riponi C, Scollary GR. 2011. Impact of glutathione on the formation of methylmethine-and carboxymethine-bridged (+)-catechin dimers in a model wine system. J Agric Food Chem. 59:7410–7418.
  • Stagliano MC, DeKeyser J, Omiecinski CJ, Jones AD. 2010. Bioassay-directed fractionation for discovery of bioactive neutral lipids guided by relative mass defect filtering and multiplexed collision-induced dissociation Michael. Rapid Commun Mass Spectrom. 24:3578–3584.
  • Stanstrup J, Gerlich M, Dragsted LO, Neumann S. 2013. Metabolite profiling and beyond: approaches for the rapid processing and annotation of human blood serum mass spectrometry data. Anal Bioanal Chem. 405:5037–5048.
  • Sumner LW, Amberg A, Barrett D, Beale MH, Beger R, Daykin CA, Tw-M F, Fiehn O, Goodacre R, Jl G, et al. 2007. Proposed minimum reporting standards for chemical analysis. Metabolomics. 3:211–221.
  • Theodoridis G, Gika H, Franceschi P, Caputi L, Arapitsas P, Scholz M, Masuero D, Wehrens R, Vrhovsek U, Mattivi F. 2012. LC-MS based global metabolite profiling of grapes: solvent extraction protocol optimisation. Metabolomics. 8:175–185.
  • Thurman EM, Ferrer I. 2010. The isotopic mass defect: A tool for limiting molecular formulas by accurate mass. Anal Bioanal Chem. 397:2807–2816.
  • Van Krevelen DW. 1950. Graphical-statistical method for the study of structure and reaction processes of coal. Fuel. 24:269–284.
  • Wang Y, Backman T, Horan K, Girke T. 2016. Mismatch tolerant maximum common substructure searching. R package. R environment.
  • Waterhouse AL, Sacks GL, Jeffery DW. 2017. Understanding wine chemistry. Chichester (United Kingdom): John Wiley & Sons, Ltd.
  • Wehrens R, Weingart G, Mattivi F. 2014. metaMS: an open-source pipeline for GC-MS-based untargeted metabolomics. J Chromatogr B Analyt Technol Biomed Life Sci. 966:109–116.
  • Xu Q-S, Liang Y-Z. 2001. Monte Carlo cross validation. Chemom Intell Lab Syst. 56:1–11.
  • Zhang H, Zhang D, Ray K, Zhu M. 2009. Mass defect filter technique and its applications to drug metabolite identification by high-resolution mass spectrometry. J Mass Spectrom. 44:999–1016.
  • Zhang H, Zhu M, Ray KL, Ma L, Zhang D. 2008. Mass defect profiles of biological matrices and the general applicability of mass defect filtering for metabolite detection. Rapid Commun Mass Spectrom. 22:2082–2088.
  • Zhu M, Ma L, Zhang D, Ray K, Zhao W, Humphreys WG, Skiles G, Sanders M, Zhang H. 2006. Detection and characterization of metabolites in biological matrices using mass defect filtering of liquid chromatography/high resolution mass spectrometry data. Pharmacology. 34:1722–1733.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.