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Chemical diversity and antifouling activity of geniculate calcareous algae (Corallinales, Rhodophyta) from Brazil
de S. Oliveira, E. A., de Oliveira, J. A. S., Araújo, P. R., Tâmega, F. T. S., Coutinho, R., & Soares, A. R. (2023). Chemical diversity and antifouling activity of geniculate calcareous algae (Corallinales, Rhodophyta) from Brazil. PeerJ, 11, e15731 (24 pp.). https://doi.org/10.7717/peerj.15731
A modular and expandable ecosystem for metabolomics data annotation in R
Rainer, J., Vicini, A., Salzer, L., Stanstrup, J., Badia, J. M., Neumann, S., … Witting, M. (2022). A modular and expandable ecosystem for metabolomics data annotation in R. Metabolites, 12(2), 173 (13 pp.). https://doi.org/10.3390/metabo12020173
Metabolomic profiling and toxicokinetics modeling to assess the effects of the pharmaceutical diclofenac in the aquatic invertebrate <em>Hyalella azteca</em>
Fu, Q., Scheidegger, A., Laczko, E., & Hollender, J. (2021). Metabolomic profiling and toxicokinetics modeling to assess the effects of the pharmaceutical diclofenac in the aquatic invertebrate Hyalella azteca. Environmental Science and Technology, 55(12), 7920-7929. https://doi.org/10.1021/acs.est.0c07887
Ontology-based metabolomics data integration with quality control
Buendia, P., Bradley, R. M., Taylor, T. J., Schymanski, E. L., Patti, G. J., & Kabuka, M. R. (2019). Ontology-based metabolomics data integration with quality control. Bioanalysis, 11(12), 1139-1154. https://doi.org/10.4155/bio-2018-0303
GC–QTOFMS with a low-energy electron ionization source for advancing isotopologue analysis in <sup>13</sup>C-based metabolic flux analysis
Mairinger, T., Sanderson, J., & Hann, S. (2019). GC–QTOFMS with a low-energy electron ionization source for advancing isotopologue analysis in 13C-based metabolic flux analysis. Analytical and Bioanalytical Chemistry, 411(8), 1495-1502. https://doi.org/10.1007/s00216-019-01590-y
Supporting non-target identification by adding hydrogen deuterium exchange MS/MS capabilities to MetFrag
Ruttkies, C., Schymanski, E. L., Strehmel, N., Hollender, J., Neumann, S., Williams, A. J., & Krauss, M. (2019). Supporting non-target identification by adding hydrogen deuterium exchange MS/MS capabilities to MetFrag. Analytical and Bioanalytical Chemistry, 411(19), 4683-4700. https://doi.org/10.1007/s00216-019-01885-0
The metaRbolomics toolbox in bioconductor and beyond
Stanstrup, J., Broeckling, C., Helmus, R., Hoffmann, N., Mathé, E., Naake, T., … Neumann, S. (2019). The metaRbolomics toolbox in bioconductor and beyond. Metabolites, 9(10), 200 (55 pp.). https://doi.org/10.3390/metabo9100200
Critical assessment of small molecule identification 2016: automated methods
Schymanski, E. L., Ruttkies, C., Krauss, M., Brouard, C., Kind, T., Dührkop, K., … Neumann, S. (2017). Critical assessment of small molecule identification 2016: automated methods. Journal of Cheminformatics, 9, 1-21. https://doi.org/10.1186/s13321-017-0207-1
MetFrag relaunched: incorporating strategies beyond <I>in silico</I> fragmentation
Ruttkies, C., Schymanski, E. L., Wolf, S., Hollender, J., & Neumann, S. (2016). MetFrag relaunched: incorporating strategies beyond in silico fragmentation. Journal of Cheminformatics, 8, 3 (16 pp.). https://doi.org/10.1186/s13321-016-0115-9
Mass spectral databases for LC/MS- and GC/MS-based metabolomics: state of the field and future prospects
Vinaixa, M., Schymanski, E. L., Neumann, S., Navarro, M., Salek, R. M., & Yanes, O. (2016). Mass spectral databases for LC/MS- and GC/MS-based metabolomics: state of the field and future prospects. Trends in Analytical Chemistry, 78, 23-35. https://doi.org/10.1016/j.trac.2015.09.005
CASMI: and the winner is...
Schymanski, E. L., & Neumann, S. (2013). CASMI: and the winner is. Metabolites, 3(2), 412-439. https://doi.org/10.3390/metabo3020412
The Critical Assessment of Small Molecule Identification (CASMI): challenges and solutions
Schymanski, E. L., & Neumann, S. (2013). The Critical Assessment of Small Molecule Identification (CASMI): challenges and solutions. Metabolites, 3(3), 517-538. https://doi.org/10.3390/metabo3030517