The DOI matches its Crossref record.
XCMS2: Processing Tandem Mass Spectrometry Data for Metabolite Identification and Structural Characterization
Open accessAbstract
Mass spectrometry based metabolomics represents a new area for bioinformatics technology development. While the computational tools currently available such as XCMS statistically assess and rank LC-MS features, they do not provide information about their structural identity. XCMS(2) is an open source software package which has been developed to automatically search tandem mass spectrometry (MS/MS) data against high quality experimental MS/MS data from known metabolites contained in a reference library (METLIN). Scoring of hits is based on a "shared peak count" method that identifies masses of fragment ions shared between the analytical and reference MS/MS spectra. Another functional component of XCMS(2) is the capability of providing structural information for unknown metabolites, which are not in the METLIN database. This "similarity search" algorithm has been developed to detect possible structural motifs in the unknown metabolite which may produce characteristic fragment ions and neutral losses to related reference compounds contained in METLIN, even if the precursor masses are not the same.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Systematic classification of unknown metabolites using high-resolution fragmentation mass spectra
Dührkop, K., Nothias, L.-F., Fleischauer, M., Reher, R., Ludwig, M., Hoffmann, M. A., Petras, D., Gerwick, W. H., Rousu, J., Dorrestein, P. C., et al. (2021) · Nature Biotechnology · doi:10.1038/s41587-020-0740-8 · Cited by 860
bioinformatics tools for mass spectroscopy-based metabolomic data processing and analysis
Sugimoto, M., Kawakami, M., Robert, M., Soga, T., & Tomita, M. (2012) · Current Bioinformatics · doi:10.2174/157489312799304431 · Cited by 307
Promises and pitfalls of untargeted metabolomics
Gertsman, I., & Barshop, B. A. (2018) · Journal of Inherited Metabolic Disease · doi:10.1007/s10545-017-0130-7 · Cited by 271
MSNovelist: de novo structure generation from mass spectra
Stravs, M. A., Dührkop, K., Böcker, S., & Zamboni, N. (2022) · Nature Methods · doi:10.1038/s41592-022-01486-3 · Cited by 226
Web-based resources for mass-spectrometry-based metabolomics: a user’s guide
Tohge, T., & Fernie, A. R. (2009) · Phytochemistry · doi:10.1016/j.phytochem.2009.02.004 · Cited by 152
A roadmap for the XCMS family of software solutions in metabolomics
Mahieu, N. G., Genenbacher, J. L., & Patti, G. J. (2016) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2015.11.009 · Cited by 151
Tottey, W., Feria-Gervasio, D., Gaci, N., Laillet, B., Pujos, E., Martin, J.-F., Sebedio, J.-L., Sion, B., Jarrige, J.-F., Alric, M., et al. (2017) · Journal of Neurogastroenterology and Motility · doi:10.5056/jnm16042 · Cited by 125
Iron Regulation in Clostridioides difficile
Berges, M., Michel, A.-M., Lassek, C., Nuss, A. M., Beckstette, M., Dersch, P., Riedel, K., Sievers, S., Becher, D., Otto, A., et al. (2018) · Frontiers in Microbiology · doi:10.3389/fmicb.2018.03183 · Cited by 63
Lu, X., Zhang, J.-X., Zhang, L., Wu, D., Tian, J., Yu, L.-J., He, L., Zhong, S., Du, H., Deng, D.-F., et al. (2022) · Science of The Total Environment · doi:10.1016/j.scitotenv.2022.156571 · Cited by 65
ChelomEx: Isotope-Assisted Discovery of Metal Chelates in Complex Media Using High-Resolution LC-MS
Baars, O., Morel, F. M. M., & Perlman, D. H. (2014) · Analytical Chemistry · doi:10.1021/ac503000e · Cited by 57
Ncube, E. N., Steenkamp, P. A., Madala, N. E., & Dubery, I. A. (2016) · Frontiers in Plant Science · doi:10.3389/fpls.2016.01469 · Cited by 8
Metadata
- Authors
- Benton, H. P., Wong, D. M., Trauger, S. A., & Siuzdak, G.
- Venue
- Analytical Chemistry
- Publisher
- American Chemical Society (ACS)
- Published
- 2008-07-16
- Volume
- 80
- Issue
- 16
- Pages
- 6382-6389
- ISSN
- 0003-2700 (ISSN-L)
- 1520-6882
- OpenAlex
- W1995543670
- Cited by
- 266
- References
- 0
- FWCI
- 6.20
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
