The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Dealing with the unknown: Metabolomics and Metabolite Atlases
Abstract
Metabolomics is the comprehensive profiling of the small molecule composition of a biological sample. Since metabolites are often the indirect products of gene expression, this approach is being used to provide new insights into a variety of biological systems (clinical, bioenergy, etc.). A grand challenge for metabolomics is the complexity of the data, which often include many experimental artifacts. This is compounded by the tremendous chemical diversity of metabolites. Identification of each uncharacterized metabolite is in many ways its own puzzle (compared with proteomics, which is based on predictable fragmentation patterns of polypeptides). Therefore, effective data reduction/prioritization strategies are critical for this rapidly developing field. Here we review liquid chromatography electrospray ionization mass spectrometry (LC/MS)-based metabolomics, methods for feature finding/prioritization, approaches for identifying unknown metabolites, and construction of method specific 'Metabolite Atlases'.
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
Cites
Sumner, L. W., Amberg, A., Barrett, D., Beale, M. H., Beger, R., Daykin, C. A., Fan, T. W.-M., Fiehn, O., Goodacre, R., Griffin, J. L., et al. (2007) · Metabolomics · doi:10.1007/s11306-007-0082-2 · Cited by 5,368
Caspi, R., Billington, R., Ferrer, L., Foerster, H., Fulcher, C. A., Keseler, I. M., Kothari, A., Krummenacker, M., Latendresse, M., Mueller, L. A., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv1164 · Cited by 3,395
Kind, T., & Fiehn, O. (2007) · BMC Bioinformatics · doi:10.1186/1471-2105-8-105 · Cited by 1,201
Data processing for mass spectrometry-based metabolomics
Katajamaa, M., & Orešič, M. (2007) · Journal of Chromatography A · doi:10.1016/j.chroma.2007.04.021 · Cited by 616
Draper, J., Enot, D. P., Parker, D., Beckmann, M., Snowdon, S., Lin, W., & Zubair, H. (2009) · BMC Bioinformatics · doi:10.1186/1471-2105-10-227 · Cited by 169
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
Cited by
Zhalnina, K., Louie, K. B., Hao, Z., Mansoori, N., da Rocha, U. N., Shi, S., Cho, H., Karaoz, U., Loqué, D., Bowen, B. P., et al. (2018) · Nature Microbiology · doi:10.1038/s41564-018-0129-3 · Cited by 2,368
Self-citationZhou, B., Xiao, J. F., Tuli, L., & Ressom, H. W. (2011) · Molecular BioSystems · doi:10.1039/c1mb05350g · Cited by 639
Metabolite identification and quantitation in LC-MS/MS-based metabolomics
Xiao, J. F., Zhou, B., & Ressom, H. W. (2012) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2011.08.009 · Cited by 620
Propagating annotations of molecular networks using in silico fragmentation
da Silva, R. R., Wang, M., Nothias, L.-F., van der Hooft, J. J. J., Caraballo-Rodríguez, A. M., Fox, E., Balunas, M. J., Klassen, J. L., Lopes, N. P., & Dorrestein, P. C. (2018) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1006089 · Cited by 363
Kuehnbaum, N. L., & Britz-McKibbin, P. (2013) · Chemical Reviews · doi:10.1021/cr300484s · Cited by 354
Metadata
- Authors
- Bowen, B. P., & Northen, T. R.
- Venue
- Journal of the American Society for Mass Spectrometry
- Publisher
- American Chemical Society (ACS)
- Published
- 2010-09-01
- Volume
- 21
- Issue
- 9
- Pages
- 1471-1476
- ISSN
- 1044-0305 (ISSN-L)
- OpenAlex
- W2075999343
- Cited by
- 198
- References
- 6
- FWCI
- 5.43
- Type
- journal article
- Language
- en
- Open access
- closed
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Dealing with the unknown: Metabolomics and Metabolite Atlases. https://app.cochat.ai/papers/10.1016/j.jasms.2010.04.003
- The paper
- Bowen, B. P., & Northen, T. R. (2010). Dealing with the unknown: Metabolomics and Metabolite Atlases. Journal of the American Society for Mass Spectrometry. https://doi.org/10.1016/j.jasms.2010.04.003
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.
