The DOI matches its Crossref record.
An accelerated workflow for untargeted metabolomics using the METLIN database
Open accessAbstract
Metabolites, typically recognized as small molecules that are involved in cellular reactions, provide a functional signature of phenotype that is complimentary to the upstream biochemical information obtained from genes, transcripts, and proteins. The high-level of correlation between metabolites and phenotype has created a surge of interest in the field that is reflected in the number of metabolomic publications growing from just a few articles in 1999 to over five thousand in 2011. Although relatively new compared to its genomic and proteomic predecessors, already metabolomics has led to the discovery of biomarkers for disease, fundamental insights into cellular biochemistry, and clues related to disease pathogenesis.1,2
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
Thévenot, E. A., Roux, A., Xu, Y., Ezan, E., & Junot, C. (2015) · Journal of Proteome Research · doi:10.1021/acs.jproteome.5b00354 · Cited by 1,735
Predicting Network Activity from High Throughput Metabolomics
Li, S., Park, Y., Duraisingham, S., Strobel, F. H., Khan, N., Soltow, Q. A., Jones, D. P., & Pulendran, B. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003123 · Cited by 936
METLIN: A Technology Platform for Identifying Knowns and Unknowns
Guijas, C., Montenegro-Burke, J. R., Domingo-Almenara, X., Palermo, A., Warth, B., Hermann, G., Koellensperger, G., Huan, T., Uritboonthai, W., Aisporna, A. E., et al. (2018) · Analytical Chemistry · doi:10.1021/acs.analchem.7b04424 · Cited by 927
Self-citationCurrent approaches and challenges for the metabolite profiling of complex natural extracts
Wolfender, J.-L., Marti, G., Thomas, A., & Bertrand, S. (2015) · Journal of Chromatography A · doi:10.1016/j.chroma.2014.10.091 · Cited by 635
Metabolomics activity screening for identifying metabolites that modulate phenotype
Guijas, C., Montenegro-Burke, J. R., Warth, B., Spilker, M. E., & Siuzdak, G. (2018) · Nature Biotechnology · doi:10.1038/nbt.4101 · Cited by 490
Self-citation
Metadata
- Authors
- Tautenhahn, R., Cho, K., Uritboonthai, W., Zhu, Z., Patti, G. J., & Siuzdak, G.
- Venue
- Nature Biotechnology
- Published
- 2012-09
- Volume
- 30
- Issue
- 9
- Pages
- 826-828
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- OpenAlex
- W2046965677
- Cited by
- 474
- References
- 0
- FWCI
- 16.96
- 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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: An accelerated workflow for untargeted metabolomics using the METLIN database. https://app.cochat.ai/papers/10.1038/nbt.2348
- The paper
- Tautenhahn, R., Cho, K., Uritboonthai, W., Zhu, Z., Patti, G. J., & Siuzdak, G. (2012). An accelerated workflow for untargeted metabolomics using the METLIN database. Nature Biotechnology. https://doi.org/10.1038/nbt.2348
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.
