The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
XCMS Online: A Web-Based Platform to Process Untargeted Metabolomic Data
Open accessAbstract
Recently, interest in untargeted metabolomics has become prevalent in the general scientific community among an increasing number of investigators. The majority of these investigators, however, do not have the bioinformatic expertise that has been required to process metabolomic data by using command-line driven software programs. Here we introduce a novel platform to process untargeted metabolomic data that uses an intuitive graphical interface and does not require installation or technical expertise. This platform, called XCMS Online, is a web-based version of the widely used XCMS software that allows users to easily upload and process liquid chromatography/mass spectrometry data with only a few mouse clicks. XCMS Online provides a solution for the complete untargeted metabolomic workflow including feature detection, retention time correction, alignment, annotation, statistical analysis, and data visualization. Results can be browsed online in an interactive, customizable table showing statistics, chromatograms, and putative METLIN identities for each metabolite. Additionally, all results and images can be downloaded as zip files for offline analysis and publication. XCMS Online is available at https://xcmsonline.scripps.edu.
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
Monoacylglycerol lipase regulates a fatty acid network that promotes cancer pathogenesis
Nomura, D. K., Long, J. Z., Niessen, S., Hoover, H. S., Ng, S.-W., & Cravatt, B. F. (2010) · Cell · doi:10.1016/j.cell.2009.11.027 · Cited by 1,002
Cited by
Metabolomics: beyond biomarkers and towards mechanisms
Johnson, C. H., Ivanisevic, J., & Siuzdak, G. (2016) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm.2016.25 · Cited by 2,829
Self-citationUsing MetaboAnalyst 3.0 for Comprehensive Metabolomics Data Analysis
Xia, J., & Wishart, D. S. (2016) · Current Protocols in Bioinformatics · doi:10.1002/cpbi.11 · Cited by 1,544
Microbiome-derived inosine modulates response to checkpoint inhibitor immunotherapy
Mager, L. F., Burkhard, R., Pett, N., Cooke, N. C. A., Brown, K., Ramay, H., Paik, S., Stagg, J., Groves, R. A., Gallo, M., et al. (2020) · Science · doi:10.1126/science.abc3421 · Cited by 1,407
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-citationZhu, Z.-J., Schultz, A. W., Wang, J., Johnson, C. H., Yannone, S. M., Patti, G. J., & Siuzdak, G. (2013) · Nature Protocols · doi:10.1038/nprot.2013.004 · Cited by 477
Self-citationIndole is an essential herbivore-induced volatile priming signal in maize
Erb, M., Veyrat, N., Robert, C. A. M., Xu, H., Frey, M., Ton, J., & Turlings, T. C. J. (2015) · Nature Communications · doi:10.1038/ncomms7273 · Cited by 467
Metabolite Measurement: Pitfalls to Avoid and Practices to Follow
Lu, W., Su, X., Klein, M. S., Lewis, I. A., Fiehn, O., & Rabinowitz, J. D. (2017) · Annual Review of Biochemistry · doi:10.1146/annurev-biochem-061516-044952 · Cited by 462
The microbiota regulate neuronal function and fear extinction learning
Chu, C., Murdock, M. H., Jing, D., Won, T. H., Chung, H., Kressel, A. M., Tsaava, T., Addorisio, M. E., Putzel, G. G., Zhou, L., et al. (2019) · Nature · doi:10.1038/s41586-019-1644-y · Cited by 421
Koelmel, J. P., Kroeger, N. M., Ulmer, C. Z., Bowden, J. A., Patterson, R. E., Cochran, J. A., Beecher, C. W. W., Garrett, T. J., & Yost, R. A. (2017) · BMC Bioinformatics · doi:10.1186/s12859-017-1744-3 · Cited by 346
Mass spectrometry searches using MASST
Wang, M., Jarmusch, A. K., Vargas, F., Aksenov, A. A., Gauglitz, J. M., Weldon, K., Petras, D., da Silva, R., Quinn, R., Melnik, A. V., et al. (2020) · Nature Biotechnology · doi:10.1038/s41587-019-0375-9 · Cited by 281
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
Metabolomics: A Way Forward for Crop Improvement
Razzaq, A., Sadia, B., Raza, A., Khalid Hameed, M., & Saleem, F. (2019) · Metabolites · doi:10.3390/metabo9120303 · Cited by 253
Applications of metabolomics to study cancer metabolism
Kaushik, A. K., & DeBerardinis, R. J. (2018) · Biochimica et Biophysica Acta (BBA) - Reviews on Cancer · doi:10.1016/j.bbcan.2018.04.009 · Cited by 179
Spontaneous DNA damage to the nuclear genome promotes senescence, redox imbalance and aging
Robinson, A. R., Yousefzadeh, M. J., Rozgaja, T. A., Wang, J., Li, X., Tilstra, J. S., Feldman, C. H., Gregg, S. Q., Johnson, C. H., Skoda, E. M., et al. (2018) · Redox Biology · doi:10.1016/j.redox.2018.04.007 · Cited by 155
“LC/ MS data were processed using XCMS Online [41].”
MethodSelf-citationRevealing disease-associated pathways by network integration of untargeted metabolomics
Pirhaji, L., Milani, P., Leidl, M., Curran, T., Avila-Pacheco, J., Clish, C. B., White, F. M., Saghatelian, A., & Fraenkel, E. (2016) · Nature Methods · doi:10.1038/nmeth.3940 · Cited by 167
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
Self-citationCannabinoids Block Cellular Entry of SARS-CoV-2 and the Emerging Variants
van Breemen, R. B., Muchiri, R. N., Bates, T. A., Weinstein, J. B., Leier, H. C., Farley, S., & Tafesse, F. G. (2022) · Journal of Natural Products · doi:10.1021/acs.jnatprod.1c00946 · Cited by 135
Multi-Omics Approaches to Define Calcific Aortic Valve Disease Pathogenesis
Blaser, M. C., Kraler, S., Lüscher, T. F., & Aikawa, E. (2021) · Circulation Research · doi:10.1161/circresaha.120.317979 · Cited by 109
Current and future perspectives of functional metabolomics in disease studies–A review
Yan, M., & Xu, G. (2018) · Analytica Chimica Acta · doi:10.1016/j.aca.2018.04.006 · Cited by 105
Network Medicine in Pathobiology
Lee, L. Y.-H., & Loscalzo, J. (2019) · The American Journal of Pathology · doi:10.1016/j.ajpath.2019.03.009 · Cited by 95
Harshman, S. W., Pitsch, R. L., Smith, Z. K., O’Connor, M. L., Geier, B. A., Qualley, A. V., Schaeublin, N. M., Fischer, M. V., Eckerle, J. J., Strang, A. J., et al. (2018) · PLOS ONE · doi:10.1371/journal.pone.0203133 · Cited by 51
Xicota, L., Ichou, F., Lejeune, F.-X., Colsch, B., Tenenhaus, A., Leroy, I., Fontaine, G., Lhomme, M., Bertin, H., Habert, M.-O., et al. (2019) · EBioMedicine · doi:10.1016/j.ebiom.2019.08.051 · Cited by 51
Identification of lipidomic profiles associated with drug-resistant prostate cancer cells
Ingram, L. M., Finnerty, M. C., Mansoura, M., Chou, C.-W., & Cummings, B. S. (2021) · Lipids in Health and Disease · doi:10.1186/s12944-021-01437-5 · Cited by 42
Daly, G., Ghini, V., Adessi, A., Fondi, M., Buchan, A., & Viti, C. (2022) · FEMS Microbiology Reviews · doi:10.1093/femsre/fuac020 · Cited by 30
Bruegger, J. J., Smith, B. C., Wynia-Smith, S. L., & Marletta, M. A. (2018) · Journal of Biological Chemistry · doi:10.1074/jbc.m117.817700 · Cited by 27
DeltaMS: a tool to track isotopologues in GC- and LC-MS data
Baumeister, T. U. H., Ueberschaar, N., Schmidt-Heck, W., Mohr, J. F., Deicke, M., Wichard, T., Guthke, R., & Pohnert, G. (2018) · Metabolomics · doi:10.1007/s11306-018-1336-x · Cited by 25
Asteggiano, A., Occhipinti, A., Capuzzo, A., Mecarelli, E., Aigotti, R., & Medana, C. (2021) · Molecules · doi:10.3390/molecules26051447 · Cited by 21
Metabolomic stability of exercise-induced sweat
Harshman, S. W., Pitsch, R. L., Schaeublin, N. M., Smith, Z. K., Strayer, K. E., Phelps, M. S., Qualley, A. V., Cowan, D. W., Rose, S. D., O'Connor, M. L., et al. (2019) · Journal of Chromatography B · doi:10.1016/j.jchromb.2019.121763 · Cited by 19
Metadata
- Authors
- Tautenhahn, R., Patti, G. J., Rinehart, D., & Siuzdak, G.
- Venue
- Analytical Chemistry
- Publisher
- American Chemical Society (ACS)
- Published
- 2012-06-05
- Volume
- 84
- Issue
- 11
- Pages
- 5035-5039
- ISSN
- 0003-2700 (ISSN-L)
- 1520-6882
- OpenAlex
- W2080046774
- Cited by
- 1,319
- References
- 1
- FWCI
- 23.00
- 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.