The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
MS-DIAL: data-independent MS/MS deconvolution for comprehensive metabolome analysis
Open accessAbstract
Data-independent acquisition (DIA) in liquid chromatography (LC) coupled to tandem mass spectrometry (MS/MS) provides comprehensive untargeted acquisition of molecular data. We provide an open-source software pipeline, which we call MS-DIAL, for DIA-based identification and quantification of small molecules by mass spectral deconvolution. For a reversed-phase LC-MS/MS analysis of nine algal strains, MS-DIAL using an enriched LipidBlast library identified 1,023 lipid compounds, highlighting the chemotaxonomic relationships between the algal strains.
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
Smoothing and Differentiation of Data by Simplified Least Squares Procedures.
Savitzky, A., & Golay, M. J. E. (1964) · Analytical Chemistry · doi:10.1021/ac60214a047 · Cited by 21,904
PLS-regression: a basic tool of chemometrics
Wold, S., Sjöström, M., & Eriksson, L. (2001) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/s0169-7439(01)00155-1 · Cited by 10,084
Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease
Wang, Z., Klipfell, E., Bennett, B. J., Koeth, R., Levison, B. S., DuGar, B., Feldstein, A. E., Britt, E. B., Fu, X., Chung, Y.-M., et al. (2011) · Nature · doi:10.1038/nature09922 · Cited by 5,597
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
Self-citationPaDEL‐descriptor: An open source software to calculate molecular descriptors and fingerprints
Yap, C. W. (2011) · Journal of Computational Chemistry · doi:10.1002/jcc.21707 · Cited by 2,962
Lipid extraction by methyl-tert-butyl ether for high-throughput lipidomics
Matyash, V., Liebisch, G., Kurzchalia, T. V., Shevchenko, A., & Schwudke, D. (2008) · Journal of Lipid Research · doi:10.1194/jlr.d700041-jlr200 · Cited by 2,598
MassBank: a public repository for sharing mass spectral data for life sciences
Horai, H., Arita, M., Kanaya, S., Nihei, Y., Ikeda, T., Suwa, K., Ojima, Y., Tanaka, K., Tanaka, S., Aoshima, K., et al. (2010) · Journal of Mass Spectrometry · doi:10.1002/jms.1777 · Cited by 2,529
Self-citationLMSD: LIPID MAPS structure database
Sud, M., Fahy, E., Cotter, D., Brown, A., Dennis, E. A., Glass, C. K., Merrill, A. H., Murphy, R. C., Raetz, C. R. H., Russell, D. W., et al. (2007) · Nucleic Acids Research · doi:10.1093/nar/gkl838 · Cited by 1,375
A revised six-kingdom system of life
CAVALIER-SMITH, T. (1998) · Biological Reviews of the Cambridge Philosophical Society · doi:10.1017/s0006323198005167 · Cited by 1,039
LipidBlast in silico tandem mass spectrometry database for lipid identification
Kind, T., Liu, K.-H., Lee, D. Y., DeFelice, B., Meissen, J. K., & Fiehn, O. (2013) · Nature Methods · doi:10.1038/nmeth.2551 · Cited by 981
Self-citation
Research works printed but not linked: 3
- Fiehn, O., Wohlgemuth, G. & Scholz, M. Proc. Lect. Notes Bioinform 3615, 224–239 (2005).
- Giroud, C., Gerber, A. & Eichenberger, W. Plant Cell Physiol. 29, 587–595 (1988).
- Brand, J.J., Andersen, R.A. & Nobles, D.R. Jr. in Applied Phycology and Biotechnology Second Edition, John Wiley & Sons, Ltd, Oxford, UK 10.1002/9781118567166.ch5 (2013). · DOI
Cited by
Using 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
Gut-microbiota-targeted diets modulate human immune status
Wastyk, H. C., Fragiadakis, G. K., Perelman, D., Dahan, D., Merrill, B. D., Yu, F. B., Topf, M., Gonzalez, C. G., Van Treuren, W., Han, S., et al. (2021) · Cell · doi:10.1016/j.cell.2021.06.019 · Cited by 1,385
Identification of bioactive metabolites using activity metabolomics
Rinschen, M. M., Ivanisevic, J., Giera, M., & Siuzdak, G. (2019) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-019-0108-4 · Cited by 1,008
A Cardiovascular Disease-Linked Gut Microbial Metabolite Acts via Adrenergic Receptors
Nemet, I., Saha, P. P., Gupta, N., Zhu, W., Romano, K. A., Skye, S. M., Cajka, T., Mohan, M. L., Li, L., Wu, Y., et al. (2020) · Cell · doi:10.1016/j.cell.2020.02.016 · Cited by 784
Self-citationGiobbe, G. G., Crowley, C., Luni, C., Campinoti, S., Khedr, M., Kretzschmar, K., De Santis, M. M., Zambaiti, E., Michielin, F., Meran, L., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-13605-4 · Cited by 500
Liu, X., & Locasale, J. W. (2017) · Trends in Biochemical Sciences · doi:10.1016/j.tibs.2017.01.004 · Cited by 453
Topic modeling for untargeted substructure exploration in metabolomics
van der Hooft, J. J. J., Wandy, J., Barrett, M. P., Burgess, K. E. V., & Rogers, S. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1608041113 · Cited by 365
Metabolomics for Plant Improvement: Status and Prospects
Kumar, R., Bohra, A., Pandey, A. K., Pandey, M. K., & Kumar, A. (2017) · Frontiers in Plant Science · doi:10.3389/fpls.2017.01302 · Cited by 328
A cheminformatics approach to characterize metabolomes in stable-isotope-labeled organisms
Tsugawa, H., Nakabayashi, R., Mori, T., Yamada, Y., Takahashi, M., Rai, A., Sugiyama, R., Yamamoto, H., Nakaya, T., Yamazaki, M., et al. (2019) · Nature Methods · doi:10.1038/s41592-019-0358-2 · Cited by 299
Self-citationSREBP-dependent lipidomic reprogramming as a broad-spectrum antiviral target
Yuan, S., Chu, H., Chan, J. F.-W., Ye, Z.-W., Wen, L., Yan, B., Lai, P.-M., Tee, K.-M., Huang, J., Chen, D., et al. (2019) · Nature Communications · doi:10.1038/s41467-018-08015-x · Cited by 253
New software tools, databases, and resources in metabolomics: updates from 2020
Misra, B. B. (2021) · Metabolomics · doi:10.1007/s11306-021-01796-1 · Cited by 227
Wang, X., Yu, N., Qian, Y., Shi, W., Zhang, X., Geng, J., Yu, H., & Wei, S. (2020) · Water Research · doi:10.1016/j.watres.2020.115989 · Cited by 179
Omics approaches for understanding heavy metal responses and tolerance in plants
Jamla, M., Khare, T., Joshi, S., Patil, S., Penna, S., & Kumar, V. (2021) · Current Plant Biology · doi:10.1016/j.cpb.2021.100213 · 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
Perez De Souza, L., Alseekh, S., Brotman, Y., & Fernie, A. R. (2020) · Expert Review of Proteomics · doi:10.1080/14789450.2020.1766975 · Cited by 145
From Omics to Multi-Omics: A Review of Advantages and Tradeoffs
Hayes, C. N., Nakahara, H., Ono, A., Tsuge, M., & Oka, S. (2024) · Genes · doi:10.3390/genes15121551 · Cited by 119
Mass Spectrometry‐Based Untargeted Plant Metabolomics
Perez de Souza, L., Alseekh, S., Naake, T., & Fernie, A. (2019) · Current Protocols in Plant Biology · doi:10.1002/cppb.20100 · Cited by 111
Cannabinoids and terpenes as chemotaxonomic markers in cannabis.
Fischedick J, E. S. (2015) · Natural Products Chemistry & Research · doi:10.4172/2329-6836.1000181 · Cited by 109
Jung, J., Lee, S. M., Lee, M.-J., Ryu, J.-S., Song, J.-H., Lee, J.-E., Kang, G., Kwon, O. S., & Park, J.-Y. (2021) · Brain, Behavior, and Immunity · doi:10.1016/j.bbi.2021.02.003 · Cited by 84
Tsugawa, H., Ikeda, K., Tanaka, W., Senoo, Y., Arita, M., & Arita, M. (2017) · Journal of Cheminformatics · doi:10.1186/s13321-017-0205-3 · Cited by 82
Self-citationJeong, Y., Da Silva, K. M., Iturrospe, E., Fuiji, Y., Boogaerts, T., van Nuijs, A. L. N., Koelmel, J., & Covaci, A. (2022) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2022.129378 · Cited by 76
Large neutral amino acid levels tune perinatal neuronal excitability and survival
Knaus, L. S., Basilico, B., Malzl, D., Gerykova Bujalkova, M., Smogavec, M., Schwarz, L. A., Gorkiewicz, S., Amberg, N., Pauler, F. M., Knittl-Frank, C., et al. (2023) · Cell · doi:10.1016/j.cell.2023.02.037 · Cited by 70
Barupal, D. K., Fan, S., Wancewicz, B., Cajka, T., Sa, M., Showalter, M. R., Baillie, R., Tenenbaum, J. D., Louie, G., Initiative, A. D. N., et al. (2018) · Scientific Data · doi:10.1038/sdata.2018.263 · Cited by 65
Self-citationMeyer, C., Stravs, M. A., & Hollender, J. (2024) · Environmental Science & Technology · doi:10.1021/acs.est.4c00968 · Cited by 64
A palmitoyl transferase chemical–genetic system to map ZDHHC-specific S-acylation
Ocasio, C. A., Baggelaar, M. P., Sipthorp, J., Losada de la Lastra, A., Tavares, M., Volarić, J., Soudy, C., Storck, E. M., Houghton, J. W., Palma-Duran, S. A., et al. (2024) · Nature Biotechnology · doi:10.1038/s41587-023-02030-0 · Cited by 56
Zhang, B., Zhang, Y., Liu, X., Zhao, C., Yin, J., Li, X., Zhang, X., Wang, J., & Wang, S. (2023) · Food Chemistry · doi:10.1016/j.foodchem.2022.133904 · Cited by 47
Johnson, N. R., Yuan, P., Castillo, E., Lopez, T. P., Yue, W., Bond, A., Rivera, B. M., Sullivan, M. C., Hirouchi, M., Giles, K., et al. (2023) · Nature Communications · doi:10.1038/s41467-022-35753-w · Cited by 46
The HDL lipidome is widely remodeled by fast food versus Mediterranean diet in 4 days
Zhu, C., Sawrey-Kubicek, L., Beals, E., Hughes, R. L., Rhodes, C. H., Sacchi, R., & Zivkovic, A. M. (2019) · Metabolomics · doi:10.1007/s11306-019-1579-1 · Cited by 40
Assembly of gut-derived bacterial communities follows “early-bird” resource utilization dynamics
Aranda-Díaz, A., Willis, L., Nguyen, T. H., Ho, P.-Y., Vila, J., Thomsen, T., Chavez, T., Yan, R., Yu, F. B., Neff, N., et al. (2023) · doi:10.1101/2023.01.13.523996 · Cited by 35
Yan, L., Zhou, J., Wang, D., Si, D., Liu, Y., Zhong, L., & Yin, Y. (2018) · Metabolomics · doi:10.1007/s11306-018-1375-3 · Cited by 36
Strategies to unlock lipid production improvement in algae
Anand, V., Kashyap, M., Samadhiya, K., & Kiran, B. (2019) · International Journal of Environmental Science and Technology · doi:10.1007/s13762-018-2098-8 · Cited by 34
Shiver, A. L., Sun, J., Culver, R., Violette, A., Wynter, C., Nieckarz, M., Mattiello, S. P., Sekhon, P. K., Bottacini, F., Friess, L., et al. (2025) · Cell · doi:10.1016/j.cell.2025.02.010 · Cited by 27
Molecular profiling of sponge deflation reveals an ancient relaxant-inflammatory response
Ruperti, F., Becher, I., Stokkermans, A., Wang, L., Marschlich, N., Potel, C., Maus, E., Stein, F., Drotleff, B., Schippers, K. J., et al. (2024) · Current Biology · doi:10.1016/j.cub.2023.12.021 · Cited by 23
Wan, X., Eguchi, A., Qu, Y., Yang, Y., Chang, L., Shan, J., Mori, C., & Hashimoto, K. (2022) · European Archives of Psychiatry and Clinical Neuroscience · doi:10.1007/s00406-022-01437-1 · Cited by 21
Intararuchikul, T., Teerapattarakan, N., Rodsiri, R., Tantisira, M., Wohlgemuth, G., Fiehn, O., & Tansawat, R. (2019) · Biomedical Chromatography · doi:10.1002/bmc.4395 · Cited by 19
Self-citationStreun, G. L., Steuer, A. E., Ebert, L. C., Dobay, A., & Kraemer, T. (2021) · Clinical Chemistry and Laboratory Medicine (CCLM) · doi:10.1515/cclm-2021-0010 · Cited by 18
Ramaprasad, A., Burda, P.-C., Calvani, E., Sait, A. J., Palma-Duran, S. A., Withers-Martinez, C., Hackett, F., Macrae, J., Collinson, L., Gilberger, T. W., et al. (2022) · eLife · doi:10.7554/elife.82207 · Cited by 18
Klont, F., Kremer, D., Gomes Neto, A. W., Berger, S. P., Touw, D. J., Hak, E., Bonner, R., Bakker, S. J. L., & Hopfgartner, G. (2021) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2021.02.008 · Cited by 14
Metadata
- Authors
- Tsugawa, H., Cajka, T., Kind, T., Ma, Y., Higgins, B., Ikeda, K., Kanazawa, M., VanderGheynst, J., Fiehn, O., & Arita, M.
- Venue
- Nature Methods
- Published
- 2015-05-04
- Volume
- 12
- Issue
- 6
- Pages
- 523-526
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- OpenAlex
- W1543068333
- Cited by
- 3,397
- References
- 31
- FWCI
- 30.23
- Type
- journal article
- Language
- en
- Open access
- green
- Licence
- www.springer.com/tdm (tdm)
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.
