The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
MetaboAnalyst 6.0: towards a unified platform for metabolomics data processing, analysis and interpretation
Open accessAbstract
We introduce MetaboAnalyst version 6.0 as a unified platform for processing, analyzing, and interpreting data from targeted as well as untargeted metabolomics studies using liquid chromatography - mass spectrometry (LC–MS). The two main objectives in developing version 6.0 are to support tandem MS (MS2) data processing and annotation, as well as to support the analysis of data from exposomics studies and related experiments. Key features of MetaboAnalyst 6.0 include: (i) a significantly enhanced Spectra Processing module with support for MS2 data and the asari algorithm; (ii) a MS2 Peak Annotation module based on comprehensive MS2 reference databases with fragment-level annotation; (iii) a new Statistical Analysis module dedicated for handling complex study design with multiple factors or phenotypic descriptors; (iv) a Causal Analysis module for estimating metabolite - phenotype causal relations based on two-sample Mendelian randomization, and (v) a Dose-Response Analysis module for benchmark dose calculations. In addition, we have also improved MetaboAnalyst's visualization functions, updated its compound database and metabolite sets, and significantly expanded its pathway analysis support to around 130 species. MetaboAnalyst 6.0 is freely available at https://www.metaboanalyst.ca.
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Cites
KEGG for taxonomy-based analysis of pathways and genomes
Kanehisa, M., Furumichi, M., Sato, Y., Kawashima, M., & Ishiguro-Watanabe, M. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkac963 · Cited by 5,833
Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases
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Orienting the causal relationship between imprecisely measured traits using GWAS summary data.
Hemani, G., Tilling, K., & Davey Smith, G. (2017) · PLoS Genetics · doi:10.1371/journal.pgen.1007081 · Cited by 2,607
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Foundation models for generalist medical artificial intelligence
Moor, M., Banerjee, O., Abad, Z. S. H., Krumholz, H. M., Leskovec, J., Topol, E. J., & Rajpurkar, P. (2023) · Nature · doi:10.1038/s41586-023-05881-4 · Cited by 2,028 · cited 1×
“For future directions, we will continue to improve metabolome annotations, better integrate with other omics data, and explore new ways to interact with users via generative artificial intelligence technologies ( 70–73 ).”
BackgroundThe NHGRI-EBI GWAS Catalog: knowledgebase and deposition resource
Sollis, E., Mosaku, A., Abid, A., Buniello, A., Cerezo, M., Gil, L., Groza, T., Güneş, O., Hall, P., Hayhurst, J., et al. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkac1010 · Cited by 1,641
Integrative analysis of multimodal mass spectrometry data in MZmine 3
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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
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Developing a Cell Quenching Method to Facilitate Single Cell Mass Spectrometry Metabolomics Studies
Wije Munige, S., Bhusal, D., Peng, Z., Chen, D., & Yang, Z. (2025) · JACS Au · doi:10.1021/jacsau.5c00327 · Cited by 11
APOE genotype-dependent differences in human astrocytic energy metabolism
Budny, V., Bodenmann, C., Zürcher, K. J., Krüger, M., de Leeuw, S. M., Weber, R. Z., Rust, R., Ravotto, L., Ruminot, I., Barros, L. F., et al. (2025) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2025.1603657 · Cited by 6
Antioxidant and longevity inducing properties of coconut water on human dermal fibroblasts
Nakorn, S. N., Dokduang, H., Namwat, N., Klanrit, P., Wangwiwatsin, A., Promraksa, B., Sitthirak, S., Seaban, T., & Loilome, W. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e41010 · Cited by 6
Lim, S. H., Mun, D., Ryu, S., Kang, M.-G., Kang, A., Lee, D. J., Seo, E., Son, S.-H., Kim, S., Jang, K. B., et al. (2025) · Current Research in Food Science · doi:10.1016/j.crfs.2025.101188 · Cited by 5
Metadata
- Authors
- Pang, Z., Lu, Y., Zhou, G., Hui, F., Xu, L., Viau, C., Spigelman, A. F., MacDonald, P. E., Wishart, D. S., Li, S., & Xia, J.
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2024-04-08
- Volume
- 52
- Issue
- W1
- Pages
- W398-W406
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- OpenAlex
- W4394578957
- Cited by
- 2,291
- References
- 8
- FWCI
- 265.83
- Type
- journal article
- Language
- en
- Open access
- gold
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- Pang, Z., Lu, Y., Zhou, G., Hui, F., Xu, L., Viau, C., Spigelman, A. F., MacDonald, P. E., Wishart, D. S., Li, S., & Xia, J. (2024). MetaboAnalyst 6.0: towards a unified platform for metabolomics data processing, analysis and interpretation. Nucleic Acids Research. https://doi.org/10.1093/nar/gkae253
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