New software tools, databases, and resources in metabolomics: updates from 2020
Author:
Publisher
Springer Science and Business Media LLC
Subject
Clinical Biochemistry,Biochemistry,Endocrinology, Diabetes and Metabolism
Link
https://link.springer.com/content/pdf/10.1007/s11306-021-01796-1.pdf
Reference121 articles.
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2. Aksenov, A. A., Laponogov, I., Zhang, Z., Doran, S. L. F., Belluomo, I., Veselkov, D., et al. (2020). Auto-deconvolution and molecular networking of gas chromatography–mass spectrometry data. Nature Biotechnology. https://doi.org/10.1038/s41587-020-0700-3
3. Alexandrov, T. (2020). Spatial metabolomics and imaging mass spectrometry in the age of artificial intelligence. Annual Review of Biomedical Data Science, 3, 1. https://doi.org/10.1146/annurev-biodatasci-011420-031537
4. Aron, A. T., Gentry, E. C., McPhail, K. L., Nothias, L. F., Nothias-Esposito, M., Bouslimani, A., et al. (2020). Reproducible molecular networking of untargeted mass spectrometry data using GNPS. Nature Protocols, 15(6), 1954–1991. https://doi.org/10.1038/s41596-020-0317-5
5. Bararpour, N., Gilardi, F., Carmeli, C., Sidibe, J., Ivanisevic, J., Caputo, T., Augsburger, M., Grabherr, S., Desvergne, B., Guex, N., Bochud, M., Thomas, A. (2020). Visualization and normalization of drift effect across batches in metabolome-wide association studies. biorx, 914051. https://doi.org/10.1101/2020.01.22.914051
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