PeakDecoder enables machine learning-based metabolite annotation and accurate profiling in multidimensional mass spectrometry measurements

• Published in: Nature communications Vol. 14; no. 1; pp. 2461 - 18
• Main Authors: Bilbao, Aivett, Munoz, Nathalie, Kim, Joonhoon, Orton, Daniel J., Gao, Yuqian, Poorey, Kunal, Pomraning, Kyle R., Weitz, Karl, Burnet, Meagan, Nicora, Carrie D., Wilton, Rosemarie, Deng, Shuang, Dai, Ziyu, Oksen, Ethan, Gee, Aaron, Fasani, Rick A., Tsalenko, Anya, Tanjore, Deepti, Gardner, James, Smith, Richard D., Michener, Joshua K., Gladden, John M., Baker, Erin S., Petzold, Christopher J., Kim, Young-Mo, Apffel, Alex, Magnuson, Jon K., Burnum-Johnson, Kristin E.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 28.04.2023; Nature Publishing Group; Nature Portfolio
• Subjects: 49/47; 631/114/1305; 631/114/1314; 631/1647/296; 631/45/320; 82/16; 82/58; Algorithms; Annotations; BASIC BIOLOGICAL SCIENCES; Biological activity; Chemical reactions; Chromatography; Chromatography, Liquid - methods; Construction standards; Gas chromatography; Humanities and Social Sciences; Ion Mobility Spectrometry; Ionic mobility; Ions; Learning algorithms; Libraries; Liquid chromatography; Machine learning; Mass spectrometry; Mass Spectrometry - methods; Mass spectroscopy; Metabolites; Metabolomics; Metabolomics - methods; Microorganisms; Mobility; multidisciplinary; Pseudomonas putida; Science; Science (multidisciplinary); Scientific imaging; Sensitivity analysis; Workflow

Multidimensional measurements using state-of-the-art separations and mass spectrometry provide advantages in untargeted metabolomics analyses for studying biological and environmental bio-chemical processes. However, the lack of rapid analytical methods and robust algorithms for these heterogeneous...