Published November 9, 2020
| Version v1
Journal article
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Auto-deconvolution and molecular networking of gas chromatography–mass spectrometry data
- 1. Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA; Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California,San Diego, La Jolla, CA, USA
- 2. Department of Surgery and Cancer, Imperial College London, South Kensington Campus, London, UK
- 3. Center of Plant Systems Biology and Biotechnology, Plovdiv, Bulgaria; Max‐Planck‐Institut für Molekulare Pflanzenphysiologie, Potsdam‐Golm, Germany
Description
We engineered a machine learning approach, MSHub, to enable auto-deconvolution of gas chromatography–mass spectrometry (GC–MS) data. We then designed workflows to enable the community to store, process, share, annotate, compare and perform molecular networking of GC–MS data within the Global Natural Product Social (GNPS) Molecular Networking analysis platform. MSHub/GNPS performs auto-deconvolution of compound fragmentation patterns via unsupervised non-negative matrix factorization and quantifies the reproducibility of fragmentation patterns across samples.
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