Auto-deconvolution and molecular networking of gas chromatography-mass spectrometry data.

ABSTRACT: 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.

SUBMITTER: Aksenov AA 

PROVIDER: S-EPMC7971188 | biostudies-literature |

REPOSITORIES: biostudies-literature