Algorithmic Learning for Auto-deconvolution and Molecular Networking of GC-MS Data

ALBARRACÍN ORIO A; Y COLABORADORES; ASKENOV AA; PETRAS D; SMANIA AM

NATURE BIOTECHNOLOGY

NATURE PUBLISHING GROUP

Lugar: Londres; Año: 2021

1087-0156

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.