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

Nat Biotechnol. 2021 Feb;39(2):169-173. doi: 10.1038/s41587-020-0700-3. Epub 2020 Nov 9.

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.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Algorithms*
  • Animals
  • Anura
  • Gas Chromatography-Mass Spectrometry*
  • Humans
  • Metabolomics*