Expanding the Coverage of Metabolic Landscape in Cultivated Rice with Integrated Computational Approaches.

ABSTRACT: Genome-scale metabolomics analysis is increasingly used for pathway and function discovery in the post-genomics era. The great potential offered by developed mass spectrometry (MS)-based technologies has been hindered, since only a small portion of detected metabolites were identifiable so far. To address the critical issue of low identification coverage in metabolomics, we adopted a deep metabolomics analysis strategy by integrating advanced algorithms and expanded reference databases. The experimental reference spectra and in silico reference spectra were adopted to facilitate the structural annotation. To further characterize the structure of metabolites, two approaches were incorporated into our strategy, i.e., structural motif search combined with neutral loss scanning and metabolite

SUBMITTER: Li X 

PROVIDER: S-EPMC9880819 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature