Mass spectra-based framework for automated structural elucidation of metabolome data to explore phytochemical diversity.
A novel framework for automated elucidation of metabolite structures in liquid chromatography-mass spectrometer metabolome data was constructed by integrating databases. High-resolution tandem mass spectra data automatically acquired from each metabolite signal were used for database searches. Three distinct databases, KNApSAcK, ReSpect, and the PRIMe standard compound database, were employed for the structural elucidation. The outputs were retrieved using the CAS metabolite identifier for identification and putative annotation. A simple metabolite ontology system was also introduced to attain putative characterization of the metabolite signals. The automated method was applied for the metabolome data sets obtained from the rosette leaves of 20 Arabidopsis accessions. Phenotypic variations in novel Arabidopsis metabolites among these accessions could be investigated using this method.
RIKEN Plant Science Center Yokohama, Japan.
Fumio
F
Matsuda
Ryo
R
Nakabayashi
Yuji
Y
Sawada
Makoto
M
Suzuki
Masami Y
MY
Hirai
Shigehiko
S
Kanaya
Kazuki
K
Saito