1 research outputs found
LipidOA: A Machine-Learning and Prior-Knowledge-Based Tool for Structural Annotation of Glycerophospholipids
The Paternò–Büchi (PB) reaction
is a carbon–carbon
double bond (CC)-specific derivatization reaction that can
be used to pinpoint the location(s) of CC(s) in unsaturated
lipids and quantitate the location of isomers when coupled with tandem
mass spectrometry (MS/MS). As the data of PB-MS/MS are increasingly
generated, the establishment of a corresponding data analysis tool
is highly needed. Herein, LipidOA, a machine-learning and prior-knowledge-based
data analysis tool, is developed to analyze PB-MS/MS data generated
by liquid chromatography–mass spectrometry workflows. LipidOA
consists of four key functional modules to realize an annotation of
glycerophospholipid (GPL) structures at the fatty acyl-specific CC
location level. These include (1) data preprocessing, (2) picking
CC diagnostic ions, (3) de novo annotation,
and (4) result ranking. Importantly, in the result-ranking module,
the reliability of structural annotation is sorted via the use of
a machine learning classifier and comparison to the total fatty acid
database generated from the same sample. LipidOA is trained and validated
by four PB-MS/MS data sets acquired using different PB reagents on
mass spectrometers of different resolutions and of different biological
samples. Overall, LipidOA provides high precision (higher than 0.9)
and a wide coverage for structural annotations of GPLs. These results
demonstrate that LipidOA can be used as a robust and flexible tool
for annotating PB-MS/MS data collected under different experimental
conditions using different lipidomic workflows