LPPtiger

LPPtiger identifies oxidized phospholipids (oxPLs) from liquid chromatography–mass spectrometry (LC-MS/MS) datasets to enable untargeted lipidomics investigation of oxPL diversity and function.

Key Features:

• Three integrated algorithms: Integrates three algorithms to predict the oxidized lipidome, generate oxPL spectral libraries, and identify oxPLs from tandem mass spectrometry (MS/MS) data.
• OxPL spectral library generation: Generates libraries of oxidized phospholipid (oxPL) spectra to support MS-based identification.
• Prediction of oxidized lipidome: Predicts oxidized lipidome coverage beyond targeted assays to facilitate untargeted lipidomics.
• Parallel processing: Employs parallel processing to increase processing speed and throughput.
• Multi-scoring workflow: Applies a multi-scoring workflow to improve identification confidence from complex MS/MS datasets.
• High-throughput identification: Enables high-throughput identification of novel oxidized lipids from LC-MS datasets.

Scientific Applications:

• Untargeted lipidomics discovery: Supports discovery of novel oxidized phospholipids beyond previously characterized species.
• Functional studies of oxPLs: Facilitates investigation of oxPL roles in cellular functions and stress responses by identifying diverse oxPL species.
• Spectral library support for MS workflows: Provides oxPL spectral libraries to support MS/MS-based identification and downstream analyses.

Methodology:

Integrates three algorithms to predict the oxidized lipidome, generate oxPL spectral libraries, and identify oxPLs from LC-MS/MS tandem mass spectrometry data using parallel processing and a multi-scoring workflow.