LPPtiger

Identification of oxidized lipids from DDA datasets

Description

LPPtiger is an open source software for the high-throughput identification of lipids from DDA data acquired by LC-MS experiments. LPPtiger combines three unique algorithms to predict oxidized lipidome, generate oxPL spectra libraries, and identify oxPLs from tandem MS data using parallel processing and a multi-scoring identification workflow. LPPtiger can evaluate candidate structures using its unique five-criteria scoring system including isotope score to check isotopic pattern, rank score for bottom-up identification, spectra match score to check the spectra similarity to predicted spectra, fingerprint score to check low abundant signals, and signal to noise score to check the specificity of candidate structure. LPPtiger provide a solution to generate customized oxidized lipids library predicted from unmodified lipids and use it for precise identification of oxidized lipids.

Technical Information

License:

GPL (academic) / commercial license (commercial use)

GUI:

Yes

CLI:

Desktop client:

Yes

Web platform:

Input formats:

mzML

Output formats:

HTML,

Excel

Platforms:

Linux,

Windows

Programming languages:

Python

Source code repository:

Yes

Download / Web-service link:

Yes

Documentation and user guide:

Yes

Training datasets:

Yes

Publications:

PMID:29123162

Tasks

4.5) Identification of oxidized lipids

Lipid coverage:

GPL

Spectra matching:

Yes

Decision rules:

Yes

Requirements:

Whitelist of fatty acid residues and lipid classes specific head group fragments (provided + customizable)

Algorithms:

Generate oxidized lipid library predicted from unmodified lipids. Use the information of predicted oxidized library for identification. Evaluate candidate structure using multiple criteria including: isotopic pattern, rule-based assignment and ranking of MS2 signals, spectra matching to predicted spectra, fingerprint fragments check, and overall peak specificity to the candidate structure.

Scores:

Signal to noise score,

Fingerprint score,

Spectra match score,

Rank score,

Isotope score

Spectra annotation:

Yes

Other features:

In silico oxidation to generate theoretical library of predicted oxidized lipids; export predicted oxidized lipids as structure library in SDF format; export predicted spectra pattern that generated by in silico fragmentation as spectra library in MSP format; Batch processing mode