Metabolomics Navigator Tools, strategies & workflow guidance for metabolomics

Metabolite annotation and identification

LPPtiger 2

LPPtiger 2 supports epilipidomics by predicting modified lipid candidates and their fragmentation patterns, preparing acquisition inclusion lists and annotating modified lipids in LC-MS experiments.

LC-MSLipidomicsDesktop graphical interfacePython packageFree for academic use
Access toolSuggest an update

Overview

LPPtiger 2 supports epilipidomics by predicting modified lipid candidates and their fragmentation patterns, preparing acquisition inclusion lists and annotating modified lipids in LC-MS experiments. Its interface includes a graphical application and Python source workflow.

Also known as: LPPtiger2

What can this tool do?

In-silico spectrum predictionStructure elucidation

Common uses

  • Predict and annotate oxidized or otherwise modified lipid species.
  • Generate in-silico fragmentation and inclusion lists for epilipidomics workflows.

Scientific fit

Primary function

Metabolite annotation and identification

Other functions

Spectral processing and comparison

Analysis types

Lipidomics

Data compatibility

Inputs

Mass spectra, Molecular structures

Outputs

Mass spectra, Compound list or annotations

Input formats

Lipid/modification configurations and manual-specified LC-MS data

Output formats

Predicted modified lipids, fragments, inclusion lists and annotations

Scope and considerations

Designed for

  • LPPtiger 2 supports epilipidomics by predicting modified lipid candidates and their fragmentation patterns, preparing acquisition inclusion lists and annotating modified lipids in LC-MS experiments.

Not designed for

  • General identification of every lipid class or definitive localization of all oxidation and modification sites.

Important requirements

  • A supported desktop executable or documented Python environment, configured lipid/modification rules and LC-MS inputs organized according to the supplied manual and test data.

Considerations

  • Candidate coverage depends on configured modification chemistry and fragmentation rules. Inspect diagnostic evidence and competing assignments before reporting structures.

Access and documentation

Primary accessDownloadDocumentation

README offers AGPLv3 for academic/non-commercial use and requests contact for commercial use. This restricted statement is recorded rather than an unrestricted open-source grant.

License: AGPL-3.0

Evidence and review

Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch3.md.

Core publications

Resource papers, direct updates and tool-specific methods. Selection policy.

  1. Analytical and computational workflow for in-depth analysis of oxidized complex lipids in blood plasma. Nature Communications (2022). doi:10.1038/s41467-022-33225-9

Related tools

BAMSimilar function · BAM generates and ranks structural candidates for suspect metabolites by applying biotransformation rules to known anchor structures.BUDDY / msbuddySimilar function · Generate and rank molecular formula annotations for small-molecule mass-spectrometry queries.CANOPUSSimilar function · A SIRIUS-integrated machine-learning method and webservice for predicting compound classes directly from high-resolution MS/MS data without requiring an exact database structure match.CFM-IDSimilar function · Predict tandem mass spectra, annotate fragments and rank candidate structures for small molecules.

Entry history

Changes to this catalogue entry, not the tool's software release history. Acceptance and editorial review are separate.

  1. 2026-10-27 · First committed entry

    Add 45 source-reviewed Enveda tool entries in third expansion batch

    View change · Entry at this revision

Full GitHub history