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LipidSig 2.0

Automated lipidomics analysis integrating lipid characteristics and biological context

Description

LipidSig 2.0 is an advanced web-based platform designed for comprehensive lipidomics data analysis, featuring automated lipid recognition and assignment of 29 biologically relevant lipid characteristics. Integrating data from LIPID MAPS, SwissLipids, HMDB, and MetaNetX, the tool supports multiple lipid nomenclatures through Goslin-based parsing and extensive ID cross-referencing. LipidSig 2.0 provides 24 data-processing methods and offers a full analytical workflow including profiling, multi-group differential expression, enrichment analysis, machine learning, and systems-level network analysis using five different algorithms. These functions enable users to explore lipid structural diversity, metabolic pathway alterations, and characteristic-level biological implications. With integrated ID conversion and data-check modules, LipidSig 2.0 streamlines complex lipidomics tasks for users without programming expertise, serving as a useful resource for discovering lipid-based mechanisms, biomarkers and metabolic signatures.

Technical Information

Website:
https://lipidsig.bioinfomics.org/

Covered lipid categories:
FA;GL;GP;ST;PR;SL;PK;SP;

Publications:
10.1093/nar/gkae335, 10.1093/nar/gkab419, 10.1093/bioadv/vbaf047

Documentation:
https://lipidsig.bioinfomics.org/Tutorial/

Download / Web-service:
https://lipidsig.bioinfomics.org/

Source code:
https://github.com/BioinfOMICS/LipidSigR.

Programming languages:
R

Platforms:
Web-based platform, R package

Input formats:
CSV, TSV, XLSX

Output formats:
CSV, PDF,

Interface:
Web platform, GUI, CLI

Licence:
MIT License

Sections

Section 6 — Analysis and Visualisation

Section 7 — Data Integration

Section 7.1 — Lipid Annotations and ID Converters

Section 7.2 — Lipid Ontology Enrichment Analysis

Section 7.3 — Pathway and Network Solutions

Species covered:
LipidSig is not restricted to a specific species.

Data visualisation:
LipidSig 2.0 supports visualization of differential expression results (volcano and lollipop plots), characteristic-based lipid patterns, correlation analyses, enrichment results, machine-learning outputs, and multiple metabolic network views including reaction networks, pathway activity maps, and GATOM subnetworks. All visual outputs are publication-ready and downloadable.

Visualisation export:
Yes

Pathway mapping:
https://lipidsig.bioinfomics.org/Tutorial/?Network

Pathway mapping strategy:
https://lipidsig.bioinfomics.org/Tutorial/?Network

Pathway editing:
No

Links to related genes/proteins:
https://lipidsig.bioinfomics.org/Tutorial/?Network

Up/down regulation on maps:
Yes

Direct projection of identification results:
No