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Updated: Oct 9, 2025

Shotgun Lipidomics of Rodent Tissues

Shotgun Lipidomics of Rodent Tissues

Published on: November 18, 2022

LINT-Web: A Web-Based Lipidomic Data Mining Tool Using Intra-Omic Integrative Correlation Strategy.

Fengsheng Li¹, Jia Song², Yingkun Zhang³

¹Shanghai Key Laboratory of Metabolic Remodeling and Health, Institute of Metabolism and Integrative Biology, Fudan University, Shanghai, 200438, China.

|December 20, 2021

Summary

Lipidomics data analysis is challenging due to limited tools. This study introduces LINT-web, a novel tool for intra-omic analysis to predict lipid functions, improving data mining for researchers.

Keywords:

lipidomics online tools systems biology transcriptomics

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Last Updated: Oct 9, 2025

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Quantitative Analysis of the Cellular Lipidome of Saccharomyces Cerevisiae Using Liquid Chromatography Coupled with Tandem Mass Spectrometry

Quantitative Analysis of the Cellular Lipidome of Saccharomyces Cerevisiae Using Liquid Chromatography Coupled with Tandem Mass Spectrometry

Published on: March 8, 2020

Area of Science:

Biochemistry
Bioinformatics
Systems Biology

Background:

Lipidomics profiles lipidome alterations in biological systems.
Lipidomic data is high-dimensional, requiring advanced bioinformatic tools for analysis.
Current tools for lipidomic data processing and pathway analysis are limited and often rely on potentially biased public databases.

Purpose of the Study:

To address the limitations in lipidomic data analysis and interpretation.
To introduce an intra-omic integrative correlation strategy for lipidomic data mining.
To develop a user-friendly web-based tool for processing lipidomic datasets and predicting lipid biological functions.

Main Methods:

Developed LINT-web, an interactive web-based tool for intra-omic analysis.
Employed an intra-omic integrative correlation strategy for lipidomic data mining.
Validated LINT-web using two biological systems.

Main Results:

LINT-web simplifies lipidomic data processing and enhances the prediction of lipid biological functions.
The intra-omic strategy allows for unscrambling and predicting lipid functions from correlated genomic ontological results.
The tool was successfully validated on two biological systems, demonstrating its practical utility.

Conclusions:

LINT-web offers a simplified and improved experience for lipidomic data processing.
The tool empowers researchers, even those without advanced statistical expertise, to predict lipid biological functions.
This approach overcomes the limitations of public databases for lipid pathway analysis, reducing bias.