Eclipse: a Python package for alignment of two or more nontargeted LC-MS metabolomics datasets

Area of Science:

• Metabolomics
• Bioinformatics
• Data Analysis

Background:

• Nontargeted LC-MS metabolomics datasets offer rich biological insights but pose analytical challenges.
• Aligning multiple independently processed datasets is crucial for comprehensive analysis.
• Existing software solutions are insufficient for complex alignment tasks.

Purpose of the Study:

• To introduce Eclipse, an open-source Python package for aligning multiple LC-MS metabolomics datasets.
• To provide a robust solution for complex matching scenarios involving more than two datasets.

Main Methods:

• Developed Eclipse, an open-source Python package.
• Implemented a novel graph-based approach for dataset alignment.
• Designed to handle matching scenarios with n > 2 datasets.

Main Results:

• Eclipse effectively aligns multiple nontargeted LC-MS metabolomics datasets.
• The graph-based approach successfully addresses complex matching challenges.
• Provides a valuable tool for researchers working with large metabolomics datasets.

Conclusions:

• Eclipse offers an effective and open-source solution for aligning multiple LC-MS metabolomics datasets.
• The novel graph-based method enhances data integration capabilities in metabolomics research.
• Facilitates more complete and accurate analysis of complex biological samples.