El-MAVEN: A Fast, Robust, and User-Friendly Mass Spectrometry Data Processing Engine for Metabolomics.

Area of Science:

• Metabolomics
• Computational Biology
• Bioinformatics

Background:

• Metabolomic data analysis is crucial for understanding biological processes and disease.
• Existing open-source tools for mass spectrometry data analysis (LC-MS, GC-MS, LC-MS/MS) lack interactivity and efficiency for large sample sizes.
• There is a need for advanced, user-friendly platforms to handle large-scale metabolomic datasets.

Purpose of the Study:

• To introduce El-MAVEN, an open-source analysis platform designed for efficient and interactive analysis of large metabolomic datasets.
• To provide a user-friendly workflow for processing and analyzing metabolomic data, particularly for datasets exceeding 100 samples.
• To enhance the speed and interactivity of metabolomic data analysis through advanced computational techniques.

Main Methods:

• El-MAVEN extends the MAVEN platform, incorporating multiprocessing, machine learning, and memory leak reduction.
• The platform features a streamlined four-step workflow: data loading, analysis, results generation, and export.
• Integration with Polly™, a cloud-based platform, enables advanced flux and integrative-omics analysis.

Main Results:

• El-MAVEN provides fast and interactive analysis capabilities for large metabolomic datasets.
• The platform ensures a seamless user experience, even with complex and extensive data.
• Results are exportable in multiple formats for further analysis on different platforms.

Conclusions:

• El-MAVEN significantly accelerates the process of gaining insights from raw metabolomic data.
• It offers a powerful and efficient solution for researchers analyzing large-scale metabolomic studies.
• The platform enhances the accessibility and utility of metabolomic data analysis in biological and pathophysiological research.