El-MAVEN

El-MAVEN processes LC-MS, GC-MS, and LC-MS/MS metabolomics data to detect, quantify, and analyze metabolites in large-scale studies.

Key Features:

• Supported Data Types: Supports input from liquid chromatography-mass spectrometry (LC-MS), gas chromatography-mass spectrometry (GC-MS), and tandem mass spectrometry (LC-MS/MS).
• Large-Scale Dataset Handling: Handles large metabolomic datasets, including datasets with over 100 samples.
• Computational Performance: Employs multiprocessing to improve performance on large datasets.
• Machine Learning: Incorporates machine learning techniques to enhance analytical performance and accuracy.
• Memory Management: Implements measures to reduce memory leaks during large-scale processing.
• Data Export: Exports results in multiple formats for downstream analysis and integration into other workflows.
• Integration with Polly™: Integrates with Polly™ to enable flux analysis and integrative-omics studies.
• Processing Workflow: Structures processing into the sequence of loading data, analysis, visualization, and export.

Scientific Applications:

• Metabolomics Data Processing: Enables detection and quantification of metabolites across large LC-MS/GC-MS/LC-MS/MS datasets.
• Metabolic Pathway Analysis: Facilitates identification of metabolic pathway alterations and their implications in health and disease.
• Flux Analysis and Integrative-Omics: Supports flux analysis and integrative-omics workflows via integration with Polly™.

Methodology:

Processing follows the explicit sequence of loading data, analyzing data, visualizing results, and exporting findings.