MAW

MAW annotates metabolites by assigning chemical structures to features from untargeted liquid chromatography–tandem mass spectrometry (LC-MS², MS²) data to support metabolite identification in metabolomics studies.

Key Features:

• Automated Workflow: Processes tandem MS (MS²) input through automated pre-processing, spectral matching, and computational classification for metabolite annotation.
• Integration of Databases and Tools: Integrates the R package Spectra for database integration and SIRIUS for metabolite annotation within the MAW-R segment, and uses RDKit in the MAW-Py segment for cheminformatics-based candidate selection.
• In Silico Annotation and Molecular Networking: Generates in silico spectra and supports molecular networking to combine spectral evidence with compound databases for improved annotation confidence.
• Chemical Structure Assignment and Similarity Networks: Assigns chemical structures to detected features and constructs chemical structure similarity networks for downstream analysis.
• FAIR Compliance and Reproducibility: Produces outputs aligned with FAIR principles to ensure traceability and reproducibility of annotations.
• Performance Evaluation: Validated in case studies demonstrating improved candidate ranking by integrating spectral databases with annotation tools such as SIRIUS.

Scientific Applications:

• Clinical Metabolomics: Supports biomarker identification in disease-related clinical metabolomics studies.
• Natural Product Discovery: Facilitates exploration of complex biological matrices to discover novel natural products with potential pharmaceutical relevance.

Methodology:

Pre-processing of LC-MS² spectra; spectral and compound database matching using the R package Spectra and SIRIUS; generation of in silico spectra and molecular networking; computational classification, annotation, and candidate prioritization using RDKit within MAW-R and MAW-Py.