MetaboAnnotatoR

MetaboAnnotatoR performs automated annotation of isotopologues, adducts, and in-source fragments from LC-MS All-ion fragmentation datasets by combining correlation-based parent-fragment linking with molecular fragment matching.

Key Features:

• Parent-fragment linking: Uses correlation-based methods to reconstruct parent-fragment relationships from AIF LC-MS/MS data.
• Molecular fragment matching: Annotates resulting pseudo-MS/MS spectra by matching against fragment ion libraries.
• Targeted subset annotation: Focuses on a subset of features rather than attempting to annotate the full dataset, reducing computational time and simplifying the annotation process.
• Multi-entity annotation: Identifies isotopologues, adducts, and in-source fragments in untargeted metabolomics and lipidomics data.
• Validation across matrices: Demonstrated in human serum datasets and validated across other biological matrices and different instrument types.

Scientific Applications:

• Untargeted metabolomics: Annotates complex LC-MS datasets containing tens of thousands of features from thousands of metabolites without requiring additional MS/MS experiments.
• Lipidomics: Analyzes lipid-dominated datasets with high precision and recall for features found in highest-rank scores.
• All-ion fragmentation data analysis: Processes AIF LC-MS/MS acquisition data that provides fragmentation data at no additional experimental time cost.
• Cross-platform validation: Applicable to datasets from different instrument types and various biological matrices including human serum.

Methodology:

The workflow combines correlation-based parent-fragment linking with molecular fragment matching to reconstruct parent-fragment relationships and annotate pseudo-MS/MS spectra from AIF LC-MS datasets.