peakPantheR

peakPantheR extracts and integrates annotated metabolic features from liquid chromatography-mass spectrometry (LC-MS) profiling datasets to improve feature annotation and quantification for metabolomics studies.

Key Features:

• R package implementation: Implemented as an R package for programmatic analysis of LC-MS profiling data.
• Targeted extraction: Facilitates targeted extraction of annotated metabolic features from LC-MS chromatograms.
• Chromatographic and spectral database integration: Leverages chromatographic and spectral databases to improve annotation accuracy.
• Integration with prior sample information: Utilizes prior knowledge of sample matrix composition to enhance interpretability of extracted features.
• Data quality assessment workflows: Supports workflows for real-time data quality assessment of LC-MS runs.
• Annotated dataset generation: Produces comprehensive annotated metabolic profiles for downstream analyses.
• Addresses untargeted LC-MS limitations: Targets common challenges in untargeted LC-MS feature extraction and metabolite identification.

Scientific Applications:

• Metabolomics profiling: Enables precise identification and quantification of metabolites from LC-MS datasets.
• Biomarker discovery: Generates interpretable annotated datasets for studies investigating disease biomarkers.
• Metabolic pathway analysis: Provides annotated feature sets useful for investigating metabolic pathways.
• Untargeted LC-MS data processing: Enhances annotation and integration in untargeted LC-MS assays to produce reliable metabolic phenotypes.

Methodology:

The package uses chromatographic and spectral databases together with prior sample matrix composition information to perform targeted annotation and extraction of features from complex LC-MS profiling data.