PiMP

PiMP processes LC-MS metabolomics data to automate preprocessing, statistical analysis, and evidence-based metabolite annotation for biological interpretation.

Key Features:

• Automated Workflow: Automates preprocessing and analysis of raw LC-MS data to produce reproducible metabolomics results.
• Summary Page: Generates a concise summary of experimental results and associated metadata formatted for publication-style reporting.
• Metabolite Page: Lists identified metabolites with evidence cards that document peak shapes, intensity variations across sample groups, and linked database information for annotation.

Scientific Applications:

• Systems biology: Supports integrative analysis of metabolic profiles to investigate metabolic pathways and network-level changes.
• Clinical diagnostics: Enables comparative analysis of sample groups for biomarker identification and disease-associated metabolic signatures.
• Environmental metabolomics: Analyzes complex LC-MS datasets from environmental samples to characterize chemical exposures and ecosystem metabolites.
• Metabolic pathway and biomarker discovery: Facilitates identification of pathway perturbations and candidate biomarkers through evidence-based metabolite annotation and statistical comparison.

Methodology:

Performs data processing, statistical analysis, and visualization and applies evidence-based metabolite annotation by evaluating peak shapes, intensity changes across sample groups, and matching to existing databases.