MRMkit

MRMkit automates processing of large-scale targeted mass spectrometry (LC-MS) metabolomics data to capture peak shapes and interference patterns, learn retention time offsets by compound class, and produce reproducible metabolite quantification from multimodal ion chromatograms.

Key Features:

• Automated Peak Integration: Captures peak shapes and interference patterns across numerous samples to deliver fully automated and reproducible peak integration results.
• Data Normalization and Quality Metrics: Provides data normalization functions and quality metrics for evaluating data integrity and consistency across extensive datasets.
• Visualizations for Data Quality Evaluation: Supplies visualizations to assess data quality and identify problematic chromatograms or sample-level issues.
• Retention Time Offset Learning: Learns retention time offset patterns based on user-specified compound classes to improve peak picking in multimodal ion chromatograms.
• Recommendations for Peak Picking: Analyzes large-sample data to recommend peak-picking settings for optimized identification and quantification in complex datasets.

Scientific Applications:

• Large-scale targeted metabolomics studies: Automates and standardizes processing from LC-MS raw data through final quantification in studies with large sample cohorts.
• High-throughput LC-MS metabolomics: Enables high-throughput and accurate metabolite quantification by applying automated integration, normalization, and quality assessment across many samples.

Methodology:

Leverages large-sample datasets to capture detailed peak shapes and interference patterns, performs automated peak integration, applies learning algorithms to infer retention time offset patterns by compound class, and analyzes aggregated data to generate peak-picking recommendations.