mzRAPP

mzRAPP evaluates the reliability of automated pre-processing workflows for non-targeted metabolomics LC-HRMS data by generating and validating benchmarks from user-supplied information.

Key Features:

• Benchmark generation and validation: mzRAPP creates a benchmark dataset from user-provided inputs and validates it against known standards.
• Performance metrics: The tool computes comprehensive metrics that assess the accuracy and consistency of each pre-processing step.
• Support for multiple pre-processing tools: Supports XCMS, XCMS3, MetaboanalystR 3.0, XCMS-online, MZmine 2, MS-DIAL, OpenMS, and El-MAVEN.
• Implementation in R: Implemented in the R programming language.

Scientific Applications:

• Integration of non-targeted and targeted metabolomics: Provides a benchmark-based assessment enabling evaluation of non-targeted and targeted data on the same LC-HRMS dataset to support biomarker and metabolic pathway discovery.

Methodology:

Users supply experimental information to generate a benchmark; the benchmark is validated against known standards; the validated benchmark is used to evaluate pre-processing steps across the supported tools and to produce detailed performance metrics.