mspire-simulator

mspire-simulator simulates peptide retention times and LC-MS chromatographic profiles to generate realistic synthetic proteomic datasets for testing feature detection (peak picking) and quantitation.

Key Features:

• Realistic Simulation: Generates LC-MS features with realistic mass-to-charge (m/z) and intensity variance and incorporates multiple noise components.
• Retention Time and Peak Intensity Prediction: Uses machine-learning models to predict peptide retention times and peak intensities for simulated chromatographic profiles.
• Customizable Parameters via Genetic Algorithm: Employs a genetic algorithm to fit and customize model parameters to specific experimental datasets.
• Cross-Platform Applicability: Validated on data from three different mass spectrometers, including two fundamentally distinct instrument types.

Scientific Applications:

• Validation of Feature Detection and Quantitation: Provides simulated datasets that replicate experimental conditions to evaluate and benchmark peak picking and quantitation algorithms, including low-abundance peaks.
• Data Overlay and Profile Analysis: Enables overlay of simulated data onto actual datasets to analyze complex chromatographic profiles and assess analytical methods.

Methodology:

Generation of LC-MS features with realistic m/z and intensity variance and noise components; machine-learning models for retention time and peak intensity prediction; genetic algorithm-based parameter fitting to match specific experimental datasets.