SMITER

SMITER simulates liquid-chromatography-coupled tandem mass spectrometry (LC-MS/MS) experiments from chemical formulas to produce synthetic mzML output for testing and benchmarking computational mass-spectrometry analyses.

Key Features:

• Modular design: Modular architecture enables integration and replacement of noise and fragmentation models.
• Chemical-formula-based simulation: Uses chemical formulas as the basis to model any biomolecule amenable to mass spectrometry.
• Noise and fragmentation models: Includes a default noise model and multiple fragmentation methods, comprising several peptide fragmentation strategies, two nucleoside fragmentation models, and one lipid fragmentation model.
• mzML output: Generates synthetic mzML files representing simulated LC-MS/MS runs.
• Extensibility via Python: Integrates with the Python ecosystem to add modules such as retention time (RT) prediction.

Scientific Applications:

• Generation of gold-standard datasets: Produces defined LC-MS/MS datasets with known ground truth for algorithm development, testing, and validation.
• Evaluation of analytical challenges: Simulates co-elution and co-fragmentation scenarios to assess impacts on detection and quantification.
• Experimental planning and optimization: Enables assessment of potential experimental outcomes to guide LC-MS/MS experimental design.

Methodology:

Simulations are generated from chemical formulas using a modular framework that applies a default noise model and selectable fragmentation models for peptides, nucleosides, and lipids, with outputs written to mzML and optional integration of additional Python-based modules such as RT prediction.