imspy-simulation

TimsTOF data simulation tools for proteomics.

Installation

pip install imspy-simulation

For search integration (validation workflows):

pip install imspy-simulation[search]

For KOINA remote model support (optional):

pip install imspy-predictors[koina]

Features

• Frame Builders: DIA and DDA frame simulation with annotation support
• TimSim: Complete simulation pipeline for synthetic timsTOF data
• Prediction Models: Local PyTorch models with optional KOINA remote model support (see Prediction Models)
• Validation: Tools for validating simulated data against search results
• Integration Testing (EVAL): Automated validation against DiaNN, FragPipe, and Sage (see Integration Testing)
• Isotope Simulation: Accurate isotope distribution generation
• TDF Writing: Write simulated data to Bruker TDF format

Quick Start

from imspy_simulation import (
    DIAFrameBuilder,
    DDAFrameBuilder,
    SimulationDatabase,
    TransmissionHandle,
    create_frame_builder,
    AcquisitionMode,
)

# Create a DIA frame builder
frame_builder = DIAFrameBuilder(
    database_path="path/to/synthetic_data.db",
    num_threads=16,
)

# Build frames
frames = frame_builder.build_frames([1, 2, 3])

CLI Tools

timsim

Full simulation pipeline:

timsim config.toml
timsim config.toml --save-path output.d --reference-path reference.d --fasta-path proteome.fasta

Prediction Models

TimSim uses deep learning models for retention time, ion mobility (CCS), and fragment intensity prediction. By default, local PyTorch models are used. Optionally, remote models can be accessed via KOINA servers:

[models]
rt_model = ""              # "" = local (default), or e.g. "Deeplc_hela_hf"
ccs_model = ""             # "" = local (default), or e.g. "AlphaPeptDeep_ccs_generic"
intensity_model = ""       # "" = local (default), or e.g. "prosit", "alphapeptdeep"

Requires pip install imspy-predictors[koina] for remote models. Falls back to local models if KOINA is unreachable. See SIMULATOR_README.md for the full list of available models.

Integration Testing

The EVAL pipeline validates simulated datasets against production proteomics search engines:

python -m imspy_simulation.timsim.integration.sim --env env.toml --list
python -m imspy_simulation.timsim.integration.sim --env env.toml --test IT-DIA-HELA
python -m imspy_simulation.timsim.integration.eval --env env.toml --test IT-DIA-HELA

See the Validation README for setup, available tests, and configuration details.

Submodules

• builders/: Frame builder implementations (DIA, DDA)
• core/: Core protocols and wrappers
• data/: Simulation database and transmission handling
• timsim/: TimSim simulation pipeline
  • jobs/: Individual simulation steps
  • integration/: Integration workflows
  • validate/: Validation tools

Dependencies

• imspy-core: Core data structures (required)
• imspy-predictors: ML predictors for CCS, RT, intensity (required)
• imspy-search: Database search for validation (optional)

Related Packages

• imspy-core: Core data structures and timsTOF readers
• imspy-predictors: ML-based predictors
• imspy-search: Database search functionality
• imspy-vis: Visualization tools

License

MIT License - see LICENSE file for details.