GeneSNAKE: a Python package for simulation of gene regulatory networks and perturbation-induced expression data

Area of Science:

• Systems Biology
• Computational Biology
• Bioinformatics

Background:

• Understanding gene interactions is crucial in systems biology.
• Gene regulatory networks (GRNs) are key to studying these interactions.
• Evaluating GRN inference methods requires simulated data from known GRNs.

Purpose of the Study:

• To introduce GeneSNAKE, a Python package for generating biologically realistic GRNs and expression data.
• To provide a tool for benchmarking GRN inference methods.
• To overcome limitations in existing simulation tools regarding network and data property control.

Main Methods:

• GeneSNAKE allows users to generate GRNs and expression data.
• It offers control over noise levels, noise models, and perturbation designs.
• The package includes functions for comparing network similarity and data/GRN properties.

Main Results:

• GeneSNAKE provides a comprehensive suite of modules for GRN simulation and benchmarking.
• It enables fine-grained control over network and data characteristics, including noise and perturbations.
• The package facilitates the creation of realistic simulated biological data.

Conclusions:

• GeneSNAKE is a powerful, open-source tool for simulating gene regulatory networks and expression data.
• Its flexibility and comprehensive features make it valuable for benchmarking GRN inference algorithms.
• The Python implementation allows for user extension and modification.