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

Motivation: Understanding how genes interact with and regulate each other is a key challenge in systems biology. One of the primary methods to study this is through gene regulatory networks (GRNs). The field of GRN inference faces many challenges, which necessitate effective tools for evaluating inference methods. Data that corresponds to a known GRN, from various conditions and experimental setups is necessary for this purpose, which is only possible to attain via simulation. However, most existing tools for GRN-based simulation are limited either in network or data properties, with few or no options to modify these properties. Results: We present GeneSNAKE, a Python package designed to allow users to generate biologically realistic GRNs and expression data for benchmarking purposes. GeneSNAKE improves on previous work by providing a unique combination of modules, allowing users to control a wide range of GRN and data properties. It provides full control of the noise level, several noise models, full control of the perturbation design, and a wide range of pre-defined perturbation schemes. For benchmarking, GeneSNAKE offers several functions both for comparing network similarity, and properties in data and GRNs. These functions can further be used to study properties of biological data to produce simulated data with more realistic properties. Availability and implementation: GeneSNAKE is an open-source, comprehensive simulation and benchmarking package with powerful capabilities that are not combined in any other single package. Thanks to the Python implementation, it can be extended and modified by users. The tool is available at: https://bitbucket.org/sonnhammergrni/genesnake/.