sgnesR: An R package for simulating gene expression data from an underlying real gene network structure considering...

Area of Science:

• Computational Biology
• Systems Biology
• Bioinformatics

Background:

• Gene expression is a complex process influenced by various factors.
• Stochastic simulation algorithms (SSA) are crucial for modeling gene expression dynamics.
• Accurate simulation of gene expression requires consideration of delays in biological processes.

Purpose of the Study:

• To introduce sgnesR, an R package for simulating gene expression data.
• To provide an accessible interface for users to generate realistic gene expression profiles from user-defined networks.
• To facilitate the study of gene regulatory networks and their dynamics.

Main Methods:

• Utilizes the stochastic simulation algorithm (SSA) for gene expression simulation.
• Incorporates adjustable delay parameters for promoter, RNA, and protein synthesis.
• Enables network inference and evaluation of association measures from simulated data.

Main Results:

• Successfully generated gene expression profiles using two example network models.
• Demonstrated the capability of sgnesR to infer gene networks.
• Validated the evaluation of association measures for network components.

Conclusions:

• sgnesR is a freely available R package for academic use.
• The package is compatible with R version 3.2.0 and runs on Linux, Windows, and Mac OS X.
• Facilitates realistic gene expression data simulation for biological network analysis.