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

BMC Bioinformatics

Software

Olli Yli-Harja¹  Jason Lloyd-Price²  Matthias Dehmer³  Andre Ribeiro⁴  Shailesh Tripathi⁵  Frank Emmert-Streib⁶ 

[1] Computational Systems Biology, Department of Signal Processing, Tampere University of Technology, Tampere, Finland;Institute of Biosciences and Medical Technology, Tampere, Finland;Department of Biostatistics, Harvard T.H. Chan School of Public Health, Harvard University, Boston, USA;Laboratory of Biosystem Dynamics, Department of Signal Processing, Tampere University of Technology, Tampere, Finland;Institute for Theoretical Informatics, Mathematics and Operations Research, Department of Computer Science, Universität der Bundeswehr München, Munich, Germany;Laboratory of Biosystem Dynamics, Department of Signal Processing, Tampere University of Technology, Tampere, Finland;Institute of Biosciences and Medical Technology, Tampere, Finland;Predictive Medicine and Data Analytics Lab, Department of Signal Processing, Tampere University of Technology, Tampere, Finland;Predictive Medicine and Data Analytics Lab, Department of Signal Processing, Tampere University of Technology, Tampere, Finland;Institute of Biosciences and Medical Technology, Tampere, Finland;

关键词: Gene expression data;    Gene network;    Simulation;   

DOI  :  10.1186/s12859-017-1731-8

 received in 2016-05-23, accepted in 2017-06-15,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundsgnesR (Stochastic Gene Network Expression Simulator in R) is an R package that provides an interface to simulate gene expression data from a given gene network using the stochastic simulation algorithm (SSA). The package allows various options for delay parameters and can easily included in reactions for promoter delay, RNA delay and Protein delay. A user can tune these parameters to model various types of reactions within a cell. As examples, we present two network models to generate expression profiles. We also demonstrated the inference of networks and the evaluation of association measure of edge and non-edge components from the generated expression profiles.ResultsThe purpose of sgnesR is to enable an easy to use and a quick implementation for generating realistic gene expression data from biologically relevant networks that can be user selected.ConclusionssgnesR is freely available for academic use. The R package has been tested for R 3.2.0 under Linux, Windows and Mac OS X.

【 授权许可】

CC BY   
© The Author(s) 2017