Background: Mammalian genes are regulated at the transcriptional and posttranscriptional
levels. These mechanisms may involve the direct promotion or inhibition of transcription
via a regulator or post-transcriptional regulation through factors such as micro (mi)RNAs.
Objective: Construct gene regulation relationships modulated by causality inference-based miRNA-
(transition factor)-(target gene) networks and analysis gene expression data to identify gene expression
Methods: Mouse gene expression regulation relationships were manually curated from literature
using a text mining method which were then employed to generate miRNA-(transition factor)-
(target gene) networks. An algorithm was then introduced to identify gene expression regulators
from transcriptome profiling data by applying enrichment analysis to these networks.
Results: A total of 22,271 mouse gene expression regulation relationships were curated for 4,018
genes and 242 miRNAs. GEREA software was developed to perform the integrated analyses. We
applied the algorithm to transcriptome data for synthetic miR-155 oligo-treated mouse CD4+ Tcells
and confirmed that miR-155 is an important network regulator. The software was also tested
on publicly available transcriptional profiling data for Salmonella infection, resulting in the identification
of miR-125b as an important regulator.
Conclusion: The causality inference-based miRNA-(transition factor)-(target gene) networks serve
as a novel resource for gene expression regulation research, and GEREA is an effective and useful
adjunct to the currently available methods. The regulatory networks and the algorithm implemented
in the GEREA software package are available under a free academic license at