As part of the Cytoscape environment, TETRAMER provides an intuitive approach for reconstructing temporal transcriptional gene regulatory networks (GRNs) from user-provided temporal/stage differential expression data. This is performed by extracting specific TF-TG relationships from previously reconstructed GRNs in a variety of cell/tissue systems, and from different methods for data acquisition (microarray transcriptomes (CellNet); Cap Analysis of Gene Expression (CAGE) (Regulatory circuits); qualified transcription factors ChIP-sequencing assays (NGS-QC generator). Furthermore, TETRAMER provides an iterative approach to interrogate the capacity of each TF -retrieved on the reconstructed GRN- to drive the studied cell fate transition. For it, the temporal transcriptional regulation cascade derived from each TF is scrutinized as a way to verify its influence on the reconstitution of the differential gene expression patterns associated to the cell fate transition.

Predicted Master Regulators

TETRAMER has been used to predict master transcription factor regulators over a variety of cell fate transition cases like retinoids-driven Neuronal cell differentiation; OSKM-driven cell reprogramming as well as B-limphoma/primary macrophage transdifferentiation. Herein, the references to the articles from which the transcriptomes information has been used for TF prediction are available, as well as their relevant master regulator regulatory programs as predicted by TETRAMER.
In addition, TETRAMER has been used for evaluating TFs' cell fate transition capacity over a comparative study of transcriptomes assessed over more than 300 cell type/tissues. The results of these large study can be explored herein.


A rather intuitive overview of how to use TETRAMER for reconstructing GRNs, as well as to predict master TF regulators.


A direct access to the TETRAMER app (Cytoscape environment), as well as to the required gene regulatory networks collected from major publicly available efforts.


TETRAMER has been originally designed for predicting master regulators on retinoids-driven neuronal/endodermal cell fate transitions.