DREM 2.0: Improved reconstruction of dynamic regulatory networks from time-series expression data
1 Ray and Stephanie Lane Center for Computational Biology, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213 USA
2 Machine Learning Department, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, 15213, PA, USA
3 Computer Science Department, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, 15213, PA, USA
4 Department of Biological Chemistry, University of California Los Angeles, Los Angeles, 90095, CA, USA
Citation and License
BMC Systems Biology 2012, 6:104 doi:10.1186/1752-0509-6-104Published: 16 August 2012
Modeling dynamic regulatory networks is a major challenge since much of the protein-DNA interaction data available is static. The Dynamic Regulatory Events Miner (DREM) uses a Hidden Markov Model-based approach to integrate this static interaction data with time series gene expression leading to models that can determine when transcription factors (TFs) activate genes and what genes they regulate. DREM has been used successfully in diverse areas of biological research. However, several issues were not addressed by the original version.
DREM 2.0 is a comprehensive software for reconstructing dynamic regulatory networks that supports interactive graphical or batch mode. With version 2.0 a set of new features that are unique in comparison with other softwares are introduced. First, we provide static interaction data for additional species. Second, DREM 2.0 now accepts continuous binding values and we added a new method to utilize TF expression levels when searching for dynamic models. Third, we added support for discriminative motif discovery, which is particularly powerful for species with limited experimental interaction data. Finally, we improved the visualization to support the new features. Combined, these changes improve the ability of DREM 2.0 to accurately recover dynamic regulatory networks and make it much easier to use it for analyzing such networks in several species with varying degrees of interaction information.