ArticleBMC systems biology2012
DREM 2.0: Improved reconstruction of dynamic regulatory networks from time-series expression data.
Article in BMC systems biology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 82 papers, 1 of them a synthesis that pooled it.
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Who cites it
82 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Time trajectories in the transcriptomic response to exercise - a meta-analysis.Nature communications · 2021Pooled it
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- Age-independent and targetable transcription factor networks regulating CD8Cell reports · 2026Article
- Recovering time-varying networks from single-cell data.Bioinformatics (Oxford, England) · 2025Article
- Reconstructing Dynamic Gene Regulatory Networks Using f-Divergence from Time-Series scRNA-Seq Data.Current issues in molecular biology · 2025Article
- BADDADAN: Mechanistic modelling of time-series gene module expression.Quantitative plant biology · 2025Article
- Temporal Expression Analysis to Unravel Gene Regulatory Dynamics by microRNAs.Methods in molecular biology (Clifton, N.J.) · 2025Article
- GeTeSEPdb: A comprehensive database and online tool for the identification and analysis of gene profiles with temporal-specific expression patterns.Computational and structural biotechnology journal · 2024Article
- Constructing the dynamic transcriptional regulatory networks to identify phenotype-specific transcription regulators.Briefings in bioinformatics · 2024Article
- Strigolactone induces D14-dependent large-scale changes in gene expression requiring SWI/SNF chromatin remodellers.The Plant journal : for cell and molecular biology · 2024Article
- The mitochondrial multi-omic response to exercise training across rat tissues.Cell metabolism · 2024Article
- Multiomics analysis identifies novel facilitators of human dopaminergic neuron differentiation.EMBO reports · 2024Article
- Transcriptome Studies Reveal theAnimals : an open access journal from MDPI · 2023Article
- Escape from oncogene-induced senescence is controlled by POU2F2 and memorized by chromatin scars.Cell genomics · 2023Article
- Computational approaches to understand transcription regulation in development.Biochemical Society transactions · 2023Review
- The mitochondrial multi-omic response to exercise training across tissues.bioRxiv : the preprint server for biology · 2023Article
- N6-methyladenosine RNA is modified in the rat hippocampus following traumatic brain injury with hypothermia treatment.Frontiers in neuroscience · 2023Article
- Temporal and regulatory dynamics of the inner ear transcriptome during development in mice.Scientific reports · 2022Article
- Integrating bioinformatic strategies in spatial life science research.Briefings in bioinformatics · 2022Review
22 more citing papers are in PubMed but not listed here.
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Authors and funding
6 authors.
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Abstract
backgroundModeling 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.
resultsDREM 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.
conclusionsDREM 2.0 provides a unique framework for constructing and visualizing dynamic regulatory networks. DREM 2.0 can be downloaded from: http://www.sb.cs.cmu.edu/drem.
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