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COGRIM specifications


Unique identifier OMICS_22849
Alternative name Clustering Of Genes into Regulons using Integrated Modeling
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux, Mac OS, Windows
Programming languages R
Computer skills Advanced
Stability Stable
Maintained Yes




No version available


  • person_outline Christian Stoeckert

Publication for Clustering Of Genes into Regulons using Integrated Modeling

COGRIM citations


Unraveling Regulatory Programs for NF kappaB, p53 and MicroRNAs in Head and Neck Squamous Cell Carcinoma

PLoS One
PMCID: 3777940
PMID: 24069219
DOI: 10.1371/journal.pone.0073656

[…] dvantage of the newly developed method is supported by the improved prediction of NF-κB and p53 target genes, in comparison with other methods. First, we compared our method to a Bayesian model-based COGRIM. Among the NF-κB targets predicted by our method, of 19% (in the wt p53-deficient) and 15% (in the mt p53) are consistent with known ones published previously. By contrast, the known NF-κB gene […]


Motif guided sparse decomposition of gene expression data for regulatory module identification

BMC Bioinformatics
PMCID: 3072956
PMID: 21426557
DOI: 10.1186/1471-2105-12-82

[…] μ. Since there is no ground truth of target genes available for this experiment, we used the functional enrichment of regulatory modules to compare the performance of mSD with that of another method, COGRIM []. COGRIM is derived from a Bayesian hierarchical model and implemented using the Gibbs sampling technique. COGRIM can help infer the activation or inhibition of TFs acting on their target gen […]


Transcriptional programs: Modelling higher order structure in transcriptional control

BMC Bioinformatics
PMCID: 2725141
PMID: 19607663
DOI: 10.1186/1471-2105-10-218

[…] les and explicitly models which TFs up or down-regulate which genes. SAMBA [] is a biclustering framework that analyses gene expression, protein interaction, growth phenotype, and TF binding data. In COGRIM, Chen et al. [] use Gibbs sampling in a Bayesian hierarchical model to integrate expression data, PSSM analyses and ChIP-chip data. They model uncertainty in each data source independently but […]


Systems biology defined NF κB regulons, interacting signal pathways and networks are implicated in the malignant phenotype of head and neck cancer cell lines differing in p53 status

Genome Biol
PMCID: 2397505
PMID: 18334025
DOI: 10.1186/gb-2008-9-3-r53

[…] Previously, heterogeneous gene expression signatures were identified in the UM-SCC cell lines associated with different p53 status []. In this study, NF-κB target genes were predicted by COGRIM modeling from 1,265 genes differentially expressed in UM-SCC cells, and subgrouped by their p53 status (Figure ). A total of 748 genes were identified as putative NF-κB target genes, which repr […]


Bayesian hierarchical model for transcriptional module discovery by jointly modeling gene expression and ChIP chip data

BMC Bioinformatics
PMCID: 1994961
PMID: 17683565
DOI: 10.1186/1471-2105-8-283

[…] th the binding data used to establish the constraints for the network structure []. The number of genes participating in the network construction is limited because of the complexity of model search. COGRIM [] algorithm uses a Bayesian hierarchical framework to fit a gene-by-gene linear regression model of a gene's expression levels as function of is a quadratic function of all TFs' expression lev […]

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COGRIM institution(s)
Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA; Center for Bioinformatics, University of Pennsylvania, Philadelphia, PA, USA; Department of Statistics, The Wharton School, University of Pennsylvania, Philadelphia, PA, USA; Department of Genetics, School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
COGRIM funding source(s)
Supported in part by NIH grant U01-DK56947 and a grant from the University of Pennsylvania Research Foundation.

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