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Prediction of tissue-specific cis-regulatory modules using Bayesian networks and regression trees.

Chen X, Blanchette M - BMC Bioinformatics (2007)

Bottom Line: The network integrates predicted transcription factor binding site information, transcription factor expression data, and target gene expression data.Our approach is shown to accurately identify known human liver and erythroid-specific modules.When applied to the prediction of tissue-specific modules in 10 different tissues, the network predicts a number of important transcription factor combinations whose concerted binding is associated to specific expression.

View Article: PubMed Central - HTML - PubMed

Affiliation: McGill Centre for Bioinformatics, 3775 University Street, room 332, Montreal, Quebec, Canada, H3A 2B4. xchen@cs.washington.edu

ABSTRACT

Background: In vertebrates, a large part of gene transcriptional regulation is operated by cis-regulatory modules. These modules are believed to be regulating much of the tissue-specificity of gene expression.

Result: We develop a Bayesian network approach for identifying cis-regulatory modules likely to regulate tissue-specific expression. The network integrates predicted transcription factor binding site information, transcription factor expression data, and target gene expression data. At its core is a regression tree modeling the effect of combinations of transcription factors bound to a module. A new unsupervised EM-like algorithm is developed to learn the parameters of the network, including the regression tree structure.

Conclusion: Our approach is shown to accurately identify known human liver and erythroid-specific modules. When applied to the prediction of tissue-specific modules in 10 different tissues, the network predicts a number of important transcription factor combinations whose concerted binding is associated to specific expression.

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Regression tree obtained from the best of ten runs on the set of 6,278 modules and 10 tissues. Nodes are colored based on the tissue in which a particular factor is expressed.
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Figure 5: Regression tree obtained from the best of ten runs on the set of 6,278 modules and 10 tissues. Nodes are colored based on the tissue in which a particular factor is expressed.

Mentions: The regression tree obtained obtained from the best run is shown in Figure 5. We can clearly observe from the tree that the positive assignments along each path leading to a leaf typically consists of TFs expressed in the same tissue. Several known tissue-specific combinations of TFs are recovered in the tree, such as C/EBP + HNF1 and C/EBP + HNF4 in liver. Also, many new and potentially meaningful TF combinations are predicted, such as C/EBP + AR in liver and Tax/CREB + GATA1 in erythroid.


Prediction of tissue-specific cis-regulatory modules using Bayesian networks and regression trees.

Chen X, Blanchette M - BMC Bioinformatics (2007)

Regression tree obtained from the best of ten runs on the set of 6,278 modules and 10 tissues. Nodes are colored based on the tissue in which a particular factor is expressed.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2230503&req=5

Figure 5: Regression tree obtained from the best of ten runs on the set of 6,278 modules and 10 tissues. Nodes are colored based on the tissue in which a particular factor is expressed.
Mentions: The regression tree obtained obtained from the best run is shown in Figure 5. We can clearly observe from the tree that the positive assignments along each path leading to a leaf typically consists of TFs expressed in the same tissue. Several known tissue-specific combinations of TFs are recovered in the tree, such as C/EBP + HNF1 and C/EBP + HNF4 in liver. Also, many new and potentially meaningful TF combinations are predicted, such as C/EBP + AR in liver and Tax/CREB + GATA1 in erythroid.

Bottom Line: The network integrates predicted transcription factor binding site information, transcription factor expression data, and target gene expression data.Our approach is shown to accurately identify known human liver and erythroid-specific modules.When applied to the prediction of tissue-specific modules in 10 different tissues, the network predicts a number of important transcription factor combinations whose concerted binding is associated to specific expression.

View Article: PubMed Central - HTML - PubMed

Affiliation: McGill Centre for Bioinformatics, 3775 University Street, room 332, Montreal, Quebec, Canada, H3A 2B4. xchen@cs.washington.edu

ABSTRACT

Background: In vertebrates, a large part of gene transcriptional regulation is operated by cis-regulatory modules. These modules are believed to be regulating much of the tissue-specificity of gene expression.

Result: We develop a Bayesian network approach for identifying cis-regulatory modules likely to regulate tissue-specific expression. The network integrates predicted transcription factor binding site information, transcription factor expression data, and target gene expression data. At its core is a regression tree modeling the effect of combinations of transcription factors bound to a module. A new unsupervised EM-like algorithm is developed to learn the parameters of the network, including the regression tree structure.

Conclusion: Our approach is shown to accurately identify known human liver and erythroid-specific modules. When applied to the prediction of tissue-specific modules in 10 different tissues, the network predicts a number of important transcription factor combinations whose concerted binding is associated to specific expression.

Show MeSH
Related in: MedlinePlus