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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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Related in: MedlinePlus

The regression tree generated by the iteration with the best likelihood for a 1X (top) and 2X (bottom) data sets. Internal nodes corresponding to liver-specific transcription factors are colored yellow, and those corresponding to erythroid-specific factors are red.
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Figure 4: The regression tree generated by the iteration with the best likelihood for a 1X (top) and 2X (bottom) data sets. Internal nodes corresponding to liver-specific transcription factors are colored yellow, and those corresponding to erythroid-specific factors are red.

Mentions: Figure 4 shows the regression trees generated from one run for the 1X and 2X data sets. Each internal node tests the value of an attribute Bf, which indicates whether factor Φf is predicted to bind the module in the tissue under consideration. Each leaf shows the conditional probability predicted, which is the probability of R = 1 on the condition specified by the path from to root to the leaf.


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

Chen X, Blanchette M - BMC Bioinformatics (2007)

The regression tree generated by the iteration with the best likelihood for a 1X (top) and 2X (bottom) data sets. Internal nodes corresponding to liver-specific transcription factors are colored yellow, and those corresponding to erythroid-specific factors are red.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 4: The regression tree generated by the iteration with the best likelihood for a 1X (top) and 2X (bottom) data sets. Internal nodes corresponding to liver-specific transcription factors are colored yellow, and those corresponding to erythroid-specific factors are red.
Mentions: Figure 4 shows the regression trees generated from one run for the 1X and 2X data sets. Each internal node tests the value of an attribute Bf, which indicates whether factor Φf is predicted to bind the module in the tissue under consideration. Each leaf shows the conditional probability predicted, which is the probability of R = 1 on the condition specified by the path from to root to the leaf.

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