Limits...
GBNet: deciphering regulatory rules in the co-regulated genes using a Gibbs sampler enhanced Bayesian network approach.

Shen L, Liu J, Wang W - BMC Bioinformatics (2008)

Bottom Line: Most of the rules learned by GBNet on YY1 and co-factors were supported by literature.In addition, a spacing constraint between YY1 and E2F was also supported by independent TF binding experiments.We thus conclude that GBNet is a useful tool for deciphering the "grammar" of transcriptional regulation.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Chemistry and Biochemistry, University of California, San Diego, California, USA. shen@ucsd.edu

ABSTRACT

Background: Combinatorial regulation of transcription factors (TFs) is important in determining the complex gene expression patterns particularly in higher organisms. Deciphering regulatory rules between cooperative TFs is a critical step towards understanding the mechanisms of combinatorial regulation.

Results: We present here a Bayesian network approach called GBNet to search for DNA motifs that may be cooperative in transcriptional regulation and the sequence constraints that these motifs may satisfy. We showed that GBNet outperformed the other available methods in the simulated and the yeast data. We also demonstrated the usefulness of GBNet on learning regulatory rules between YY1, a human TF, and its co-factors. Most of the rules learned by GBNet on YY1 and co-factors were supported by literature. In addition, a spacing constraint between YY1 and E2F was also supported by independent TF binding experiments.

Conclusion: We thus conclude that GBNet is a useful tool for deciphering the "grammar" of transcriptional regulation.

Show MeSH
Heatmap of YY1 target gene expression patterns.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC2571992&req=5

Figure 5: Heatmap of YY1 target gene expression patterns.

Mentions: Because YY1 can cooperate with various TFs, we used gene expression profiles to define these co-regulated subgroups of the YY1 target genes. Su et al. [23] performed microarray experiments in 79 human tissues and 782 YY1 target genes identified by GITTAR were probed in their arrays. We found five clusters among these YY1 target genes using hierarchical clustering algorithm [24] (Fig. 5). Cluster H1 to H4 were selected based on a correlation cutoff of 0.60 and a cluster size cutoff of 10 genes. Cluster H5 was manually selected because its members were significantly up-regulated and tightly correlated in testis tissues (correlation = 0.64) despite the average pairwise correlation over all the 79 tissues was only 0.33. Cluster H5 represents tissue-specific expression of YY1 targets and it is interesting to examine the underlying mechanism of transcriptional regulation.


GBNet: deciphering regulatory rules in the co-regulated genes using a Gibbs sampler enhanced Bayesian network approach.

Shen L, Liu J, Wang W - BMC Bioinformatics (2008)

Heatmap of YY1 target gene expression patterns.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2571992&req=5

Figure 5: Heatmap of YY1 target gene expression patterns.
Mentions: Because YY1 can cooperate with various TFs, we used gene expression profiles to define these co-regulated subgroups of the YY1 target genes. Su et al. [23] performed microarray experiments in 79 human tissues and 782 YY1 target genes identified by GITTAR were probed in their arrays. We found five clusters among these YY1 target genes using hierarchical clustering algorithm [24] (Fig. 5). Cluster H1 to H4 were selected based on a correlation cutoff of 0.60 and a cluster size cutoff of 10 genes. Cluster H5 was manually selected because its members were significantly up-regulated and tightly correlated in testis tissues (correlation = 0.64) despite the average pairwise correlation over all the 79 tissues was only 0.33. Cluster H5 represents tissue-specific expression of YY1 targets and it is interesting to examine the underlying mechanism of transcriptional regulation.

Bottom Line: Most of the rules learned by GBNet on YY1 and co-factors were supported by literature.In addition, a spacing constraint between YY1 and E2F was also supported by independent TF binding experiments.We thus conclude that GBNet is a useful tool for deciphering the "grammar" of transcriptional regulation.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Chemistry and Biochemistry, University of California, San Diego, California, USA. shen@ucsd.edu

ABSTRACT

Background: Combinatorial regulation of transcription factors (TFs) is important in determining the complex gene expression patterns particularly in higher organisms. Deciphering regulatory rules between cooperative TFs is a critical step towards understanding the mechanisms of combinatorial regulation.

Results: We present here a Bayesian network approach called GBNet to search for DNA motifs that may be cooperative in transcriptional regulation and the sequence constraints that these motifs may satisfy. We showed that GBNet outperformed the other available methods in the simulated and the yeast data. We also demonstrated the usefulness of GBNet on learning regulatory rules between YY1, a human TF, and its co-factors. Most of the rules learned by GBNet on YY1 and co-factors were supported by literature. In addition, a spacing constraint between YY1 and E2F was also supported by independent TF binding experiments.

Conclusion: We thus conclude that GBNet is a useful tool for deciphering the "grammar" of transcriptional regulation.

Show MeSH