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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.

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Distribution of motif ranks in BBNet and GBNet. Ties are in orange.
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Figure 4: Distribution of motif ranks in BBNet and GBNet. Ties are in orange.

Mentions: To further demonstrate that GBNet is less prone to get trapped in local optima, we examined the ranks of single motifs that appear in the rules learned by GBNet and BBNet. In search for the optimal Bayesian network, all motifs under consideration were first sorted in the descending order by their individual Bayesian scores, which reflect how well an individual motif can explain the data. The motifs were then added to the Bayesian network in this order to expedite the convergence of searching. Therefore, it is not unexpected to see that a large portion (43%) of motifs present in the rules learned by BBNet had the highest individual ranks (Fig. 4). As a comparison, GBNet found rules that involved motifs giving lower Bayesian score if considered individually (lower individual ranks) but higher (better) Bayesian score if considered together with satisfying specific sequence constraints (Fig. 4).


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)

Distribution of motif ranks in BBNet and GBNet. Ties are in orange.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 4: Distribution of motif ranks in BBNet and GBNet. Ties are in orange.
Mentions: To further demonstrate that GBNet is less prone to get trapped in local optima, we examined the ranks of single motifs that appear in the rules learned by GBNet and BBNet. In search for the optimal Bayesian network, all motifs under consideration were first sorted in the descending order by their individual Bayesian scores, which reflect how well an individual motif can explain the data. The motifs were then added to the Bayesian network in this order to expedite the convergence of searching. Therefore, it is not unexpected to see that a large portion (43%) of motifs present in the rules learned by BBNet had the highest individual ranks (Fig. 4). As a comparison, GBNet found rules that involved motifs giving lower Bayesian score if considered individually (lower individual ranks) but higher (better) Bayesian score if considered together with satisfying specific sequence constraints (Fig. 4).

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