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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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The number of different types of regulatory rules learned by BBNet and GBNet.
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Figure 3: The number of different types of regulatory rules learned by BBNet and GBNet.

Mentions: From the 49 yeast clusters, BBNet and GBNet learned 105 and 112 regulatory rules in total, respectively. Consistent with the observation in [16], most (100 or 95%) of the regulatory rules learned by BBNet were simply "presence of a motif", which could also be learned by any motif finding algorithm. Because the searching started with "presence of a motif" and BBNet is easy to get trapped in local optima, it is not surprising that other types of sequence constraints were underrepresented. Although presence of a motif is still the majority of the rules learned by GBNet, the percentage is only 73% (82/112) and the portion of other types of constraints was significantly increased (Fig. 3). Finding rules other than presence distinguishes GBNet from other motif finding algorithms. This feature is particularly important in studying combinatorial regulation in higher organisms such as human.


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)

The number of different types of regulatory rules learned by BBNet and GBNet.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: The number of different types of regulatory rules learned by BBNet and GBNet.
Mentions: From the 49 yeast clusters, BBNet and GBNet learned 105 and 112 regulatory rules in total, respectively. Consistent with the observation in [16], most (100 or 95%) of the regulatory rules learned by BBNet were simply "presence of a motif", which could also be learned by any motif finding algorithm. Because the searching started with "presence of a motif" and BBNet is easy to get trapped in local optima, it is not surprising that other types of sequence constraints were underrepresented. Although presence of a motif is still the majority of the rules learned by GBNet, the percentage is only 73% (82/112) and the portion of other types of constraints was significantly increased (Fig. 3). Finding rules other than presence distinguishes GBNet from other motif finding algorithms. This feature is particularly important in studying combinatorial regulation in higher organisms such as human.

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