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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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Gene expression pairwise correlation distribution for target genes of the two spacing constraints found by GBNet on cluster H3.
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Figure 6: Gene expression pairwise correlation distribution for target genes of the two spacing constraints found by GBNet on cluster H3.

Mentions: The regulatory rules learned by GBNet and BBNet for all five clusters along with their P-values and Bayesian scores are listed in Table 2. Consistent with the observation in the simulated data and the yeast clusters, GBNet found sequence constraints between cooperative TFs in every cluster while BBNet only learned presence of motifs that can also be found by other means. The GBNet rules also achieved higher Bayesian scores than the BBNet presence rules, which suggest better fitting to the data. Again, the Bayesian networks learned by GBNet are not more complex than those learned by BBNet. In cluster H3, GBNet gave a Bayesian network with one less rule than BBNet but its Bayesian score is 10.0 higher. Two spacing constraints were found on H3: YY1-E2F and E2F-ELK1. We examined the gene expression pairwise correlation (PC) of the target genes of the two spacing constraints. While the E2F-ELK1 pair only marginally raises the PC, the YY1-E2F pair significantly improves the PC compared with background (Fig. 6). This shows the YY1-E2F pair is much more specific than the E2F-ELK1 pair in regulating transcriptional levels of their target genes. Finally, combining the two spacing constraints gives the optimal PC (Fig. 6).


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)

Gene expression pairwise correlation distribution for target genes of the two spacing constraints found by GBNet on cluster H3.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 6: Gene expression pairwise correlation distribution for target genes of the two spacing constraints found by GBNet on cluster H3.
Mentions: The regulatory rules learned by GBNet and BBNet for all five clusters along with their P-values and Bayesian scores are listed in Table 2. Consistent with the observation in the simulated data and the yeast clusters, GBNet found sequence constraints between cooperative TFs in every cluster while BBNet only learned presence of motifs that can also be found by other means. The GBNet rules also achieved higher Bayesian scores than the BBNet presence rules, which suggest better fitting to the data. Again, the Bayesian networks learned by GBNet are not more complex than those learned by BBNet. In cluster H3, GBNet gave a Bayesian network with one less rule than BBNet but its Bayesian score is 10.0 higher. Two spacing constraints were found on H3: YY1-E2F and E2F-ELK1. We examined the gene expression pairwise correlation (PC) of the target genes of the two spacing constraints. While the E2F-ELK1 pair only marginally raises the PC, the YY1-E2F pair significantly improves the PC compared with background (Fig. 6). This shows the YY1-E2F pair is much more specific than the E2F-ELK1 pair in regulating transcriptional levels of their target genes. Finally, combining the two spacing constraints gives the optimal PC (Fig. 6).

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