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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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YY1 and E2F pairs predicted by GBNet were confirmed by ChIP-chip experiments. 79% of the 170 YY1-E2F pairs constrained by the distance were found to have probes with significant binding ratio change (more than 2-fold) within 300 bps.
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Figure 7: YY1 and E2F pairs predicted by GBNet were confirmed by ChIP-chip experiments. 79% of the 170 YY1-E2F pairs constrained by the distance were found to have probes with significant binding ratio change (more than 2-fold) within 300 bps.

Mentions: To confirm the distance constraint between YY1 and E2F family members, we examined how many of YY1 and E2F sites that satisfy the distance constraint (within 40 bp) were supported by the E2F ChIP-chip experiments (Fig. 7). Because the probes in the promoter array were not uniformly distributed in each promoter, a predicted E2F site by GBNet may fall into a gap between probes. In addition, the sonicated DNA segments in ChIP-chip experiments had a length of hundreds of base pairs. Therefore, if a predicted E2F site is within a short distance from a probe with significant binding ratio, it is likely the E2F proteins bind to the predicted site. Among the 170 YY1-E2F motif pairs predicted by GBNet in the cluster H3 genes, we found that 79% of them were close to a probe (within 300 bp) with significant binding ratio of more than 2-fold (Table S4) (see Additional file 2 for more details). As a control, 104 genes which contain an YY1 site but do not satisfy the YY1-E2F spacing constraint were selected from the genome. Among these control genes, only 20% contain a probe (within 300 bp) with significant binding ratio of more than 2-fold. The statistical significance (p-value = 1.4e-22) was evaluated by Fisher's exact test between the two groups. This suggests that most of the predicted E2F sites by GBNet were bound by E2F proteins and the majority of the YY1-E2F distance constraints identified by GBNet were thus supported by the E2F ChIP-chip experiments.


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)

YY1 and E2F pairs predicted by GBNet were confirmed by ChIP-chip experiments. 79% of the 170 YY1-E2F pairs constrained by the distance were found to have probes with significant binding ratio change (more than 2-fold) within 300 bps.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 7: YY1 and E2F pairs predicted by GBNet were confirmed by ChIP-chip experiments. 79% of the 170 YY1-E2F pairs constrained by the distance were found to have probes with significant binding ratio change (more than 2-fold) within 300 bps.
Mentions: To confirm the distance constraint between YY1 and E2F family members, we examined how many of YY1 and E2F sites that satisfy the distance constraint (within 40 bp) were supported by the E2F ChIP-chip experiments (Fig. 7). Because the probes in the promoter array were not uniformly distributed in each promoter, a predicted E2F site by GBNet may fall into a gap between probes. In addition, the sonicated DNA segments in ChIP-chip experiments had a length of hundreds of base pairs. Therefore, if a predicted E2F site is within a short distance from a probe with significant binding ratio, it is likely the E2F proteins bind to the predicted site. Among the 170 YY1-E2F motif pairs predicted by GBNet in the cluster H3 genes, we found that 79% of them were close to a probe (within 300 bp) with significant binding ratio of more than 2-fold (Table S4) (see Additional file 2 for more details). As a control, 104 genes which contain an YY1 site but do not satisfy the YY1-E2F spacing constraint were selected from the genome. Among these control genes, only 20% contain a probe (within 300 bp) with significant binding ratio of more than 2-fold. The statistical significance (p-value = 1.4e-22) was evaluated by Fisher's exact test between the two groups. This suggests that most of the predicted E2F sites by GBNet were bound by E2F proteins and the majority of the YY1-E2F distance constraints identified by GBNet were thus supported by the E2F ChIP-chip experiments.

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