Limits...
Defining transcriptional networks through integrative modeling of mRNA expression and transcription factor binding data.

Gao F, Foat BC, Bussemaker HJ - BMC Bioinformatics (2004)

Bottom Line: These results are validated by an analysis of enrichment for functional annotation, response for transcription factor deletion, and over-representation of cis-regulatory motifs.We are able to assign directionality to transcription factors that control divergently transcribed genes sharing the same promoter region.Finally, we identify an intrinsic limitation of transcription factor deletion experiments related to the combinatorial nature of transcriptional control, to which our approach provides an alternative.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Biological Sciences, Columbia University, New York, New York 10027, USA. fg2037@columbia.edu

ABSTRACT

Background: Functional genomics studies are yielding information about regulatory processes in the cell at an unprecedented scale. In the yeast S. cerevisiae, DNA microarrays have not only been used to measure the mRNA abundance for all genes under a variety of conditions but also to determine the occupancy of all promoter regions by a large number of transcription factors. The challenge is to extract useful information about the global regulatory network from these data.

Results: We present MA-Networker, an algorithm that combines microarray data for mRNA expression and transcription factor occupancy to define the regulatory network of the cell. Multivariate regression analysis is used to infer the activity of each transcription factor, and the correlation across different conditions between this activity and the mRNA expression of a gene is interpreted as regulatory coupling strength. Applying our method to S. cerevisiae, we find that, on average, 58% of the genes whose promoter region is bound by a transcription factor are true regulatory targets. These results are validated by an analysis of enrichment for functional annotation, response for transcription factor deletion, and over-representation of cis-regulatory motifs. We are able to assign directionality to transcription factors that control divergently transcribed genes sharing the same promoter region. Finally, we identify an intrinsic limitation of transcription factor deletion experiments related to the combinatorial nature of transcriptional control, to which our approach provides an alternative.

Conclusion: Our reliable classification of ChIP positives into functional and non-functional TF targets based on their expression pattern across a wide range of conditions provides a starting point for identifying the unknown sequence features in non-coding DNA that directly or indirectly determine the context dependence of transcription factor action. Complete analysis results are available for browsing or download at http://bussemaker.bio.columbia.edu/papers/MA-Networker/.

Show MeSH

Related in: MedlinePlus

Enrichment for functional annotation. The number of significantly over-represented Gene Ontology (GO) categories in the group B+/C+ of genes that couple to transcription factor activity (red) and the group B+/C- of genes that do not couple (green) for each of the 37 transcription factors analyzed. No significant enrichment for any GO category was found in most B+/C- gene groups, supporting the hypothesis that only the coupling B+/C+ genes are functional targets.
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC407845&req=5

Figure 2: Enrichment for functional annotation. The number of significantly over-represented Gene Ontology (GO) categories in the group B+/C+ of genes that couple to transcription factor activity (red) and the group B+/C- of genes that do not couple (green) for each of the 37 transcription factors analyzed. No significant enrichment for any GO category was found in most B+/C- gene groups, supporting the hypothesis that only the coupling B+/C+ genes are functional targets.

Mentions: Several analyses were performed to validate our results. First, we established that B+/C+ genes are significantly enriched for specific Gene Ontology (GO) categories (hypergeometric distribution; 5% false discovery rate) [20]. This result is not surprising, as we would already expect the set B+ of ChIP positives per se to be enriched for roughly the same functional categories. By contrast, for almost all TFs analyzed we found no significant enrichment for any GO category in the set of non-coupling (B+/C-) genes (Fig. 2, see supplementary website for details). This result is very significant because it suggests that our criterion for distinguishing functional from non-functional TF targets based on regulatory coupling is accurate: There seems to have been no evolutionary pressure on the set of B+/C- genes.


Defining transcriptional networks through integrative modeling of mRNA expression and transcription factor binding data.

Gao F, Foat BC, Bussemaker HJ - BMC Bioinformatics (2004)

Enrichment for functional annotation. The number of significantly over-represented Gene Ontology (GO) categories in the group B+/C+ of genes that couple to transcription factor activity (red) and the group B+/C- of genes that do not couple (green) for each of the 37 transcription factors analyzed. No significant enrichment for any GO category was found in most B+/C- gene groups, supporting the hypothesis that only the coupling B+/C+ genes are functional targets.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC407845&req=5

Figure 2: Enrichment for functional annotation. The number of significantly over-represented Gene Ontology (GO) categories in the group B+/C+ of genes that couple to transcription factor activity (red) and the group B+/C- of genes that do not couple (green) for each of the 37 transcription factors analyzed. No significant enrichment for any GO category was found in most B+/C- gene groups, supporting the hypothesis that only the coupling B+/C+ genes are functional targets.
Mentions: Several analyses were performed to validate our results. First, we established that B+/C+ genes are significantly enriched for specific Gene Ontology (GO) categories (hypergeometric distribution; 5% false discovery rate) [20]. This result is not surprising, as we would already expect the set B+ of ChIP positives per se to be enriched for roughly the same functional categories. By contrast, for almost all TFs analyzed we found no significant enrichment for any GO category in the set of non-coupling (B+/C-) genes (Fig. 2, see supplementary website for details). This result is very significant because it suggests that our criterion for distinguishing functional from non-functional TF targets based on regulatory coupling is accurate: There seems to have been no evolutionary pressure on the set of B+/C- genes.

Bottom Line: These results are validated by an analysis of enrichment for functional annotation, response for transcription factor deletion, and over-representation of cis-regulatory motifs.We are able to assign directionality to transcription factors that control divergently transcribed genes sharing the same promoter region.Finally, we identify an intrinsic limitation of transcription factor deletion experiments related to the combinatorial nature of transcriptional control, to which our approach provides an alternative.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Biological Sciences, Columbia University, New York, New York 10027, USA. fg2037@columbia.edu

ABSTRACT

Background: Functional genomics studies are yielding information about regulatory processes in the cell at an unprecedented scale. In the yeast S. cerevisiae, DNA microarrays have not only been used to measure the mRNA abundance for all genes under a variety of conditions but also to determine the occupancy of all promoter regions by a large number of transcription factors. The challenge is to extract useful information about the global regulatory network from these data.

Results: We present MA-Networker, an algorithm that combines microarray data for mRNA expression and transcription factor occupancy to define the regulatory network of the cell. Multivariate regression analysis is used to infer the activity of each transcription factor, and the correlation across different conditions between this activity and the mRNA expression of a gene is interpreted as regulatory coupling strength. Applying our method to S. cerevisiae, we find that, on average, 58% of the genes whose promoter region is bound by a transcription factor are true regulatory targets. These results are validated by an analysis of enrichment for functional annotation, response for transcription factor deletion, and over-representation of cis-regulatory motifs. We are able to assign directionality to transcription factors that control divergently transcribed genes sharing the same promoter region. Finally, we identify an intrinsic limitation of transcription factor deletion experiments related to the combinatorial nature of transcriptional control, to which our approach provides an alternative.

Conclusion: Our reliable classification of ChIP positives into functional and non-functional TF targets based on their expression pattern across a wide range of conditions provides a starting point for identifying the unknown sequence features in non-coding DNA that directly or indirectly determine the context dependence of transcription factor action. Complete analysis results are available for browsing or download at http://bussemaker.bio.columbia.edu/papers/MA-Networker/.

Show MeSH
Related in: MedlinePlus