An improved map of conserved regulatory sites for Saccharomyces cerevisiae - PubMed (original) (raw)

An improved map of conserved regulatory sites for Saccharomyces cerevisiae

Kenzie D MacIsaac et al. BMC Bioinformatics. 2006.

Abstract

Background: The regulatory map of a genome consists of the binding sites for proteins that determine the transcription of nearby genes. An initial regulatory map for S. cerevisiae was recently published using six motif discovery programs to analyze genome-wide chromatin immunoprecipitation data for 203 transcription factors. The programs were used to identify sequence motifs that were likely to correspond to the DNA-binding specificity of the immunoprecipitated proteins. We report improved versions of two conservation-based motif discovery algorithms, PhyloCon and Converge. Using these programs, we create a refined regulatory map for S. cerevisiae by reanalyzing the same chromatin immunoprecipitation data.

Results: Applying the same conservative criteria that were applied in the original study, we find that PhyloCon and Converge each separately discover more known specificities than the combination of all six programs in the previous study. Combining the results of PhyloCon and Converge, we discover significant sequence motifs for 36 transcription factors that were previously missed. The new set of motifs identifies 636 more regulatory interactions than the previous one. The new network contains 28% more regulatory interactions among transcription factors, evidence of greater cross-talk between regulators.

Conclusion: Combining two complementary computational strategies for conservation-based motif discovery improves the ability to identify the specificity of transcriptional regulators from genome-wide chromatin immunoprecipitation data. The increased sensitivity of these methods significantly expands the map of yeast regulatory sites without the need to alter any of the thresholds for statistical significance. The new map of regulatory sites reveals a more elaborate and complex view of the yeast genetic regulatory network than was observed previously.

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Figures

Figure 1

Figure 1

Performance of PhyloCon, Converge, and the combined motif set on data for factors of known specificity. PhyloCon and Converge both recover more true positives than the suite of 6 programs employed in Harbison et al. Combining the results of PhyloCon and Converge significantly increases the number of true positives recovered, and eliminates false negatives, without a large adverse effect on the false positive rate. For definitions of the scoring criteria, see the Methods section.

Figure 2

Figure 2

Selected Factor Specificities in the New Yeast Regulatory Map.

Figure 3

Figure 3

Changes in the number of putative regulatory interactions for factors common to the old and new regulatory codes. For each modified motif, the number of regulatory interactions added and lost relative to the previously reported map is shown. Our analysis produced modified factor binding specificities for 85 factors, resulting in a net gain of 398 putatively regulated genes.

Figure 4

Figure 4

Regulatory interactions added through the addition of new factor specificity estimates. A total of 200 genes were identified as being putatively regulated by factors with newly reported motifs.

Figure 5

Figure 5

Yeast transcriptional regulatory network. Nodes correspond to transcription factors and an edge from one factor to another indicates that the first factor regulates the second. Red nodes correspond to factors without a previously reported specificity. Edges are colored red for interactions unique to the new map, grey for interactions common to the old and new maps, and green for interactions unique to the old map. There are 39 new interactions gained and 6 interactions lost relative to the previous map.

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