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DISTILLER: a data integration framework to reveal condition dependency of complex regulons in Escherichia coli.

Lemmens K, De Bie T, Dhollander T, De Keersmaecker SC, Thijs IM, Schoofs G, De Weerdt A, De Moor B, Vanderleyden J, Collado-Vides J, Engelen K, Marchal K - Genome Biol. (2009)

Bottom Line: We present DISTILLER, a data integration framework for the inference of transcriptional module networks.In addition, the condition dependency and modularity of the inferred transcriptional network was studied.Surprisingly, the level of regulatory complexity seemed lower than that which would be expected from RegulonDB, indicating that complex regulatory programs tend to decrease the degree of modularity.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Electrical Engineering, Katholieke Universiteit Leuven, Leuven, Belgium. karen.lemmens@esat.kuleuven.be

ABSTRACT
We present DISTILLER, a data integration framework for the inference of transcriptional module networks. Experimental validation of predicted targets for the well-studied fumarate nitrate reductase regulator showed the effectiveness of our approach in Escherichia coli. In addition, the condition dependency and modularity of the inferred transcriptional network was studied. Surprisingly, the level of regulatory complexity seemed lower than that which would be expected from RegulonDB, indicating that complex regulatory programs tend to decrease the degree of modularity.

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Venn Diagram showing the number of overlapping interactions between the networks of RegulonDB, CLR, SEREND and DISTILLER. CLR, SEREND and DISTILLER were applied to our data sets. As the overlap between SEREND and RegulonDB is algorithmically defined to be 100%, we show only the predictions of SEREND that were not reported in RegulonDB and do not explicitly visualize the overlap with RegulonDB for SEREND.
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Figure 3: Venn Diagram showing the number of overlapping interactions between the networks of RegulonDB, CLR, SEREND and DISTILLER. CLR, SEREND and DISTILLER were applied to our data sets. As the overlap between SEREND and RegulonDB is algorithmically defined to be 100%, we show only the predictions of SEREND that were not reported in RegulonDB and do not explicitly visualize the overlap with RegulonDB for SEREND.

Mentions: We compared our results with those of two recently published network reconstruction methods in order to assess the reliability of our predictions and the complementarity between the approaches. We selected the context of likelihood relatedness (CLR) method by Faith et al. [14], which relies only on microarray data to infer interactions between regulators and target genes and the semi-supervised regulatory network discoverer (SEREND) by Ernst et al. [17]. Both methods have initially been applied to E. coli data and their software was available. Moreover, the goal of SEREND [17] best resembles our aim: the optimal use of complementary available data sources to extend the known regulatory network in a reliable way. For comparison with CLR [14] and SEREND [17], we only compared the interactions inferred for those 67 regulators for which a binding site was described in RegulonDB. Note that CLR and SEREND can, in theory, also predict interactions for regulators without known binding sites. The results of the comparisons are summarized in Figure 3.


DISTILLER: a data integration framework to reveal condition dependency of complex regulons in Escherichia coli.

Lemmens K, De Bie T, Dhollander T, De Keersmaecker SC, Thijs IM, Schoofs G, De Weerdt A, De Moor B, Vanderleyden J, Collado-Vides J, Engelen K, Marchal K - Genome Biol. (2009)

Venn Diagram showing the number of overlapping interactions between the networks of RegulonDB, CLR, SEREND and DISTILLER. CLR, SEREND and DISTILLER were applied to our data sets. As the overlap between SEREND and RegulonDB is algorithmically defined to be 100%, we show only the predictions of SEREND that were not reported in RegulonDB and do not explicitly visualize the overlap with RegulonDB for SEREND.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2690998&req=5

Figure 3: Venn Diagram showing the number of overlapping interactions between the networks of RegulonDB, CLR, SEREND and DISTILLER. CLR, SEREND and DISTILLER were applied to our data sets. As the overlap between SEREND and RegulonDB is algorithmically defined to be 100%, we show only the predictions of SEREND that were not reported in RegulonDB and do not explicitly visualize the overlap with RegulonDB for SEREND.
Mentions: We compared our results with those of two recently published network reconstruction methods in order to assess the reliability of our predictions and the complementarity between the approaches. We selected the context of likelihood relatedness (CLR) method by Faith et al. [14], which relies only on microarray data to infer interactions between regulators and target genes and the semi-supervised regulatory network discoverer (SEREND) by Ernst et al. [17]. Both methods have initially been applied to E. coli data and their software was available. Moreover, the goal of SEREND [17] best resembles our aim: the optimal use of complementary available data sources to extend the known regulatory network in a reliable way. For comparison with CLR [14] and SEREND [17], we only compared the interactions inferred for those 67 regulators for which a binding site was described in RegulonDB. Note that CLR and SEREND can, in theory, also predict interactions for regulators without known binding sites. The results of the comparisons are summarized in Figure 3.

Bottom Line: We present DISTILLER, a data integration framework for the inference of transcriptional module networks.In addition, the condition dependency and modularity of the inferred transcriptional network was studied.Surprisingly, the level of regulatory complexity seemed lower than that which would be expected from RegulonDB, indicating that complex regulatory programs tend to decrease the degree of modularity.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Electrical Engineering, Katholieke Universiteit Leuven, Leuven, Belgium. karen.lemmens@esat.kuleuven.be

ABSTRACT
We present DISTILLER, a data integration framework for the inference of transcriptional module networks. Experimental validation of predicted targets for the well-studied fumarate nitrate reductase regulator showed the effectiveness of our approach in Escherichia coli. In addition, the condition dependency and modularity of the inferred transcriptional network was studied. Surprisingly, the level of regulatory complexity seemed lower than that which would be expected from RegulonDB, indicating that complex regulatory programs tend to decrease the degree of modularity.

Show MeSH
Related in: MedlinePlus