Inferring Transcriptional Networks by Mining Omics Data

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

“…Bicluster strategies are well suited to map both the condition dependency and the modularity of the transcriptional network from microarray compendia [ 8 - 11 ], but do not give any information on the transcriptional program of the modules. Methods have been developed to infer transcriptional interactions from microarrays only, by assuming that the transcription profile of the regulator is related to that of its target genes [ 12 - 14 ].…”

“…These methods have been successfully used to infer simple regulons [ 15 - 17 ] or to directly infer complex regulons, that is, the set of genes regulated by several regulators [ 18 - 20 ]. Most of the previously mentioned integrative approaches use the level to which the target genes of a particular regulator share a similar expression pattern as a feature for inferring regulator-target interactions, but do not include an explicit condition selection strategy as is the case with bicluster strategies [ 11 ]. A few exceptions exist, including the graph-based data integration tool SAMBA [ 20 ] and the sequential approach described by Bonneau et al .…”

Structural Learning of Genetic Regulatory Networks Based on Prior Biological Knowledge and Microarray Gene Expression Measurements

“…In addition, since gene expression data only represent partial information from the transcription regulation mechanisms within a cell, the reconstructed networks often have poor accuracy (Husmeier, 2003). This suggests that the inclusion of complementary information in the BN learning is important (Van den Bulcke, Lemmens, Van de Peer & Marchal, 2006). The general framework of using biological prior knowledge ( Figure 1) consists of three steps:…”

Reverse‐Engineering Transcriptional Modules from Gene Expression Data

“…Dimensionality reduction is usually achieved by a coarse-graining step, which collapses individual genes into clus-ters of coexpressed genes or modules, where all genes in a cluster share the same model parameters. 12 This conceptual simplification has as a drawback that inferred interactions are influenced by the module quality. Moreover, it is hard to translate the concept of a biological module in a strict mathematical definition.…”