Predicts transcriptional regulatory modules (TRMs) by integrating genomic information from transcription factor (TF) ChIP-seq data, cell type-specific gene expression and protein-protein interaction data. A set of genomic sites identified for a specific TF by ChIP-seq is scanned for the presence of over-represented motifs, to which putative TFs are assigned by sequence similarity. Cell type-specific expression data is used to filter out candidate TFs, and in a final step only those candidates that have been reported to make interactions in the BioGRID database are retained.
Characterizes DNA associations with human transcription factors (TFs) using expression profiles, protein-protein interactions and recognition motifs. PAnDA predicts TF binding events with >0.80 accuracy revealing cell-specific regulatory patterns that can be exploited for future investigations. Even when the precise DNA-binding motifs of a specific TF are not available, the information derived from protein-protein networks is sufficient to perform high-confidence predictions (area under the ROC curve of 0.89).
Combines protein–protein interaction networks and domain–domain interaction networks with the cluster-quality metric cohesiveness. The metric is locally maximized on the holistic level of protein interactions, and connectivity constraints on the domain level are used to account for the exclusive and thus inherently combinatorial nature of the interactions within such assemblies.
Provides a literature-derived transcriptional regulatory map for Arabidopsis. ATRM is a web application that reveals the heterogeneity of developmental and stress response subnetworks and the wiring preference of novel transcription factors (TFs) in transcriptional regulatory systems. It was evaluated by comparing the proportion of regulatory pairs co-existing in the same biological process.
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