Dataset Information

PSTIING: a 'systems' approach towards integrating signalling pathways, interaction and transcriptional regulatory networks in inflammation and cancer.

ABSTRACT: pSTIING (http://pstiing.licr.org) is a new publicly accessible web-based application and knowledgebase featuring 65 228 distinct molecular associations (comprising protein-protein, protein-lipid, protein-small molecule interactions and transcriptional regulatory associations), ligand-receptor-cell type information and signal transduction modules. It has a particular major focus on regulatory networks relevant to chronic inflammation, cell migration and cancer. The web application and interface provide graphical representations of networks allowing users to combine and extend transcriptional regulatory and signalling modules, infer molecular interactions across species and explore networks via protein domains/motifs, gene ontology annotations and human diseases. pSTIING also supports the direct cross-correlation of experimental results with interaction information in the knowledgebase via the CLADIST tool associated with pSTIING, which currently analyses and clusters gene expression, proteomic and phenotypic datasets. This allows the contextual projection of co-expression patterns onto prior network information, facilitating the identification of functional modules in physiologically relevant systems.

SUBMITTER: Ng A

PROVIDER: S-EPMC1347407 | biostudies-literature | 2006 Jan

REPOSITORIES: biostudies-literature

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pSTIING: a 'systems' approach towards integrating signalling pathways, interaction and transcriptional regulatory networks in inflammation and cancer.

Ng Aylwin A Bursteinas Borisas B Gao Qiong Q Mollison Ewan E Zvelebil Marketa M

Nucleic acids research 20060101 Database issue

pSTIING (http://pstiing.licr.org) is a new publicly accessible web-based application and knowledgebase featuring 65 228 distinct molecular associations (comprising protein-protein, protein-lipid, protein-small molecule interactions and transcriptional regulatory associations), ligand-receptor-cell type information and signal transduction modules. It has a particular major focus on regulatory networks relevant to chronic inflammation, cell migration and cancer. The web application and interface p ...[more]

PMID: 16381926

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Project description:Genome-wide libraries of yeast deletion strains have been used to screen for genes that drive phenotypes such as stress response. A surprising observation emerging from these studies is that the genes with the largest changes in mRNA expression during a state transition are not those that drive that transition. Here, we show that integrating gene expression data with context-independent protein interaction networks can help prioritize master regulators that drive biological phenotypes.Genes essential for survival had previously been shown to exhibit high centrality in protein interaction networks. However, the set of genes that drive growth in any specific condition is highly context-dependent. We inferred regulatory networks from gene expression data and transcription factor binding motifs in Saccharomyces cerevisiae, and found that high-degree nodes in regulatory networks are enriched for transcription factors that drive the corresponding phenotypes. We then found that using a metric combining protein interaction and transcriptional networks improved the enrichment for drivers in many of the contexts we examined. We applied this principle to a dataset of gene expression in normal human fibroblasts expressing a panel of viral oncogenes. We integrated regulatory interactions inferred from this data with a database of yeast two-hybrid protein interactions and ranked 571 human transcription factors by their combined network score. The ranked list was significantly enriched in known cancer genes that could not be found by standard differential expression or enrichment analyses.There has been increasing recognition that network-based approaches can provide insight into critical cellular elements that help define phenotypic state. Our analysis suggests that no one network, based on a single data type, captures the full spectrum of interactions. Greater insight can instead be gained by exploring multiple independent networks and by choosing an appropriate metric on each network. Moreover we can improve our ability to rank phenotypic drivers by combining the information from individual networks. We propose that such integrative network analysis could be used to combine clinical gene expression data with interaction databases to prioritize patient- and disease-specific therapeutic targets.

Dataset Information

PSTIING: a 'systems' approach towards integrating signalling pathways, interaction and transcriptional regulatory networks in inflammation and cancer.

Publications

pSTIING: a 'systems' approach towards integrating signalling pathways, interaction and transcriptional regulatory networks in inflammation and cancer.

Similar Datasets

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