Wordprocessing

Virtual pathway explorer (viPEr) and pathway enrichment analysis tool (PEANuT): creating and analyzing focus networks to identify cross-talk between molecules and pathways

Abstract:

BACKGROUND: Interpreting large-scale studies from microarrays or next-generation sequencing for further experimental testing remains one of the major challenges in quantitative biology. Combining expression with physical or genetic interaction data has already been successfully applied to enhance knowledge from all types of high-throughput studies. Yet, toolboxes for navigating and understanding even small gene or protein networks are poorly developed. RESULTS: We introduce two Cytoscape plug-ins, which support the generation and interpretation of experiment-based interaction networks. The virtual pathway explorer viPEr creates so-called focus networks by joining a list of experimentally determined genes with the interactome of a specific organism. viPEr calculates all paths between two or more user-selected nodes, or explores the neighborhood of a single selected node. Numerical values from expression studies assigned to the nodes serve to score identified paths. The pathway enrichment analysis tool PEANuT annotates networks with pathway information from various sources and calculates enriched pathways between a focus and a background network. Using time series expression data of atorvastatin treated primary hepatocytes from six patients, we demonstrate the handling and applicability of viPEr and PEANuT. Based on our investigations using viPEr and PEANuT, we suggest a role of the FoxA1/A2/A3 transcriptional network in the cellular response to atorvastatin treatment. Moreover, we find an enrichment of metabolic and cancer pathways in the Fox transcriptional network and demonstrate a patient-specific reaction to the drug. CONCLUSIONS: The Cytoscape plug-in viPEr integrates -omics data with interactome data. It supports the interpretation and navigation of large-scale datasets by creating focus networks, facilitating mechanistic predictions from -omics studies. PEANuT provides an up-front method to identify underlying biological principles by calculating enriched pathways in focus networks.

26467653

Projects: A1.3: Identification of crucial metabolic processes during hepatocyte pr..., A3.2: Cross-talk of signaling pathways and endocytic machinery in hepato..., A3.5: The impact of cell polarity on metabolism detoxification and endoc..., C2: Organization of the sinusoidal system and the liver lobule - referen..., C5: Structural changes and functional consequences of the liver lobular/..., C6: Organization and function of the sinusoidal system and the liver lob...

BMC Genomics
BMC Genomics. 2015 Oct 14;16(1):790. doi: 10.1186/s12864-015-2017-z.
14th Oct 2015

M. Garmhausen, F. Hofmann, V. Senderov, M. Thomas, B. A. Kandel, B. H. Habermann

help Authors

[Marius Garmhausen] [Ute Hofmann] [Maria Thomas] [Bianca Habermann]

help Attributions

None

help Scales


Not Specified
Views: 1147
  • Created: 18th Dec 2015 at 12:55

Related items

Ajax-loader-large

Log in / Register

Need an account?
Sign up

Forgotten password?

Front Page

Virtual Liver Network

(v.0.22.0)

Related Projects and friends


Imprint Taverna workflow workbench myExperiment JWS Online ISATAB myGrid Sabio-RK BioPortal Semantic SBML

Powered by:

Ror-logo-32

Icons:
Silk icons 1.3
Crystal Clear icons