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KeyPathwayMiner 4.0: condition-specific pathway analysis by combining multiple omics studies and networks with Cytoscape

Nicolas Alcaraz145*, Josch Pauling3, Richa Batra4, Eudes Barbosa47, Alexander Junge128, Anne GL Christensen5, Vasco Azevedo7, Henrik J Ditzel56 and Jan Baumbach124

Author Affiliations

1 Max Planck Institute for Informatics, Saarbrücken, Germany

2 Cluster of Excellence for Multimodal Computing and Interaction, Saarland University, Saarbrücken, Germany

3 Department of Biochemistry and Molecular Biology, University of Southern Denmark, Odense, Denmark

4 Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark

5 Department of Cancer and Inflammation Research, Institute of Molecular Medicine, University of Southern Denmark, Odense, Denmark

6 Department of Oncology, Odense University Hospital, Odense, Denmark

7 Institute of Biological Sciences, Laboratory of Molecular and Cellular Genetic, Federal University of Minas Gerais, Belo Horizonte, Brazil

8 Center for non-coding RNA in Technology and Health, Section for Animal Genetics, Bioinformatics and Breeding, University of Copenhagen, Frederiksberg, Denmark

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BMC Systems Biology 2014, 8:99  doi:10.1186/s12918-014-0099-x

Published: 19 August 2014



Over the last decade network enrichment analysis has become popular in computational systems biology to elucidate aberrant network modules. Traditionally, these approaches focus on combining gene expression data with protein-protein interaction (PPI) networks. Nowadays, the so-called omics technologies allow for inclusion of many more data sets, e.g. protein phosphorylation or epigenetic modifications. This creates a need for analysis methods that can combine these various sources of data to obtain a systems-level view on aberrant biological networks.


We present a new release of KeyPathwayMiner (version 4.0) that is not limited to analyses of single omics data sets, e.g. gene expression, but is able to directly combine several different omics data types. Version 4.0 can further integrate existing knowledge by adding a search bias towards sub-networks that contain (avoid) genes provided in a positive (negative) list. Finally the new release now also provides a set of novel visualization features and has been implemented as an app for the standard bioinformatics network analysis tool: Cytoscape.


With KeyPathwayMiner 4.0, we publish a Cytoscape app for multi-omics based sub-network extraction. It is available in Cytoscape’s app store webcite or via webcite.

Network enrichment; Protein-protein interaction; Multi-omics; Key pathways