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

Abstract

BackgroundOver 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.ResultsWe 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.ConclusionWith KeyPathwayMiner 4.0, we publish a Cytoscape app for multi-omics based sub-network extraction. It is available in Cytoscape’s app store http://apps.cytoscape.org/apps/keypathwayminer or via http://keypathwayminer.mpi-inf.mpg.de.

Defining Human Tyrosine Kinase Phosphorylation Networks Using Yeast as an In Vivo Model Substrate

“…Here we defined minimal networks that best represent the clustered phospho-signal using subnetwork extraction procedures (Alcaraz et al, 2014). For each of the 16 kinases, we extracted minimal subnetworks that contained all phospho-proteins and on average 12% connecting, non-phosphorylated proteins from a global yeast interactome of 5,804 proteins and 63,545 PPIs (Fig.…”

“…Several related algorithms that search networks to retrieve active subnetworks have been developed in the context of expression analysis (Alcaraz et al, 2014), disease associations (Vanunu et al, 2010) and cancer mutational analysis (Hofree et al, 2013;Creixell et al, 2015b). We overlaid the phosphorylation values on the corresponding proteins in the yeast interactome network as seeding points to search for subnetworks with a maximum number of phospho-proteins.…”

“…We overlaid the phosphorylation values on the corresponding proteins in the yeast interactome network as seeding points to search for subnetworks with a maximum number of phospho-proteins. Specifically, using a greedy search algorithm (Alcaraz et al, 2014) we extracted subnetworks including all mapped pY-proteins for a given kinase, and a minimal number of proteins not phosphorylated that were required to connect the subnetworks. The number of non-phosphorylated proteins can then act as an indicator of how clustered target proteins are within these networks (Figure 4c).…”

“…Optimal subnetworks were generated using cytoscape (Version 3.2.1 / Java environment 1.8.0_51, (Shannon et al, 2003)) app "KeyPathMiner" (KPM 4, (Alcaraz et al, 2014)) and network propagation preformed using a liner integer program (Vanunu et al, 2010) implemented in the cytoscape app "Propagate".…”

Integrative methods for analyzing big data in precision medicine

“…Many other studies have also focused on developing methods for identifying aberrant network modules and pathways by utilizing molecular networks and other omics data. For example, Alcaraz et al [120] developed KeyPathwayMiner, a method for extraction of aberrant network modules from PPI network by integrating gene expression and DNA methylation data. The authors demonstrated the performance of KeyPathwayMiner on TCGA colorectal cancer patients.…”