Efficient key pathway mining: combining networks and OMICS data

Abstract

Systems biology has emerged over the last decade. Driven by the advances in sophisticated measurement technology the research community generated huge molecular biology data sets. These comprise rather static data on the interplay of biological entities, for instance protein-protein interaction network data, as well as quite dynamic data collected for studying the behavior of individual cells or tissues in accordance with changing environmental conditions, such as DNA microarrays or RNA sequencing. Here we bring the two different data types together in order to gain higher level knowledge. We introduce a significantly improved version of the KeyPathwayMiner software framework. Given a biological network modelled as a graph and a set of expression studies, KeyPathwayMiner efficiently finds and visualizes connected sub-networks where most components are expressed in most cases. It finds all maximal connected sub-networks where all nodes but k exceptions are expressed in all experimental studies but at most l exceptions. We demonstrate the power of the new approach by comparing it to similar approaches with gene expression data previously used to study Huntington's disease. In addition, we demonstrate KeyPathwayMiner's flexibility and applicability to non-array data by analyzing genome-scale DNA methylation profiles from colorectal tumor cancer patients. KeyPathwayMiner release 2 is available as a Cytoscape plugin and online at http://keypathwayminer.mpi-inf.mpg.de.

Network analysis of human post-mortem microarrays reveals novel genes, microRNAs, and mechanistic scenarios of potential importance in fighting huntington's disease

“…There is no overlap of genes or biological pathways reported in the study of Alcaraz et al [34] with those identified in our work. Out of the 11 new genes proposed by these authors seven genes (CTNNB1, GNAQ, GRB2, OPTN, TP53, UBE2K and YWHAB) have p -value of < 0.01 but were not significantly expressed in all tissue types so they were not included into our 531 SDEGs.…”

“…Studying molecular mechanisms of HD Kalathur et al [33] found indications for potential relevance of the cell cycle processes, RNA splicing, Wnt and ErbB signaling, and proposed a candidate set of 24 novel genetic modifiers. Alcaraz et al [34] used the GSE3790 dataset in one of the case studies to prove the efficiency of their KeyPathwayMiner computational tool. An interesting moment in their analysis is that some of the proposed new HD-relevant genes (termed “exception” genes) are statistically insignificant.…”

Consolidation and translation regulation: Figure 1.

“…Therefore, efforts are being made to create principled and biologically meaningful representations of these large-scale data in models that are flexible enough to Systems Biology modeling has been widely used in biology for many years; it frequently comprises just a single data type (for example, mRNA level or protein concentration) or uses small numbers of molecules or canonical pathways and rarely takes spatial constrains into consideration. More recently, integrative methods have begun to overlay multiple data sources onto these models, for example, visualizing mRNA expression data in the context of protein-interaction networks (Alcaraz et al 2012;Li et al 2012) or proteomic data (Hallock and Thomas 2012), but these methods of data integration do not implicitly model the relationships between the different data types, and the functional insight obtained is limited.…”

Systems Level Analysis and Identification of Pathways and Networks Associated with Liver Fibrosis

“…This approach followed the work by Zhang et al [50] for mapping other species' gene expression data to a human PPI network. We utilized the KeyPathwayMiner program in Cytoscape 2.8 to obtain the liver fibrosis-relevant sub-network [51]–[53]. KeyPathwayMiner attempts to find maximally connected sub-networks for the input query genes with gene expression data using the ant-colony optimization algorithm [51].…”

“…We utilized the KeyPathwayMiner program in Cytoscape 2.8 to obtain the liver fibrosis-relevant sub-network [51]–[53]. KeyPathwayMiner attempts to find maximally connected sub-networks for the input query genes with gene expression data using the ant-colony optimization algorithm [51]. We used KeyPathwayMiner with ant-colony optimization algorithm , node exceptions (K) set to 100 , and case exceptions (L) set to 0 .…”