KENeV : A web-application for the automated reconstruction and visualization of the enriched metabolic and signaling super-pathways deriving from genomic experiments

Abstract

Gene expression analysis, using high throughput genomic technologies,has become an indispensable step for the meaningful interpretation of the underlying molecular complexity, which shapes the phenotypic manifestation of the investigated biological mechanism. The modularity of the cellular response to different experimental conditions can be comprehended through the exploitation of molecular pathway databases, which offer a controlled, curated background for statistical enrichment analysis. Existing tools enable pathway analysis, visualization, or pathway merging but none integrates a fully automated workflow, combining all above-mentioned modules and destined to non-programmer users.We introduce an online web application, named KEGG Enriched Network Visualizer (KENeV), which enables a fully automated workflow starting from a list of differentially expressed genes and deriving the enriched KEGG metabolic and signaling pathways, merged into two respective, non-redundant super-networks. The final networks can be downloaded as SBML files, for further analysis, or instantly visualized through an interactive visualization module.In conclusion, KENeV (available online at http://www.grissom.gr/kenev) provides an integrative tool, suitable for users with no programming experience, for the functional interpretation, at both the metabolic and signaling level, of differentially expressed gene subsets deriving from genomic experiments.

Control of anterior GR adient 2 ( AGR 2) dimerization links endoplasmic reticulum proteostasis to inflammation

“…Instead, it employs a nonparametric, resampling‐based, empirical alternative to multiple testing corrections, which provides a corrected measure for the statistical significance of the enrichments based on their frequencies of observation. Hence, the algorithm prioritizes terms with less frequent enrichments, which tend to represent broader, more systemic functions, represented by larger groups of genes (Chatziioannou & Moulos, ; Pilalis & Chatziioannou, ; Pilalis et al , ). The ranking of genes is performed by a graph‐theoretical method, which corrects the annotation bias of the Gene Ontology hierarchical network and ranks the related genes according to their regulatory impact in the corrected semantic network (Moutselos et al , ; Koutsandreas et al , ).…”

A Computational Pipeline for the Extraction of Actionable Biological Information From NGS-Phage Display Experiments

“…This ranking is corrected by performing bootstrapping as an alternative to multiple test correction methods (Bonferroni, FDR), thus avoiding false assumptions about the distribution of p -values. Instead of adjusting the p -values, the bootstrapping algorithm reorders the initial distribution and prioritizes the less frequently observed enrichments which tend to represent broader pathways or functions and, thus, are of stronger biological content (Pilalis and Chatziioannou, 2013; Pilalis et al, 2015).…”

PANEV: an R package for a pathway-based network visualization

“…Pathway analysis and visualization tools are now successfully and routinely applied to gene expression and genetic data analyses and they represent a support key to understand biological systems [6][7][8][9][10][11]. In this regard, pathwaybased approaches are particularly useful when complex phenomena, with a quantitative inheritance, are under study [12].…”

“…The network is created using KEGG information [16]. As far as we know, no other KEGG visualization tool [6][7][8] provides such a feature that may help to identify functional candidate genes among the list of provided ones. PANEV has also features that are rarely simultaneously available in other pathway visualization tools [7,17,18].…”