GeNGe: systematic generation of gene regulatory networks

By Hendrik Hache, C. Wierling, Hans Lehrach, Ralf Herwig. 2009

538 citations recorded by the source corpus.

Summary: The analysis of gene regulatory networks (GRNs) is a central goal of bioinformatics highly accelerated by the advent of new experimental techniques, such as RNA interference. A battery of reverse engineering methods has been developed in recent years to reconstruct the underlying GRNs from these and other experimental data. However, the performance of the individual methods is poorly understood and validation of algorithmic performances is still missing to a large extent. To enable such systematic validation, we have developed the web application GeNGe (GEne Network GEnerator), a controlled framework for the automatic generation of GRNs. The theoretical model for a GRN is a non-linear differential equation system. Networks can be user-defined or constructed in a modular way with the option to introduce global and local network perturbations. Resulting data can be used, e.g. as benchmark data for evaluating GRN reconstruction methods or for predicting effects of perturbations as theoretical counterparts of biological experiments. Availability: Available online at http://genge.molgen.mpg.de Contact: hache@molgen.mpg.de Supplementary information: Supplementary data are available at Bioinformatics online.

Subfields: Molecular Biology

Citations in the corpus

• Cistrome: an integrative platform for transcriptional regulation studies (2011, unknown): “This tool uses Bioconductor GO and GOstats [15] packages together with a query to the DAVID (Database for Annotation, Visualization and Integrated Discovery) web server [16].”
• Enrichment Map: A Network-Based Method for Gene-Set Enrichment Visualization and Interpretation (2010, unknown): “GOstats [23] and elim [24] adopt a reverse strategy: child terms (i.e. leaves in the hierarchy) are tested first, then parent nodes are modified so as not to include the genes present in their enriched children.”
• Genome-wide analysis of ETS-family DNA-binding in vitro and in vivo (2010, unknown): “analysis (Smyth, 2004; Wu et al, 2004; Falcon and Gentleman, 2007) is provided in the Supplementary data.”
• Genome of an arbuscular mycorrhizal fungus provides insight into the oldest plant symbiosis (2013, unknown): “To check whether the potential components were functionally biased, a GO bias analysis was conducted on the genes participating in the different components of the proposed mixture model, using the GOstats (59) R package.”
• Improved detection of overrepresentation of Gene-Ontology annotations with parent-child analysis (2007, unknown): “A similar algorithm was implemented in the GOstats package of Bioconductor (Falcon and Gentleman, 2007).”
• voom: precision weights unlock linear model analysis tools for RNA-seq read counts (2014, unknown): “Gene ontology analysis used the GOstats software package [64] and version 2.”
• g:Profiler—a web server for functional interpretation of gene lists (2016 update) (2016, unknown): “While data are frequently updated in some tools such as g:Profiler (9), GOstats (10) and Babelomics (11), many popular tools like DAVID (12) and Bingo (13) have not been updated in years.”
• DNA methylation profiles in monozygotic and dizygotic twins (2009, unknown): “The promoter and CGI probes were also subjected to the Gene Ontology (GO)-based analysi”
• Chipster: user-friendly analysis software for microarray and other high-throughput data (2011, unknown): “The pathway tools include gene enrichment analysis for Gene Ontology (GO) terms [21] and KEGG pathways [22] based on the hypergeometric test implemented in the GOstats package [23].”
• Using clusterProfiler to characterize multiomics data (2024, unknown): “Tools such as GOstats 33 , gprofiler2 34 , DAVID 35 , Enrichr 30 and Metascape 36 only support the ORA algorithm and cannot handle gene ranking lists without specific thresholds.”
• clusterProfiler: an R Package for Comparing Biological Themes Among Gene Clusters (2012, unknown): “Related tools for identifying predominant biological themes in a collection of genes were developed to enhance data interpretation, including GO::TermFinder (Boyle et al., 2004) and GOstats (Falcon and Gentleman, 2007) for GO enrichment analysis, and SubpathwayMiner (Li et al., 2009) for detecting e”