Assembling global maps of cellular function through integrative analysis of physical and genetic networks

To take full advantage of high-throughput genetic and physical interaction mapping projects, the raw interactions must first be assembled into models of cell structure and function. PanGIA (for physical and genetic interaction alignment) is a plug-in for the bioinformatics platform Cytoscape, designed to integrate physical and genetic interactions into hierarchical module maps. PanGIA identifies 'modules' as sets of proteins whose physical and genetic interaction data matches that of known protein complexes. Higher-order functional cooperativity and redundancy is identified by enrichment for genetic interactions across modules. This protocol begins with importing interaction networks into Cytoscape, followed by filtering and basic network visualization. Next, PanGIA is used to infer a set of modules and their functional inter-relationships. This module map is visualized in a number of intuitive ways, and modules are tested for functional enrichment and overlap with known complexes. The full protocol can be completed between 10 and 30 min, depending on the size of the data set being analyzed.     

GeneMANIA Cytoscape plugin: fast gene function predictions on the desktop

The GeneMANIA Cytoscape plugin brings fast gene function prediction capabilities to the desktop. GeneMANIA identifies the most related genes to a query gene set using a guilt-by-association approach. The plugin uses over 800 networks from six organisms and each related gene is traceable to the source network used to make the prediction. Users may add their own interaction networks and expression profile data to complement or override the default data. Availability and Implementation: The GeneMANIA Cytoscape plugin is implemented in Java and is freely available at http://www.genemania.org/plugin/.     

CytoITMprobe: a network information flow plugin for Cytoscape

ABSTRACT: BACKGROUND: Cytoscape is a well-developed flexible platform for visualization, integration and analysis of network data. Apart from the sophisticated graph layout and visualization routines, it hosts numerous user-developed plugins that significantly extend its core functionality. Earlier, we developed a network information flow framework and implemented it as a web application, called ITM Probe. Given a context consisting of one or more user-selected nodes, ITM Probe retrieves other network nodes most related to that context. It requires neither user restriction to subnetwork of interest nor additional and possibly noisy information. However, plugins for Cytoscape with these features do not yet exist. To provide the Cytoscape users the possibility of integrating ITM Probe into their workflows, we developed CytoITMprobe, a new Cytoscape plugin. FINDINGS: CytoITMprobe maintains all the desirable features of ITM Probe and adds additional flexibility not achievable through its web service version. It provides access to ITM Probe either through a web server or locally. The input, consisting of a Cytoscape network, together with the desired origins and/or destinations of information and a dissipation coefficient, is specified through a query form. The results are shown as a subnetwork of significant nodes and several summary tables. Users can control the composition and appearance of the subnetwork and interchange their ITM Probe results with other software tools through tab-delimited files. CONCLUSIONS: The main strength of CytoITMprobe is its flexibility. It allows the user to specify as input any Cytoscape network, rather than being restricted to the pre-compiled protein-protein interaction networks available through the ITM Probe web service. Users may supply their own edge weights and directionalities. Consequently, as opposed to ITM Probe web service, CytoITMprobe can be applied to many other domains of network-based research beyond protein-networks. It also enables seamless integration of ITM Probe results with other Cytoscape plugins having complementary functionality for data analysis.     

GIANT: A Cytoscape Plugin for Modular Networks

Network analysis provides deep insight into real complex systems. Revealing the link between topological and functional role of network elements can be crucial to understand the mechanisms underlying the system. Here we propose a Cytoscape plugin (GIANT) to perform network clustering and characterize nodes at the light of a modified Guimerà-Amaral cartography. This approach results into a vivid picture of the a topological/functional relationship at both local and global level. The plugin has been already approved and uploaded on the Cytoscape APP store.     

Predicting new molecular targets for rhein using network pharmacology

Background

Drugs can influence the whole biological system by targeting interaction reactions. The existence of interactions between drugs and network reactions suggests a potential way to discover targets. The in silico prediction of potential interactions between drugs and target proteins is of core importance for the identification of new drugs or novel targets for existing drugs. However, only a tiny portion of drug-targets in current datasets are validated interactions. This motivates the need for developing computational methods that predict true interaction pairs with high accuracy. Currently, network pharmacology has used in identifying potential drug targets to predicting the spread of drug activity and greatly contributed toward the analysis of biological systems on a much larger scale than ever before.

Methods

In this article, we present a computational method to predict targets for rhein by exploring drug-reaction interactions. We have implemented a computational platform that integrates pathway, protein-protein interaction, differentially expressed genome and literature mining data to result in comprehensive networks for drug-target interaction. We used Cytoscape software for prediction rhein-target interactions, to facilitate the drug discovery pipeline.

Results

Results showed that 3 differentially expressed genes confirmed by Cytoscape as the central nodes of the complicated interaction network (99 nodes, 153 edges). Of note, we further observed that the identified targets were found to encompass a variety of biological processes related to immunity, cellular apoptosis, transport, signal transduction, cell growth and proliferation and metabolism.

Conclusions

Our findings demonstrate that network pharmacology can not only speed the wide identification of drug targets but also find new applications for the existing drugs. It also implies the significant contribution of network pharmacology to predict drug targets.     

The rice kinase database. A phylogenomic database for the rice kinome

The rice (Oryza sativa) genome contains 1,429 protein kinases, the vast majority of which have unknown functions. We created a phylogenomic database (http://rkd.ucdavis.edu) to facilitate functional analysis of this large gene family. Sequence and genomic data, including gene expression data and protein-protein interaction maps, can be displayed for each selected kinase in the context of a phylogenetic tree allowing for comparative analysis both within and between large kinase subfamilies. Interaction maps are easily accessed through links and displayed using Cytoscape, an open source software platform. Chromosomal distribution of all rice kinases can also be explored via an interactive interface.     

BioNetBuilder2.0: bringing systems biology to chicken and other model organisms

Background

Systems Biology research tools, such as Cytoscape, have greatly extended the reach of genomic research. By providing platforms to integrate data with molecular interaction networks, researchers can more rapidly begin interpretation of large data sets collected for a system of interest. BioNetBuilder is an open-source client-server Cytoscape plugin that automatically integrates molecular interactions from all major public interaction databases and serves them directly to the user's Cytoscape environment. Until recently however, chicken and other eukaryotic model systems had little interaction data available.

Results

Version 2.0 of BioNetBuilder includes a redesigned synonyms resolution engine that enables transfer and integration of interactions across species; this engine translates between alternate gene names as well as between orthologs in multiple species. Additionally, BioNetBuilder is now implemented to be part of the Gaggle, thereby allowing seamless communication of interaction data to any software implementing the widely used Gaggle software. Using BioNetBuilder, we constructed a chicken interactome possessing 72,000 interactions among 8,140 genes directly in the Cytoscape environment. In this paper, we present a tutorial on how to do so and analysis of a specific use case.

Conclusion

BioNetBuilder 2.0 provides numerous user-friendly systems biology tools that were otherwise inaccessible to researchers in chicken genomics, as well as other model systems. We provide a detailed tutorial spanning all required steps in the analysis. BioNetBuilder 2.0, the tools for maintaining its data bases, standard operating procedures for creating local copies of its back-end data bases, as well as all of the Gaggle and Cytoscape codes required, are open-source and freely available at http://err.bio.nyu.edu/cytoscape/bionetbuilder/.

     

Predicting Disease-Related Proteins Based on Clique Backbone in Protein-Protein Interaction Network

Network biology integrates different kinds of data, including physical or functional networks and disease gene sets, to interpret human disease. A clique (maximal complete subgraph) in a protein-protein interaction network is a topological module and possesses inherently biological significance. A disease-related clique possibly associates with complex diseases. Fully identifying disease components in a clique is conductive to uncovering disease mechanisms. This paper proposes an approach of predicting disease proteins based on cliques in a protein-protein interaction network. To tolerate false positive and negative interactions in protein networks, extending cliques and scoring predicted disease proteins with gene ontology terms are introduced to the clique-based method. Precisions of predicted disease proteins are verified by disease phenotypes and steadily keep to more than 95%. The predicted disease proteins associated with cliques can partly complement mapping between genotype and phenotype, and provide clues for understanding the pathogenesis of serious diseases.     

Bioinformatics Analysis of Microarray Data to Reveal Novel Genes Related to Cold-Resistance of Maize

Low temperature has become a major abiotic stress factor that can reduce maize yield and cause a number of economic loss. This study was designed to identify key genes and pathways associated with coldresistance of maize. The gene expression profile GSE46704, including 4 control temperature treated plants and 4 low temperature treated plants, was downloaded from the Gene Expression Omnibus database. Differentially-expressed genes (DEGs) were identified by limma package. Then, protein-protein interaction (PPI) network and module selection were constructed using Cytoscape. Moreover, the DEGs were re-matched based on the Zea mays L. gene ID and symbol data from PlantRegMap. Finally, the re-matched DEGs were performed functional and pathway enrichment analyses by the DAVID online tool. A total of 750 DEGs were screened (including 387 up-regulated and 363 down-regulated genes) In the PPI network, GRMZM2G070837_P01 and GRMZM2G114578_P01 had higher degrees. Besides, carbohydrate metabolic process, starch and sucrose metabolism and biosynthesis of secondary metabolites were significantly enriched in functional and pathway enrichment analysis. GRMZM2G070837_P01 and GRMZM2G114578_P01 might play a critical role in cold-resistance of maize. Meanwhile, carbohydrate metabolic process, starch and sucrose metabolism and biosynthesis of secondary metabolites might function in cold-resistance of maize.     

A network module-based method for identifying cancer prognostic signatures

Discovering robust prognostic gene signatures as biomarkers using genomics data can be challenging. We have developed a simple but efficient method for discovering prognostic biomarkers in cancer gene expression data sets using modules derived from a highly reliable gene functional interaction network. When applied to breast cancer, we discover a novel 31-gene signature associated with patient survival. The signature replicates across 5 independent gene expression studies, and outperforms 48 published gene signatures. When applied to ovarian cancer, the algorithm identifies a 75-gene signature associated with patient survival. A Cytoscape plugin implementation of the signature discovery method is available at http://wiki.reactome.org/index.php/Reactome_FI_Cytoscape_Plugin     

APID2NET: unified interactome graphic analyzer

MOTIVATION: Exploration and analysis of interactome networks at systems level requires unification of the biomolecular elements and annotations that come from many different high-throughput or small-scale proteomic experiments. Only such integration can provide a non-redundant and consistent identification of proteins and interactions. APID2NET is a new tool that works with Cytoscape to allow surfing unified interactome data by querying APID server (http://bioinfow.dep.usal.es/apid/) to provide interactive analysis of protein-protein interaction (PPI) networks. The program is designed to visualize, explore and analyze the proteins and interactions retrieved, including the annotations and attributes associated to them, such as: GO terms, InterPro domains, experimental methods that validate each interaction, PubMed IDs, UniProt IDs, etc. The tool provides interactive graphical representation of the networks with all Cytoscape capabilities, plus new automatic tools to find concurrent functional and structural attributes along all protein pairs in a network. AVAILABILITY: http://bioinfow.dep.usal.es/apid/apid2net.html. SUPPLEMENTARY INFORMATION: Installation Guide and User's Guide are supplied at the Web site indicated above.