NetworkAnalyst - integrative approaches for protein–protein interaction network analysis and visual exploration
TLDR
NetworkAnalyst, taking advantage of state-of-the-art web technologies, is developed, to enable high performance network analysis with rich user experience and presents the results via a powerful online network visualization framework.Abstract:
Biological network analysis is a powerful approach to gain systems-level understanding of patterns of gene expression in different cell types, disease states and other biological/experimental conditions. Three consecutive steps are required - identification of genes or proteins of interest, network construction and network analysis and visualization. To date, researchers have to learn to use a combination of several tools to accomplish this task. In addition, interactive visualization of large networks has been primarily restricted to locally installed programs. To address these challenges, we have developed NetworkAnalyst, taking advantage of state-of-the-art web technologies, to enable high performance network analysis with rich user experience. NetworkAnalyst integrates all three steps and presents the results via a powerful online network visualization framework. Users can upload gene or protein lists, single or multiple gene expression datasets to perform comprehensive gene annotation and differential expression analysis. Significant genes are mapped to our manually curated protein-protein interaction database to construct relevant networks. The results are presented through standard web browsers for network analysis and interactive exploration. NetworkAnalyst supports common functions for network topology and module analyses. Users can easily search, zoom and highlight nodes or modules, as well as perform functional enrichment analysis on these selections. The networks can be customized with different layouts, colors or node sizes, and exported as PNG, PDF or GraphML files. Comprehensive FAQs, tutorials and context-based tips and instructions are provided. NetworkAnalyst currently supports protein-protein interaction network analysis for human and mouse and is freely available at http://www.networkanalyst.ca.read more
Citations
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Journal ArticleDOI
NetworkAnalyst 3.0: a visual analytics platform for comprehensive gene expression profiling and meta-analysis.
TL;DR: The growing application of gene expression profiling demands powerful yet user-friendly bioinformatics tools to support systems-level data understanding and NetworkAnalyst was first released in 2014 to address the key need for interpreting gene expression data within the context of protein-protein interaction networks.
Journal ArticleDOI
NetworkAnalyst for statistical, visual and network-based meta-analysis of gene expression data
TL;DR: This protocol provides a step-wise description of how to effectively use NetworkAnalyst to perform network analysis and visualization from gene lists; to perform meta- analysis on gene expression data while taking into account multiple metadata parameters; and, finally, to perform a meta-analysis of multiple gene expressionData sets.
Journal ArticleDOI
Gaining insight into exclusive and common transcriptomic features linked with biotic stress responses in Malus.
TL;DR: This study represents a first preliminary curated meta-analysis of apple transcriptomic responses to biotic stresses and discovered the presence of several proteins affected by more than one biotic stress including a WRKY40 and some highly interactive proteins such as heat shock proteins.
Journal ArticleDOI
miRNet - dissecting miRNA-target interactions and functional associations through network-based visual analysis.
TL;DR: MiRNet as mentioned in this paper is an easy-to-use web-based tool that offers statistical, visual and network-based approaches to help researchers understand miRNAs functions and regulatory mechanisms.
Journal ArticleDOI
Sensitization to immune checkpoint blockade through activation of a STAT1/NK axis in the tumor microenvironment
Rachael M. Zemek,Emma de Jong,Wee Loong Chin,Wee Loong Chin,Iona S. Schuster,Iona S. Schuster,Iona S. Schuster,Vanessa S. Fear,Thomas H. Casey,Cath Forbes,Sarah Dart,Connull Leslie,Ayham Zaitouny,Ayham Zaitouny,Michael Small,Michael Small,Louis Boon,Alistair R. R. Forrest,Daithi O. Muiri,Mariapia A. Degli-Esposti,Mariapia A. Degli-Esposti,Mariapia A. Degli-Esposti,Michael Millward,Michael Millward,Anna K. Nowak,Anna K. Nowak,Timo Lassmann,Anthony Bosco,Richard A. Lake,W. Joost Lesterhuis +29 more
TL;DR: The results identify a pretreatment tumor microenvironment that predicts response to ICB, which can be therapeutically attained and suggest a biomarker-driven approach to patient management to establish whether a patient would benefit from treatment with sensitizing therapeutics before ICB.
References
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TL;DR: Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
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STRING v9.1: protein-protein interaction networks, with increased coverage and integration
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TL;DR: The update to version 9.1 of STRING is described, introducing several improvements, including extending the automated mining of scientific texts for interaction information, to now also include full-text articles, and providing users with statistical information on any functional enrichment observed in their networks.