NetworkAnalyst - integrative approaches for protein–protein interaction network analysis and visual exploration

By Jianguo Xia, Maia J. Benner, Robert E. W. Hancock. Nucleic Acids Res., 2014

417 citations recorded by the source corpus.

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.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/be6b7fb3c8eb7b8610497042ff6de0a5b8d770bb

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In Molecular Biology

Citations in the corpus

NetworkAnalyst 3.0: a visual analytics platform for comprehensive gene expression profiling and meta-analysis (2019, background): “Other features can be found in our prior publications (1,2,4,5).”
Artificial intelligence in cancer target identification and drug discovery (2022, methodology): “Arti ﬁ cial intelligence biology analysis applications 188 usually use omics data to build networks and identify co-expression modules of genes, proteins, metabolites, critical pathways between molecules, and key molecules in biological networks 189 .”