Abstract
The growing application of gene expression profiling demands powerful yet user-friendly bioinformatics tools to support systems-level data understanding. NetworkAnalyst was first released in 2014 to address the key need for interpreting gene expression data within the context of protein-protein interaction (PPI) networks. It was soon updated for gene expression meta-analysis with improved workflow and performance. Over the years, NetworkAnalyst has been continuously updated based on community feedback and technology progresses. Users can now perform gene expression profiling for 17 different species. In addition to generic PPI networks, users can now create cell-type or tissue specific PPI networks, gene regulatory networks, gene co-expression networks as well as networks for toxicogenomics and pharmacogenomics studies. The resulting networks can be customized and explored in 2D, 3D as well as Virtual Reality (VR) space. For meta-analysis, users can now visually compare multiple gene lists through interactive heatmaps, enrichment networks, Venn diagrams or chord diagrams. In addition, users have the option to create their own data analysis projects, which can be saved and resumed at a later time. These new features are released together as NetworkAnalyst 3.0, freely available at https://www.networkanalyst.ca.
Highlights
The scientific community is in the midst of a boom of transcriptomics yet there are few accepted and standardized bioinformatics tools to organize, analyze, visualize and interpret the resulting big data
Nucleic Acids Research, 2019, Vol 47, Web Server issue W235 the advanced level, we have spent most of our efforts on developing and improving visual analytics methods to address several key challenges in biological big data analysis
After ID checking and conversion, users can visually compare different lists and perform enrichment analysis on a subset of genes generated from different set operations using multiple visual analytics tools
Summary
The scientific community is in the midst of a boom of transcriptomics yet there are few accepted and standardized bioinformatics tools to organize, analyze, visualize and interpret the resulting big data. We introduce NetworkAnalyst 3.0 as a powerful web-based visual analytics platform for comprehensive profiling, meta-analysis and systems-level interpretation of gene expression data. Nucleic Acids Research, 2019, Vol 47, Web Server issue W235 the advanced level, we have spent most of our efforts on developing and improving visual analytics methods to address several key challenges in biological big data analysis.
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