07-26, 11:55–12:03 (US/Eastern), Tomatis Auditorium
Functional enrichment analysis has emerged as a valuable bioinformatics approach to investigate large-scale genomic, proteomic and transcriptomic datasets. This method assesses the over-representation of functional terms within a query dataset, leveraging biological databases such as Reactome, KEGG, TRANSFAC, and GO. Several tools, including Enrichr, David, g:Profiler and clusterProfiler, have been developed to facilitate this analysis.
The output of functional enrichment analyses often consists of extensive lists of significantly over-represented terms. To enhance biological interpretation, these terms are frequently visualized using representations such as UpSet plots, bar charts, and dot plots. In contrast, network-based visualization provides a distinct advantage over these visual representations as it highlights relationships and potentially uncovers underlying biologically-relevant patterns and clusters. In network-based visualization, the information is often represented as two major object types: nodes and edges. The nodes usually represent entities while edges represent the interaction between those entities.
The Bioconductor enrichViewNet package enables the visualization of enrichment results as biological network graphs. The package generates two types of network graphs: customizable gene-term networks and enrichment maps. The input format corresponds to the enrichment result format generated by the gprofiler2 package, a R client package for the g:Profiler web server. We chose the g:Profiler R client due to its fluid integration with the enrichViewNet package into a R pipeline, its popularity, as well as the continuous updating of its knowledge database.
The first type of network graphs generated by the enrichViewNet package is a gene-term network. In gene-term networks, genes and functional terms are both represented as nodes, with edges connecting genes that are part of a given functional term. The networks are automatically loaded into Cytoscape software with the Bioconductor RCy3 package. Those graphs allow for the rapid exploration of gene-term relationships and identification of key functional groups as well as the personalisation of the network elements in Cytoscape.
Enrichment maps, on the other hand, represent functional enriched terms as nodes connected by edges when these share a minimal ratio of elements (as specified by user). The enrichViewNet package incorporates functions from the Bioconductor enrichplot package to generate those maps. This approach allows for the visualization of intricate relationships between terms. Furthermore, results from more than one functional enrichment analyses can be visualized on the same network graph.
To illustrate the positive benefit of enrichViewNet, we analyzed the protein-protein interactions of the inhibitory receptor, CLEC12A, identified in HEK293 cells by quantitative iTRAQ proteomics. The enrichViewNet package revealed a complex network of interactions that highlighted the potential roles for CLEC12A in cellular processes not previously associated with this myeloid inhibitory receptor.
In conclusion, the enrichViewNet package is a flexible tool for the visualization of functional enrichment analysis results as biological network graphs. Its seamless integration with R pipeline, coupled with its ability to generate insightful network representations, makes it a valuable addition to the toolkit of biological researchers.
The enrichViewNet package is available on Bioconductor: https://bioconductor.org/packages/enrichViewNet
I have an academic background in both computer science and engineering. I have had several professional experiences in various environments such as research centers and private companies. All those assignments have it common that they are related to research and development. I have accumulated more than 10 years of experience as a computational analyst, and I have had the opportunity to work in various fields such as plant genomics, epigenetic inheritance, and cancer. Since joining the Tuveson Laboratory at Cold Spring Harbor Laboratory, I have been collaborating with biologists to conduct rigorous computational analyses in order to achieve a better understanding of pancreatic cancer mechanisms. I am particularly interested in the development of novel bioinformatics methods and software.