Table 6.

Advantages, disadvantages and context of bioinformatic tools for the analysis and visualisation of biological pathways and networks.

Tool	Advantages	Disadvantages	Context of Use
Enrich-r [125]	Extensive collection of gene set libraries; visual summaries; user-friendly interface, robust for different types of enrichment analysis including transcription, pathways, ontologies, diseases/drugs, cell types, and miscellaneous.	Overestimated results with large gene sets; lack of ID conversion tools.	Ideal for rapid, interactive analysis of gene/protein sets in transcriptomics and proteomics studies. Useful for identifying pathways and functions associated with diseases, drugs or cell types.
DAVID [126]	Broad taxonomic coverage; up-to-date annotations; gene ID conversion; free; intuitive interface; species parameter for list upload to minimize ambiguity.	General results; based on existing data; requires familiarity with biological databases.	In-depth analyses of gene sets with emphasis on detailed annotations and molecular interactions; comparative and functional studies on different species.
Metascape [127]	Automatic processing and recognition of various gene identifier; auto-clustering; supports multiple flexible file formats; user-friendly interface.	Generic results; may generate too many enriched pathways; limited support for species other than human and mouse.	Complex analyses requiring the integration of multiple omics data. Excellent for studies requiring enrichment analysis and automatic gene clustering.
GSEA [138]	Robust analysis method sensitive to the top and bottom of the gene list; handles large gene sets; support for gene lists from different model organisms.	It requires advanced skills in bioinformatics, systems biology and statistics; computationally intensive, requiring considerable processing resources.	Preferred for studies exploring subtle differences in gene expression between groups of samples, such as comparative studies between healthy and diseased conditions. Useful in oncology and genetics research.
GSVA [129]	pathway-centric analysis of molecular data; supports wide range standard analytical methods (i.e. functional enrichment, survival analysis, clustering); flexibility in input formats.	It requires expertise in R, bioinformatic and statistics; it does not consider correlations between genes, leading to an increased number of false-positive gene sets.	Suitable for pathway analysis in large-scale gene expression data, such as RNA-seq and microarray studies. Useful for studies requiring differentiated expression profile analysis.
Cytoscape [143], [144]	Open-source; runs on all operating systems that support Java; supports an ever-growing number of apps, continuously extending its capabilities and applications.	It requires memory and computational power for large networks; complex analyses may require additional tools (e.g. R/igraph).	Analysis of complex interactions in biological systems (e.g. signalling pathways, protein-protein interactions and gene relationships); integration of multi-omics data; translational research (e.g. understanding the molecular networks involved in various diseases, including cancer and genetic diseases).