The advent of high-throughput technologies — next-generation DNA and RNA sequencing, DNA microarrays, mass spectrometry applied to proteomics — generates considerable volumes of data that enable the global study of physiological and pathological states. While the analysis of gene expression levels reveals profiles specific to certain cell types and their variations during development or disease, proteomic approaches interrogate the entire protein repertoire of an organism and shed light on the interactions between proteins involved in diverse cellular functions. Yet biological processes are inherently multidimensional, integrating several levels of information: genes encoding multiple proteins, which are themselves engaged in numerous pathways and subject to complex post-translational modifications. Interpreting such high-dimensional datasets rapidly and efficiently represents a genuine challenge.
It is this difficulty that the ClueGO software addresses — an application developed for the Cytoscape environment and designed to extract representative functional biological information from long lists of genes or proteins. Functional enrichment analysis relies on the most recent public data, drawn from multiple annotation and ontology resources that the software accesses automatically, including the Gene Ontology as well as metabolic and signaling pathway databases such as KEGG, Reactome, and WikiPathways. Predefined settings facilitate the selection of relevant terms and simplify the conduct of the analysis, even for users who are not bioinformatics specialists.
The results are rendered as networks in which Gene Ontology terms and biological pathways are grouped according to their functional role, providing a synthetic overview of the information underlying large lists of molecules. ClueGO now supports many species, and additional organisms can be added upon request. The software can also be combined with the CluePedia application, which allows the visualization of protein-protein interactions within a single pathway or across several pathways. The article describes a typical workflow applied to a large dataset and answers questions frequently raised by users of the software.
The authors note that the integration of transcriptomic and proteomic data can illuminate new facets of biological and cellular functions, contributing to the identification of therapeutic targets and predictive biomarkers for improved disease classification. They also emphasize that the quality of these analyses depends on the currency of the annotations used, a decisive point for the reliability of functional enrichments.