![]() The full comprehension of the complex interactions taking place in cellular processes requires methods that are able to grasp the connections between the genes involved. The study of biological functions through discrete genes analysis methods has allowed the elucidation of numerous pathways and the understanding of gene-disease associations. GWENA goes beyond existing packages that perform co-expression analysis by including new tools to fully characterize modules, such as differential co-expression, additional enrichment databases, and network visualization. Thanks to biological and topological information as well as differential co-expression, the package helps to dissect the role of genes relationships in diseases conditions or targeted phenotypes. GWENA is an R package available through Bioconductor ( ) that has been developed to perform extended analysis of gene co-expression networks. The known phenomena of connectivity loss associated with aging was found coupled to a global reorganization of the relationships leading to expression of known aging related functions. Moreover, new insights on the variations in patterns of co-expression were identified. Remarkably, we prioritized a gene whose involvement was unknown in the muscle development and growth. To demonstrate its performance, we applied GWENA on two skeletal muscle datasets from young and old patients of GTEx study. Here we present GWENA, a new R package that integrates gene co-expression network construction and whole characterization of the detected modules through gene set enrichment, phenotypic association, hub genes detection, topological metric computation, and differential co-expression. ![]() ![]() wild vs mutant) are the main methods to do so, but to date no tool combines them all into a single pipeline. Biological integration, topology study and conditions comparison (e.g. An extended description of each of the network modules is therefore a critical step to understand the underlying processes contributing to a disease or a phenotype. Network-based analysis of gene expression through co-expression networks can be used to investigate modular relationships occurring between genes performing different biological functions. ![]()
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