GAC: A Shiny based interface for analyzing age-associated gene expression in humans

Abstract

Aging is a risk factor for almost every major disease and health issue, but its underlying mechanisms are still poorly defined. One way to improve our understanding of the aging process is to identify genes that change in expression with age. Massive databases of high-throughput gene expression data are publically available, and many software tools have been developed to interpret this data. However, these tools tend to be inaccessible to biologists who lack programming experience. As a solution, we present GAC, a user-friendly Shiny based interface that identifies age-associated genes in expression data and performs gene set analyses. To demonstrate the usability of the application and the viability of its results, we perform a case study examining age-associated genes in females between ages 30 and 40. We find that GAC is capable of finding age-associated genes relevant in literature, implicating its utility in discovering new age-associated genes.