BubbleGUM is executable through an intuitive interface so that both bioinformaticians and biologists can use it. This innovative methodology has recently been used to answer important questions in functional genomics, such as the degree of similarities between microarray datasets from different laboratories or with different experimental models or clinical cohorts. Enrichments are displayed in a graphical output that helps interpreting the results.
#Mrs. bubblegym software#
ConclusionsīubbleGUM is an open-source software that allows to automatically generate molecular signatures out of complex expression datasets and to assess directly their enrichment by GSEA on independent datasets. This analysis allowed us to confirm homologies between mouse and human immunocytes. We applied our method to generate transcriptomic fingerprints for murine cell types and to assess their enrichments in human cell types. One original feature of BubbleGUM notably resides in its capacity to integrate and compare numerous GSEA results into an easy-to-grasp graphical representation. We developed BubbleGUM (GSEA Unlimited Map), a tool that allows to automatically extract molecular signatures from transcriptomic data and perform exhaustive GSEA with multiple testing correction. Thus, there is a crucial need for an easy-to-use software for generation of relevant home-made gene sets from complex datasets, their use in GSEA, and the correction of the results when applied to multiple comparisons of many experimental conditions. This is all the more the case when attempting to define a gene set specific of one condition compared to many other ones. However, although many gene sets covering a large panel of biological fields are available in public databases, the ability to generate home-made gene sets relevant to one’s biological question is crucial but remains a substantial challenge to most biologists lacking statistic or bioinformatic expertise. This considerably improves extraction of information from high-throughput gene expression data. For example, the popular Gene Set Enrichment Analysis (GSEA) algorithm can detect moderate but coordinated expression changes of groups of presumably related genes between pairs of experimental conditions. Recent advances in the analysis of high-throughput expression data have led to the development of tools that scaled-up their focus from single-gene to gene set level.
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