Compared to healthy individuals, those with stably repressed HIV experience a higher risk of developing insulin resistance, a hallmark of pre-diabetes and a major determinant for cardiometabolic diseases. Although epigenetic processes, including in particular DNA methylation, appear to be dysregulated in individuals with insulin resistance, little is known about where these occur in the genomes of immune cells and the origins of these alterations in HIV-infected individuals. Here, we examined the genome-wide DNA methylation states of monocytes in HIV-infected individuals (n = 37) with varying levels of insulin sensitivity measured by the homeostatic model assessment of insulin resistance (HOMA-IR).
Pubmed ID: 31253200 RIS Download
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A web-based gene list enrichment analysis tool that provides various types of visualization summaries of collective functions of gene lists. It includes new gene-set libraries, an alternative approach to rank enriched terms, and various interactive visualization approaches to display enrichment results using the JavaScript library, Data Driven Documents (D3). The software can also be embedded into any tool that performs gene list analysis. System-wide profiling of genes and proteins in mammalian cells produce lists of differentially expressed genes / proteins that need to be further analyzed for their collective functions in order to extract new knowledge. Once unbiased lists of genes or proteins are generated from such experiments, these lists are used as input for computing enrichment with existing lists created from prior knowledge organized into gene-set libraries.
View all literature mentionsTHIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 5,2022.Tool that predicts interactions between transcription factors and their regulated genes from binding motifs. Understanding vertebrate development requires unraveling the cis-regulatory architecture of gene regulation. PRISM provides accurate genome-wide computational predictions of transcription factor binding sites for the human and mouse genomes, and integrates the predictions with GREAT to provide functional biological context. Together, accurate computational binding site prediction and GREAT produce for each transcription factor: 1. putative binding sites, 2. putative target genes, 3. putative biological roles of the transcription factor, and 4. putative cis-regulatory elements through which the factor regulates each target in each functional role.
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