Computational Mining of Genesets in Unraveling Angelman Syndrome: A Genomic Exploration

For researchers and clinicians, Angelman Syndrome, a rare neurodevelopmental condition with a complex genetic etiology, poses a substantial challenge. Although UBE3A gene mutations have historically been the disorder's main causes, recent developments in genomics have shown a more complex genetic landscape. This article provides a thorough investigation of the genetic causes of AS by delving into the newly emerging field of computational mining of genesets. This study extensively covers the computational approaches used in AS research, drawing on a large body of literature and the most recent breakthroughs in the field. It emphasizes how several omics data sources, including genomes, transcriptomics, and epigenomics, must be integrated in order to build complete genesets connected to AS. These genesets go beyond UBE3A and contain other genes that add to the complexity of the condition. Notably, the extended geneset network has revealed previously unknown functional linkages and interactions thanks to computational mining approaches such gene set enrichment analysis, network analysis, and machine learning. These findings' implications for the pathophysiology of AS and prospective treatment options are examined. The epigenetic alterations and transcriptional dysregulation that play a role in AS are also highlighted in this review, illustrating the syndrome's complex hereditary components. It highlights the need for a systems biology approach to fully understand the genetic basis of the disease.