2026-04-17T08:09:18Zhttps://recercat.cat/oai/request

oai:recercat.cat:10230/252212025-12-13T21:20:04Zcom_2072_6col_2072_452952

00925njm 22002777a 4500 dc Papasaikas, Panagiotis author Rao, Arvind author Huggins, Peter author Valcárcel, J. (Juan) author López Aldea, Javier author 2015-11-25T17:22:29Z 2015-11-25T17:22:29Z 2015 We present a computational framework tailored for the modeling of the complex, dynamic relationships that are encountered in splicing regulation. The starting point is whole-genome transcriptomic data from high-throughput array or sequencing methods that are used to quantify gene expression and alternative splicing across multiple contexts. This information is used as input for state of the art methods for Graphical Model Selection in order to recover the structure of a composite network that simultaneously models exon co-regulation and their cognate regulators. Community structure detection and social network analysis methods are used to identify distinct modules and key actors within the network. As a proof of concept for our framework we studied the splicing regulatory network for Drosophila development using the publicly available modENCODE data. The final model offers a comprehensive view of the splicing circuitry that underlies fly development. Identified modules are associated with major developmental hallmarks including maternally loaded RNAs, onset of zygotic gene expression, transitions between life stages and sex differentiation. Within-module key actors include well-known developmental-specific splicing regulators from the literature while additional factors previously unassociated with developmental-specific splicing are also highlighted. Finally we analyze an extensive battery of Splicing Factor knock-down transcriptome data and demonstrate that our approach captures true regulatory relationships. Exons Empalmament (Genètica) Splicing Regulatory Network Module Exon Graphical Model mRNA Processing Splicing Factor Regulator Development Reconstruction of composite regulator-target splicing networks from high-throughput transcriptome data