CoSpliceNet: a framework for co-splicing network inference from transcriptomics data

摘要: Alternative splicing has been proposed to increase transcript diversity and protein plasticity in eukaryotic organisms, but the extent which this is case currently unclear, especially with regard diversification of molecular function. Eukaryotic involves complex interactions factors their targets. Inference co-splicing networks capturing these types important for understanding crucial, highly regulated post-transcriptional process at systems level. First, several attributes, including coding potential transcripts differences functional domains proteins, were compared between splice variants isoforms assess a biological system. was shown function-related developing Arabidopsis embryos. Second, CoSpliceNet, integrates co-expression motif discovery regulatory regions infer networks, developed. CoSpliceNet applied temporal RNA sequencing data identify candidate regulators events predict RNA-binding motifs, some are supported by prior experimental evidence. Analysis inferred factor targets revealed an unexpected role unfolded response embryo development. The methods presented here can be used any system regulators, targets, motifs factors. freely available http://delasa.github.io/co-spliceNet/ .