title: CoSpliceNet: a framework for co-splicing network inference from transcriptomics data.
abstract: Alternative splicing has been proposed to increase transcript diversity and protein plasticity in eukaryotic organisms, but the extent to which this is the case is currently unclear, especially with regard to the diversification of molecular function. Eukaryotic splicing involves complex interactions of splicing factors and their targets. Inference of co-splicing networks capturing these types of interactions is important for understanding this crucial, highly regulated post-transcriptional process at the systems level.
  
  
  
  First, several transcript and protein attributes, including coding potential of transcripts and differences in functional domains of proteins, were compared between splice variants and protein isoforms to assess transcript and protein diversity in a biological system. Alternative splicing was shown to increase transcript and function-related protein diversity in developing Arabidopsis embryos. Second, CoSpliceNet, which integrates co-expression and motif discovery at splicing regulatory regions to infer co-splicing networks, was developed. CoSpliceNet was applied to temporal RNA sequencing data to identify candidate regulators of splicing events and predict RNA-binding motifs, some of which are supported by prior experimental evidence. Analysis of inferred splicing factor targets revealed an unexpected role for the unfolded protein response in embryo development.
  
  
  
  The methods presented here can be used in any biological system to assess transcript diversity and protein plasticity and to predict candidate regulators, their targets, and RNA-binding motifs for splicing factors. CoSpliceNet is freely available at http://delasa.github.io/co-spliceNet/ .
authors: Genetics, Bioinformatics and Computational Biology, Virginia Tech, Blacksburg, VA, 24061, USA. delasa@vt.edu.
authors: Delasa
authors: D
authors: Aghamirzaie
authors: Department of Plant Pathology, Physiology, and Weed Science, Virginia Tech, Blacksburg, VA, 24061, USA.
authors: Eva
authors: E
authors: Collakova
authors: Genetics, Bioinformatics and Computational Biology, Virginia Tech, Blacksburg, VA, 24061, USA.|Department of Crop and Soil Environmental Sciences, Virginia Tech, Blacksburg, VA, 24061, USA.
authors: Song
authors: S
authors: Li
authors: Genetics, Bioinformatics and Computational Biology, Virginia Tech, Blacksburg, VA, 24061, USA.|Department of Plant Pathology, Physiology, and Weed Science, Virginia Tech, Blacksburg, VA, 24061, USA.
authors: Ruth
authors: R
authors: Grene
mesh_terms: D017398:Alternative Splicing; D017360:Arabidopsis / Q000235:genetics / Q000378:metabolism; D019295:Computational Biology / Q000379:methods*; D047108:Embryonic Development / Q000235:genetics; D005786:Gene Expression Regulation; D018507:Gene Expression Regulation, Developmental; D018506:Gene Expression Regulation, Plant; D053263:Gene Regulatory Networks; D020033:Protein Isoforms; D012326:RNA Splicing; D012639:Seeds / Q000235:genetics / Q000378:metabolism; D014158:Transcription, Genetic
chemical_list: D020033:Protein Isoforms