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

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dc.contributor.authorAghamirzaie, Delasaen
dc.contributor.authorCollakova, Evaen
dc.contributor.authorLi, Songen
dc.contributor.authorGrene, Ruthen
dc.contributor.departmentFralin Life Sciences Instituteen
dc.contributor.departmentSchool of Plant and Environmental Sciencesen
dc.date.accessioned2019-11-13T13:49:28Zen
dc.date.available2019-11-13T13:49:28Zen
dc.date.issued2016en
dc.description.abstractBackground: 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. Results: 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 functionrelated 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 RNAbinding 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. Conclusions: 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/.en
dc.description.sponsorshipThis work was supported by funding from NSF-MCB-1052145, NSF-ABI- 1062472, and the Genomics, Bioinformatics, and Computational Biology Graduate Program at Virginia Tech. Funding for this work was also provided in part by the Virginia Agricultural Experiment Station and the Hatch Program of the NIFA, USDA.en
dc.format.extent16 pagesen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationAghamirzaie et al. BMC Genomics (2016) 17:845 DOI 10.1186/s12864-016-3172-6en
dc.identifier.doihttps://doi.org/10.1186/s12864-016-3172-6en
dc.identifier.urihttp://hdl.handle.net/10919/95531en
dc.identifier.volume17en
dc.language.isoenen
dc.publisherBMCen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.titleCoSpliceNet: a framework for co-splicing network inference from transcriptomics dataen
dc.title.serialBMC Genomicsen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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