spliceR: an R package for classification of alternative splicing and prediction of coding potential from RNA-seq data
1 Department of Biology, The Bioinformatics Centre, University of Copenhagen, Ole Maaloes Vej 5, Copenhagen, DK2200, Denmark
2 Biotech Research and Innovation Centre (BRIC), University of Copenhagen, Ole Maaloes Vej 5, Copenhagen, DK-2200, Denmark
3 The Finsen Laboratory, Rigshospitalet, Faculty of Health Sciences, University of Copenhagen, Ole Maaloes Vej 5, Copenhagen, DK2200, Denmark
4 The Danish Stem Cell Centre (DanStem) Faculty of Health Sciences, University of Copenhagen, Ole Maaloes Vej 5, Copenhagen, DK2200, Denmark
BMC Bioinformatics 2014, 15:81 doi:10.1186/1471-2105-15-81Published: 23 March 2014
RNA-seq data is currently underutilized, in part because it is difficult to predict the functional impact of alternate transcription events. Recent software improvements in full-length transcript deconvolution prompted us to develop spliceR, an R package for classification of alternative splicing and prediction of coding potential.
spliceR uses the full-length transcript output from RNA-seq assemblers to detect single or multiple exon skipping, alternative donor and acceptor sites, intron retention, alternative first or last exon usage, and mutually exclusive exon events. For each of these events spliceR also annotates the genomic coordinates of the differentially spliced elements, facilitating downstream sequence analysis. For each transcript isoform fraction values are calculated to identify transcript switching between conditions. Lastly, spliceR predicts the coding potential, as well as the potential nonsense mediated decay (NMD) sensitivity of each transcript.
spliceR is an easy-to-use tool that extends the usability of RNA-seq and assembly technologies by allowing greater depth of annotation of RNA-seq data. spliceR is implemented as an R package and is freely available from the Bioconductor repository ( http://www.bioconductor.org/packages/2.13/bioc/html/spliceR.html webcite).