SpliceTrap: a method to quantify alternative splicing under single cellular conditions. Motivation: Alternative splicing (AS) is a pre-mRNA maturation process leading to the expression of multiple mRNA variants from the same primary transcript. More than 90% of human genes are expressed via AS. Therefore, quantifying the inclusion level of every exon is crucial for generating accurate transcriptomic maps and studying the regulation of AS. Results: Here we introduce SpliceTrap, a method to quantify exon inclusion levels using paired-end RNA-seq data. Unlike other tools, which focus on full-length transcript isoforms, SpliceTrap approaches the expression-level estimation of each exon as an independent Bayesian inference problem. In addition, SpliceTrap can identify major classes of alternative splicing events under a single cellular condition, without requiring a background set of reads to estimate relative splicing changes. We tested SpliceTrap both by simulation and real data analysis, and compared it to state-of-the-art tools for transcript quantification. SpliceTrap demonstrated improved accuracy, robustness and reliability in quantifying exon-inclusion ratios. Conclusions: SpliceTrap is a useful tool to study alternative splicing regulation, especially for accurate quantification of local exon-inclusion ratios from RNA-seq data. Availability and Implementation: SpliceTrap can be implemented online through the CSH Galaxy server http://cancan.cshl.edu/splicetrap and is also available for download and installation at http://rulai.cshl.edu/splicetrap/.
Keywords for this software
References in zbMATH (referenced in 3 articles )
Showing results 1 to 3 of 3.
- Ruddy, Sean; Johnson, Marla; Purdom, Elizabeth: Shrinkage of dispersion parameters in the binomial family, with application to differential exon skipping (2016)
- Rossell, David; Attolini, Camille Stephan-Otto; Kroiss, Manuel; Stöcker, Almond: Quantifying alternative splicing from paired-end RNA-sequencing data (2014)
- Rodríguez-Ezpeleta, Naiara (ed.); Hackenberg, Michael (ed.); Aransay, Ana M. (ed.): Bioinformatics for high throughput sequencing (2012)