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/.
CONTACT: michael.zhang@utdallas.edu.
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics 
online.