Self-training algorithm for splice junction detection using RNA-seq and its application to honey bee transcriptomes

RNA-seq has proven to be a powerful technique for transcriptome profiling based on next-generation sequencing (NGS) technologies. However, due to the short length of NGS reads, it is challenging to accurately map RNA-seq reads to splice junctions (SJs), which is a critically important step in the analysis of alternative splicing (AS) and isoform construction. In this article, we describe a new method, called TrueSight, which for the first time combines RNA-seq read mapping quality and coding potential of genomic sequences into a unified model. The model is further utilized in a machine-learning approach to precisely identify SJs. Both simulations and real data evaluations showed that TrueSight achieved higher sensitivity and specificity than other methods. We applied TrueSight to new high coverage honey bee RNA-seq data to discover novel splice forms. We found that 60.3% of honey bee multi-exon genes are alternatively spliced. By utilizing gene models improved by TrueSight, we characterized AS types in honey bee transcriptome. We believe that TrueSight will be highly useful to comprehensively study the biology of alternative splicing.