TrueSight: a new algorithm for splice junction detection using RNA-seq

Abstract

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.

RNA interference knockdown of DNA methyl-transferase 3 affects gene alternative splicing in the honey bee

“…Detailed description of this application is in ref. 24. Across treated and control samples, RNA-seq yielded robust expression results for 13,008 genes, estimated to comprise 84.9% of the genes predicted from Assembly 4.5 and Official Gene Set 3.2 of the honey bee genome (http:// hymenopteragenome.org/beebase/?q=gbrowse_amel).…”

“…The number of sequencing reads was ∼56-72 M/sample, and the average cDNA length was 220 bp. Reads from each sample were aligned onto the A. mellifera genome assembly v4.5 using TrueSight (24), allowing up to two mismatches for both exonic and junction spanning reads. These reads were mapped to 14,124 genes of 15,314 annotated genes in the Official Gene Set v3.2.…”

A Comprehensive Review of Emerging Computational Methods for Gene Identification

“…The high-throughput RNA-Seq technology provides an extra large volume of transcript data with various splicing sites. Computational methods for predicting alternative splicing sites are highly desirable in understanding the widespread phenomena and exploring the effects of genetic variations on splicing sites [14,[162][163][164][165]. Recent RGASP experiments [153] implicitly suggest that there is a trend in conducting this type of research and indicate the emergence of challenges and competition related to these issues.…”

RNA-Seq: Improving Our Understanding of Retinal Biology and Disease

“…Notably, using RUM, we have identified thousands of novel splicing events in the mouse neural retina, including exon skipping, novel exons, and alternate 3 Splice, PASTA, and TrueSight have been developed to use regression modeling to identify splice junctions, and claim to enhance sensitivity and specificity over traditional splice junction detection methods, but have not been independently verified (Li et al 2013;Tang and Riva 2013;Gatto et al 2014). …”