Conclusions

Conclusions

We propose a new RNA-seq gapped alignment algorithm named as TrueSight, incorporating both DNA sequence and RNA-seq features into a logistic regression model which assigns reliability scores to SJs inferred by RNA-seq reads. To our knowledge, TrueSight is the first method with the ability to combine RNA-seq mapping with genome wide splicing signal and coding potential computation from the DNA sequence. Testing on both real and simulation datasets, TrueSight has shown an overall better performance than existing tools in terms of sensitivity and specificity, demonstrating that DNA information is helpful in RNA-seq alignment, especially in SJs with low coverage. Since many AS isoforms are of low coverage, we expect TrueSight useful in AS detection. Mapping RNA-seq reads to SJs is the pivotal point in an algorithm of isoform construction utilizing a reference genome. For example, in IsoLasso (Li et al.,2011a), a recent isoform construction algorithm using TopHat’s output, inferred SJs are explicitly used to significantly reduce the total number of possible isoforms subjected to the LASSO procedure. We expect that TrueSight will improve isoform construction and consequently will make a strong impact on improving the accuracy of estimating isoform expression levels.

There are several other features that we could incorporate in order to further improve the algorithm. First, we could add a module to explicitly model SJs in untranslated region (UTR). Second, we could use the three-periodic model to trace exon reading frames locally; this will enhance the modeling of SJs in coding regions and will reduce the number of pairs of candidate splice sites for a SJ to those that do not disrupt the reading frame. These models could be made GC content dependent and thus more accurate.

With the aid of TrueSight which is designed to discover SJs, we perform AS analysis to honey bee, a social insect without intensive gene annotation, using deep RNA-seq data. We have identified 22,150 instances of AS on 8,852 genes, indicating that 94.6% multi-exon genes in honey bee can code for multiple transcripts. RI and AB are the most dominant AS

subtypes in honey bee. Also, we quantitatively analyzed various factors correlated with AS based on the high coverage sequencing data. The exon-intron structure, strengths of splice sites and the methylation pattern are shown to be involved in cells’ AS decision. Specifically, from observation in CE, AB and ATE, rarely used exon sequences are found presumably to be hypo-methylated, while normal honey bee exons are hyper-methylated. The motif strength of splice site is essential in splice site recognition, and weak sites may disrupt the recognition if introns, exons, 3’/5’ alternative exon regions and terminal exons, leading to RIs, CEs, ABs and ATEs respectively. RIs are relatively shorter than non-retaining introns, however, longer RIs are more likely to be retained than shorter RIs. This observation supports both sides of intron splicing model: (i) splice site mis-recognition in short introns will lead to RI, and CE would emerge if splice site mis-recognition happened on long introns. (ii) short introns are relatively easier to be spliced out than longer ones. The size of CEs and ABs have a significant 3-periodic enrichment, suggesting (i) coding frame preserving AS might have selective advantage in evolution; (ii) nonsense-mediated decay (NMD) might turned over frame shifting AS transcripts which have premature termination codons.

AS often contributes to major developmental decisions and understanding the structure of splice-forms of a gene is essential for uncovering its AS behavior. We have examined the AS of fruitless, a well-established sex-specific AS gene in Drosophila. With the aid of refined honey bee gene model, we identified three first exons, five last exons, a cassette exon and two alternative 5’ splice sites for fruitless. The refined fruitless model would be useful in sex-determination study for honey bee.

Using the gapped alignment module within TrueSight, we designed and implemented an open-source software tool, called FusionHunter, which reliably identifies fusion transcripts from transcriptional analysis of paired-end RNA-seq. We show that FusionHunter can accurately detect fusions that were previously confirmed by RT-PCR in a publicly available dataset. The purpose of FusionHunter is to identify potential fusions with high sensitivity and specificity and to guide further functional validation in the laboratory.

As the cost of next-generating sequencing keeps going down, more and more whole transcriptome sequencing datasets for different organisms will be available. Bioinformatics tools are increasingly important in deciphering the rich biological information behind these high-throughput digital readouts, and broadening our understanding of the transcriptomic behavior of cells.

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