JunctionSeq – detection and visualization of differential splicing in RNA-Seq data

Although RNA-Seq data provide unprecedented isoform-level expression information, detection of alternative isoform regulation (AIR) remains difficult, particularly when working with an incomplete transcript annotation. Researchers at the National Human Genome Research Institute introduce JunctionSeq, a new method that builds on the statistical techniques used by the well-established DEXSeq package to detect differential usage of both exonic regions and splice junctions. In particular, JunctionSeq is capable of detecting differential usage of novel splice junctions without the need for an additional isoform assembly step, greatly improving performance when the available transcript annotation is flawed or incomplete. JunctionSeq also provides a powerful and streamlined visualization toolset that allows bioinformaticians to quickly and intuitively interpret their results.

The researchers tested their method on publicly available data from several experiments performed on the rat pineal gland and Toxoplasma gondii, successfully detecting known and previously validated AIR genes in 19 out of 19 gene-level hypothesis tests. Due to its ability to query novel splice sites, JunctionSeq is still able to detect these differences even when all alternative isoforms for these genes were not included in the transcript annotation. JunctionSeq thus provides a powerful method for detecting alternative isoform regulation even with low-quality annotations.

Genome-wide Browser tracks produced in the QoRTs/JunctionSeq pipeline.


The above screenshot displays much of the same information found in Figure 4, except using the UCSC genome browser. The top track displays the ensembl gene annotation. The second track displays the statistically significant features, with the adjusted P-value included in parentheses. The next track is a ‘wiggle’ track that displays coverage over both the forward and reverse strand (above and below the x-axis, respectively), in red and blue for day and night, respectively (overlap is colored black). The next two tracks display all exons and splice junctions, respectively, that were tested for DU by JunctionSeq. The day/night normalized mean expression values from Figure 4 are included in parentheses. The final track is from RepeatMasker, and displays regions with repeating or low-complexity elements. Using these tracks together can be vital for the purposes of interpretation and validation.

AvailabilityJunctionSeq will be included in Bioconductor release 3.3 (http://bioconductor.org/packages/JunctionSeq/), and is available now along with additional online help and documentation at the JunctionSeq GitHub page: http://hartleys.github.io/JunctionSeq/.

Hartley SW, Mullikin JC. (2016) Detection and visualization of differential splicing in RNA-Seq data with JunctionSeq. Nucleic Acids Res [Epub ahead of print]. [article]

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