Abstract
Visualisation of the transcriptome relative to a reference genome is fraught with sparsity. This is due to RNA sequencing (RNA-Seq) reads being predominantly mapped to exons that account for just under 3% of the human genome. Recently, we have used exon-only references, superTranscripts, to improve visualisation of aligned RNA-Seq data through the omission of supposedly unexpressed regions such as introns. However, variation within these regions can lead to novel splicing events that may drive a pathogenic phenotype. In these cases, the loss of information in only retaining annotated exons presents significant drawbacks. Here we present Slinker, a bioinformatics pipeline written in Python and Bpipe that uses a data-driven approach to assemble sample-specific superTranscripts. At its core, Slinker uses Stringtie2 to assemble transcripts with any sequence across any gene. This assembly is merged with reference transcripts, converted to a superTranscript, of which rich visualisations are made through Plotly with associated annotation and coverage information. Slinker was validated on five novel splicing events of rare disease samples from a cohort of primary muscular disorders. In addition, Slinker was shown to be effective in visualising deletion events within transcriptomes of tumour samples in the important leukemia gene, IKZF1. Slinker offers a succinct visualisation of RNA-Seq alignments across typically sparse regions and is freely available on Github.
Highlights
Genomic variants often carry through to the transcriptome
DNA variants can be identified through genome sequencing and transcriptional disruption can be measured by RNA sequencing (RNA-Seq)
In an effort to provide succinct representations of RNA-Seq data, we previously developed the concept of superTranscripts.[10]
Summary
Slinker: Visualising novel splicing events in RNA-Seq data [version 1; peer review: 3 approved]. Breon Schmidt 1,2, Marek Cmero[1,3,4], Paul Ekert[1,3,5,6,7], Nadia Davidson[1,2,4], Alicia Oshlack 1-3.
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