Slinker: Visualising novel splicing events in RNA-Seq data
Journal Title
F1000Research
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.
Publisher
F1000
Keywords
Exons/genetics; Humans; *rna; *RNA Splicing/genetics; RNA-Seq; Sequence Analysis, RNA; *Novel Splicing Events; *RNA-Seq; *Visualisation; *bioinformatics; *superTranscripts
Research Division(s)
Advanced Technology And Biology; Blood Cells And Blood Cancer
PubMed ID
35035899
Terms of Use/Rights Notice
Refer to copyright notice on published article.


Creation Date: 2022-02-07 10:12:24
Last Modified: 2022-02-08 02:18:05
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