Abstract
ABSTRACTBackgroundGenomic profiling efforts have revealed a rich diversity of oncogenic fusion genes. While there are many methods for identifying fusion genes from RNA-sequencing (RNA-seq) data, visualizing these transcripts and their supporting reads remains challenging.FindingsClinker is a bioinformatics tool written in Python, R, and Bpipe that leverages the superTranscript method to visualize fusion genes. We demonstrate the use of Clinker to obtain interpretable visualizations of the RNA-seq data that lead to fusion calls. In addition, we use Clinker to explore multiple fusion transcripts with novel breakpoints within the P2RY8-CRLF2 fusion gene in B-cell acute lymphoblastic leukemia.ConclusionsClinker is freely available software that allows visualization of fusion genes and the RNA-seq data used in their discovery.
Highlights
Genomic structural abnormalities, such as translocations between and within chromosomes, are common in cancer and can result in the fusion of two genes which function as an oncogenic driver
The first example of this was the recurrent t(9;22) fusion in Chronic Myeloid Leukaemia, creating the BCR-ABL1 oncogene. This fusion gene results in a constitutively activated tyrosine kinase protein that can be effectively treated with small molecule inhibitors of ABL1, such as imatinib and dasatinib (Quintás-Cardama et al, 2009)
We present Clinker, a visualisation tool for exploring and plotting fusion genes discovered in RNA-seq data
Summary
Genomic structural abnormalities, such as translocations between and within chromosomes, are common in cancer and can result in the fusion of two genes which function as an oncogenic driver. While there are many methods available for identifying fusion genes from RNA-seq data, there are few ways to visualise the fusion transcripts and the sequencing reads that support them. One visualisation strategy involves using predicted breakpoints to create the fusion transcript sequence, which can be used as a reference for read alignment (Beccuti et al, 2014). This approach demonstrates coverage across the fusion breakpoints but other information about the structure and expression of the fusion transcripts, such as its expression relative to non-fused transcripts, are lost.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
