Bioinformatics Seminar
Time:
Venue: Na
30 October 2018
NaMintie: identifying cryptic variants in cancer transcriptomes using RNA-seq data
Marek CmeroMurdoch Children
Gene fusions ; tandem duplications and other transcriptomic structural variants can modify gene function and have important implications in cancer prognosis and treatment decisions. While calling fusions from RNA-seq data is well established ; 'cryptic' variants such as fusions with non-gene sequence ; tandem duplications ; novel splice sites or other complex variants ; are difficult to detect using existing approaches. While some of these events are possible to detect from DNA sequencing ; others can be the result of variants affecting the transcriptional machinery and are only visible in the transcriptome.
To identify these variants in cancer transcriptomes ; we developed Mintie ; an integrated pipeline for the detection of cryptic variants using RNA-seq data. The Mintie pipeline first performs de novo assembly of transcripts. Next ; novel transcripts are selected and all transcripts are then quantified using pseudo-alignment. Finally ; differential expression versus controls is performed to identify over-expressed novel transcripts in each sample. Mintie also performs comprehensive annotation and visualisation of candidate cryptic variants.
In order to demonstrate Mintie ; we ran the pipeline on a cohort of high-risk B-ALL patients ; which included a subset of patients with poor outcome but no detected driver variant. In this subset ; we identified several novel candidate driver cryptic variants. One such variant was a gene-disrupting cryptic fusion involving a truncation of the tumour suppressor gene RB1. We believe Mintie will be able to identify new cancer driver mechanisms across a range of cancer types.
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