PO-400 Arriba – fast and accurate gene fusion detection from RNA-seq data

Abstract

Introduction Personalised oncology revolutionises the way how eligible therapies are selected to treat cancer patients. With the help of next-generation sequencing technology, cancer can be understood at the molecular level. This enables clinicians to match cancer patients to drugs that precisely target the driver mutations of the tumour. The foundation of a successful matchmaking is a reliable detection of somatic variants from next-generation sequencing data. Particularly the identification of more complex alterations such as gene fusions remains a challenging task.Material and methods We developed {\textquoteright}Arriba{\textquoteright}, a novel algorithm that identifies gene fusions from RNA-seq data. It is based on the output of the STAR aligner and can easily be integrated into existing bioinformatics pipelines built on this software. The tool was designed specifically for the application in a high-throughput clinical workflow, where high sensitivity and short turn-around times are crucial.Results and discussions Arriba excels previous methods in terms of sensitivity without compromising precision. Its highly efficient algorithm reduces the time required for results generation from many hours to just a few minutes. Moreover, the tool comes with a rich set of features that are useful in a clinical context, including comprehensive annotation, assistance with the design of primers for Sanger validation, and automatic generation of publication-quality figures.Conclusion The identification of gene fusions from RNA-seq data used to be an error-prone and computationally demanding task. Arriba is a novel algorithm that overcomes these shortcomings. It delivers more accurate results than previous methods within a fraction of the time.