The identification of gene fusions from RNA sequencing data is a routine task in cancer research and precision oncology. However, despite the availability of many computational tools, fusion detection remains challenging. Existing methods suffer from poor prediction accuracy and are computationally demanding. Researchers from the German Cancer Research Center have developed Arriba, a novel fusion detection algorithm with high sensitivity and short runtime. When applied to a large collection of published pancreatic cancer samples (n=803), Arriba identified a variety of driver fusions, many of which affected druggable proteins, including ALK, BRAF, FGFR2, NRG1, NTRK1, NTRK3, RET, and ROS1. The fusions were significantly associated with KRAS wild-type tumors and involved proteins stimulating the MAPK signaling pathway, suggesting that they substitute for activating mutations in KRAS. In addition, the researchers confirmed the transforming potential of two novel fusions, RRBP1-RAF1 and RASGRP1-ATP1A1, in cellular assays. These results demonstrate Arriba’s utility in both basic cancer research and clinical translation.
Arriba is an extension of a standard alignment workflow based on STAR. In legacy mode, STAR writes chimeric alignments to the file Chimeric.out.sam. In newer versions, STAR writes them to the main output file Aligned.out.bam. Arriba can take either file as input to search for gene fusions.
Availability – The most recent source code and precompiled binaries are available for the Linux operating system under the MIT and GPLv3 licenses at https://github.com/suhrig/arriba