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Arriba: accurate and efficient detection of gene fusions from RNA-Seq (H021)

ABSTRACT: The identification of gene fusions from RNA sequencing data is a routine task in cancer research. However, despite the availability of a multitude of computational tools, this task remains challenging. Existing tools suffer from poor prediction accuracy and are computationally demanding, taking many hours to deliver results. We developed Arriba, a novel fusion detection algorithm with high sensitivity and short runtime (https://github.com/suhrig/arriba/). We compiled a large collection of published RNA sequencing data (n=803) from pancreatic tumor samples Ã¢Â€Â“ a disease with dismal prognosis and few treatment options. Arriba identified a variety of driver fusions, many of which affected druggable proteins (ALK, BRAF, FGFR2, NRG1, NTRK1, NTRK3, RET, ROS1). The fusions were significantly associated with KRAS wild-type tumors and with oncogenes stimulating the MAPK signaling pathway, suggesting that they substitute for activating mutations in KRAS. In addition, we confirmed the transforming potential of two novel fusions in cellular assays (RRBP1-RAF1, RASGRP1-ATP1A1).

PROVIDER: EGAS00001003554 | EGA |

REPOSITORIES: EGA

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