GFusion: an Effective Algorithm to Identify Fusion Genes from Cancer RNA-Seq Data

Abstract

Abstract Fusion gene derived from genomic rearrangement plays a key role in cancer initiation. The discovery of novel gene fusions may be of significant importance in cancer diagnosis and treatment. Meanwhile, next generation sequencing technology provide a sensitive and efficient way to identify gene fusions in genomic levels. However, there are still many challenges and limitations remaining in the existing methods which only rely on unmapped reads or discordant alignment fragments. In this work we have developed GFusion, a novel method using RNA-Seq data, to identify the fusion genes. This pipeline performs multiple alignments and strict filtering algorithm to improve sensitivity and reduce the false positive rate. GFusion successfully detected 34 from 43 previously reported fusions in four cancer datasets. We also demonstrated the effectiveness of GFusion using 24 million 76 bp paired-end reads simulation data which contains 42 artificial fusion genes, among which GFusion successfully discovered 37 fusion genes. Compared with existing methods, GFusion presented higher sensitivity and lower false positive rate. The GFusion pipeline can be accessed freely for non-commercial purposes at: https://github.com/xiaofengsong/GFusion .