FDJD: RNA-Seq Based Fusion Transcript Detection Using Jaccard Distance

Abstract

Gene fusions events, which are the result of two genes fused together to create a hybrid gene, were first described in cancer cells in the early 1980s. These events are relatively common in many cancers including prostate, lymphoid, soft tissue, and breast. Recent advances in next-generation sequencing (NGS) provide a high volume of genomic data, including cancer genomes. The detection of possible gene fusions requires fast and accurate methods. However, current methods suffer from inefficiency, lack of sufficient accuracy, and a high false-positive rate. We present an RNA-Seq fusion detection method that uses dimensionality reduction and parallel computing to speed up the computation. We convert the RNA categorical space into a compact binary array called binary fingerprints, which enables us to reduce the memory usage and increase efficiency. The search and detection of fusion candidates are done using the Jaccard distance. The detection of candidates is followed by refinement. We benchmarked our fusion prediction accuracy using both simulated and genuine RNA-Seq datasets. Paired-end Illumina RNA-Seq genuine data were obtained from 60 publicly available cancer cell line data sets. The results are compared against the state-of-the-art-methods such as STAR-Fusion, InFusion, and TopHat-Fusion. Our results show that FDJD exhibits superior accuracy compared to popular alternative fusion detection methods. We achieved 90% accuracy on simulated fusion transcript inputs, which is the highest among the compared methods while maintaining comparable run time.