RIFTT: Robust Identification of Fusion genes in Tumor Transcriptomes

Abstract Background Fusion genes serve as crucial biomarkers for cancer diagnosis, prognosis, and therapy, yet many detection tools lead to high false-positive rates and poor consensus. We developed RIFTT, a pipeline for Robust Identification of Fusion genes in Tumor Transcriptomes which integrates Arriba and FusionCatcher, two state-of-the-art fusion callers, together with novel scoring metrics (Promiscuity Score, Fusion Transcript Score, and Robustness Score). Results Benchmarking on TCGA-LAML, BeatAML, and LL-100 datasets demonstrated RIFTT’s ability to reduce the number of candidates by up to 95% compared to single callers. At the same time, it preserves high-confidence events, outperforming both the nf-core/rnafusion pipeline and an Arriba/FusionCatcher consensus approach. RIFTT also identified novel high-evidence candidates, including a previously unreported recurrent fusion in acute myeloid leukemia. Conclusion Packaged in a Singularity container for ease of use across computing environments, RIFTT provides standardized visual and annotated tabular outputs to support downstream analysis in research workflows and clinical molecular diagnostics. RIFTT is freely available at https://github.com/pkerbs/RIFTT .