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Peer-reviewedCC BYBMC bioinformatics

RIFTT: Robust Identification of Fusion genes in Tumor Transcriptomes.

  • Paul Kerbs1,2
  • Lisa Latzko1,2
  • Ulrich Mansmann3
  • Kristian Unger2,4,5
  • Philipp A Greif1,2
  • Sebastian Vosberg6,7
7 affiliations
  1. 1Department of Medicine III, LMU University Hospital, LMU Medizin, Ludwig- Maximilians-Universität München, Munich, Germany.
  2. 2German Cancer Consortium (DKTK), partner site Munich, a partnership between DKFZ and LMU University Hospital Munich (LMU Klinikum), Munich, Germany.
  3. 3Department of Medical Data Processing, Biometry and Epidemiology, LMU University Hospital, LMU Medizin, Ludwig-Maximilians-Universität München, Munich, Germany.
  4. 4Department of Radiation Oncology, LMU University Hospital, LMU Medizin, Ludwig- Maximilians-Universität München, Munich, Germany.
  5. 5Bavarian Center for Cancer Research (BZKF), Partner Site Munich, Munich, Germany.
  6. 6Department of Medicine III, LMU University Hospital, LMU Medizin, Ludwig- Maximilians-Universität München, Munich, Germany. [email protected].
  7. 7German Cancer Consortium (DKTK), partner site Munich, a partnership between DKFZ and LMU University Hospital Munich (LMU Klinikum), Munich, Germany. [email protected].
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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 .

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Cite

BibTeXRIS
BibTeX
@article{kerbs2026riftt,
  title = {{RIFTT: Robust Identification of Fusion genes in Tumor Transcriptomes.}},
  author = {Paul Kerbs and Lisa Latzko and Ulrich Mansmann and Kristian Unger and Philipp A Greif and Sebastian Vosberg},
  journal = {BMC bioinformatics},
  year = {2026},
  month = oct,
  doi = {10.1186/s12859-026-06725-4},
  url = {https://europepmc.org/article/MED/42972257},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - RIFTT: Robust Identification of Fusion genes in Tumor Transcriptomes.
AU  - Paul Kerbs
AU  - Lisa Latzko
AU  - Ulrich Mansmann
AU  - Kristian Unger
AU  - Philipp A Greif
AU  - Sebastian Vosberg
PY  - 2026
DA  - 2026/10/30
JO  - BMC bioinformatics
DO  - 10.1186/s12859-026-06725-4
UR  - https://europepmc.org/article/MED/42972257
AB  - 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 .
SN  - 1471-2105
ER  -