Leveraging Sample-Specific Strings to Enhance Fusion Transcript Detection - Penze, Luísa de Melo Barros; Oliveira, Lucas Peres; Meidanis, João (Anais do Simpósio Brasileiro de Bioinformática (BSB), 2025)
Fusion transcripts are widely used biomarkers (e.g., in cancer diagnosis), but current methods to detect them from long-read sequencing data still yield a high number of false positives. In this study, we attack this problem by selecting reads containing absent strings in a reference transcriptome — sample-specific strings (SFS) — before applying a fusion detection method. We adapted an existing SFS retrieval algorithm to long-read RNA-seq data and applied it to select reads in eight simulated datasets. These reads were then fed to three transcript fusion detection methods — LongGF, JAFFAL, and CTAT-LR. Our results show that SFSs capture the reads with relevant information and, hence, improve the accuracy of most of these fusion detection methods.