Leveraging Sample-Specific Strings to Enhance Fusion Transcript Detection

• Luísa de Melo Barros Penze Universidade Estadual de Campinas (UNICAMP) https://orcid.org/0009-0002-7689-8649
• Lucas Peres Oliveira Universidade Estadual de Campinas (UNICAMP) https://orcid.org/0000-0002-8292-9985
• João Meidanis Universidade Estadual de Campinas (UNICAMP) https://orcid.org/0000-0001-7878-4990

DOI: https://doi.org/10.5753/bsb.2025.15130

Resumo

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.

Palavras-chave: fusion transcript, long reads, cancer

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