biostudies-literatureRybacki KIntellectual and Developmental Disabilities Research CenterUniversity of PennsylvaniaThe Children's Hospital of PhiladelphiaNIHNational Human Genome Research Institute101111https://www.ebi.ac.uk/biostudies/studies/S-EPMC12461587biostudies-literatureUnknown5(8)We present a comprehensive gene fusion (GF) detection and analysis workflow that combines targeted panel-based and whole-transcriptome long-read sequencing. We first adapted libraries from the short-read CHOP Cancer Fusion Panel, which targets 119 oncogenes commonly implicated in cancer fusions, for use on Oxford Nanopore Technologies' long-read sequencing platform. Long-read sequencing successfully detected known GFs in panel-positive samples, confirming compatibility, and enabled reduced turnaround times. To expand GF discovery in clinically challenging cases, we analyzed 24 glioma samples with negative short-read fusion panel results using whole-transcriptome long-read sequencing. This identified 20 candidate GFs in panel-negative samples that were absent from current fusion databases, all of which were experimentally validated. In summary, we introduce a computational workflow that combines panel-based and whole-transcriptome long-read sequencing with tailored analysis pipelines to enable fast and comprehensive GF detection in cancer.Cell reports methodsCombining panel-based and whole-transcriptome-based gene fusion detection by long-read sequencing.PMC12461587HG000046HG013359GM132713HD105354Rybacki KDeutsch HMAhsan MUChan JLiang ZXu FLi MWang KSong YfalseCombining panel-based and whole-transcriptome-based gene fusion detection by long-read sequencing.We present a comprehensive gene fusion (GF) detection and analysis workflow that combines targeted panel-based and whole-transcriptome long-read sequencing. We first adapted libraries from the short-read CHOP Cancer Fusion Panel, which targets 119 oncogenes commonly implicated in cancer fusions, for use on Oxford Nanopore Technologies' long-read sequencing platform. Long-read sequencing successfully detected known GFs in panel-positive samples, confirming compatibility, and enabled reduced turnaround times. To expand GF discovery in clinically challenging cases, we analyzed 24 glioma samples with negative short-read fusion panel results using whole-transcriptome long-read sequencing. This identified 20 candidate GFs in panel-negative samples that were absent from current fusion databases, all of which were experimentally validated. In summary, we introduce a computational workflow that combines panel-based and whole-transcriptome long-read sequencing with tailored analysis pipelines to enable fast and comprehensive GF detection in cancer.2025-01-01T00:00:00Z2025 Aug2026-06-03T20:13:44.636Z2026-05-01T03:11:04.865ZS-EPMC124615874069527410.1016/j.crmeth.2025.101111