{"doi":"10.1016/j.crmeth.2025.101111","title":"Combining panel-based and whole-transcriptome-based gene fusion detection by long-read sequencing","abstract":"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.","journal":"Cell Reports Methods","year":2025,"id":536401,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9439,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1401005,"name":"Xu Feng","orcid":"0000-0001-5913-9638","position":1,"is_corresponding":false},{"id":331601,"name":"Hannah M. Deutsch","orcid":"0000-0002-3757-6180","position":2,"is_corresponding":false},{"id":550835,"name":"Mian Umair Ahsan","orcid":"0000-0003-4725-2451","position":3,"is_corresponding":false},{"id":895345,"name":"Joe Chan","orcid":"0000-0002-5627-6693","position":4,"is_corresponding":false},{"id":1421725,"name":"Zizhuo Liang","orcid":null,"position":5,"is_corresponding":false},{"id":259295,"name":"Yuanquan Song","orcid":"0000-0001-7699-2059","position":6,"is_corresponding":false},{"id":446032,"name":"Marilyn M. Li","orcid":"0000-0002-4253-2369","position":7,"is_corresponding":false},{"id":291855,"name":"Kai Wang","orcid":"0000-0002-5585-982X","position":8,"is_corresponding":false},{"id":1090768,"name":"Karleena Rybacki","orcid":"0000-0002-6609-6573","position":0,"is_corresponding":true}],"reference_count":60,"raw_metadata":null,"created_at":"2026-07-19T02:52:05.227140Z","pmid":"40695274","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}