{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T12:31:03Z","timestamp":1787056263000,"version":"build-2736575974"},"reference-count":30,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T00:00:00Z","timestamp":1787011200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T00:00:00Z","timestamp":1787011200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100002920","name":"Research Grants Council, University Grants Committee","doi-asserted-by":"publisher","award":["C7003\u201024Y"],"award-info":[{"award-number":["C7003\u201024Y"]}],"id":[{"id":"10.13039\/501100002920","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Quant. Biol."],"published-print":{"date-parts":[[2026,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Although long\u2010read sequencing enables comprehensive genomic analysis, its potential is hindered by its reliance on the incomplete GRCh38 reference genome. GRCh38\u2019s unresolved bases, structural errors, and haplotype mosaics introduce reference biases, leading to missed variants and false\u2010positive calls. This limitation creates a circular dependency because benchmarks derived from GRCh38 exclude about 15% of complex genomic regions, thereby restricting the training and evaluation of state\u2010of\u2010the\u2010art variant callers. The complete telomere\u2010to\u2010telomere (T2T\u2010CHM13) assembly resolves these gaps and provides an accurate genome\u2010wide coordinate system. To bridge this transition, we present a variant training framework\u00a0that integrates high\u2010confidence variants from both GRCh38 and T2T\u2010CHM13. Our hybrid training strategy combines rigorous variant filtering and multi\u2010caller aggregation to produce more reliable variants for model training. This mixed\u2010reference model demonstrates superior accuracy across sequencing technologies, coverage depths, and diverse samples. Notably, the model trained solely on a single CHM13\u2010specific sample outperformed models trained on multiple GRCh38\u2010specific samples on the Oxford Nanopore Technologies platform. Our findings not only confirm the advantages of complete reference genomes but also reveal new challenges and opportunities for variant discovery in complex genomic regions.<\/jats:p>","DOI":"10.1002\/qub2.70051","type":"journal-article","created":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T12:04:34Z","timestamp":1787054674000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Telomere\u2010to\u2010telomere CHM13 reference reveals missing truth variants and improves deep learning\u2010based variant calling in long\u2010read sequencing data"],"prefix":"10.1002","volume":"14","author":[{"given":"Zhenxian","family":"Zheng","sequence":"first","affiliation":[{"name":"School of Computing and Data Science The University of Hong Kong  Hong Kong China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Minggao","family":"He","sequence":"additional","affiliation":[{"name":"School of Computing and Data 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