{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,11]],"date-time":"2026-10-11T10:50:19Z","timestamp":1791715819693,"version":"4.3.4"},"reference-count":39,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2026,10,11]],"date-time":"2026-10-11T00:00:00Z","timestamp":1791676800000},"content-version":"vor","delay-in-days":40,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62602207"],"award-info":[{"award-number":["62602207"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62502161"],"award-info":[{"award-number":["62502161"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62502163"],"award-info":[{"award-number":["62502163"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2024YFF1206600"],"award-info":[{"award-number":["2024YFF1206600"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,9,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Large-scale population sequencing requires reference genomes that accurately represent the genetic background of target populations. Although high-quality linear references such as T2T-CHM13 have improved the completeness of human genome representation, a single reference genome remains insufficient to capture genetic diversity across populations. Pangenome graphs provide a richer representation of multi-source genomic variation, but translating pangenome diversity into practical reference genomes remains challenging. First, the direct application of pangenome graphs in routine large-scale sequencing analysis is limited by graph complexity and poor compatibility with existing linear-reference workflows. Second, existing pangenome-driven personalized references mainly target individual samples rather than populations, and current path recommendation strategies may overlook local source consistency during branch-node selection. In this study, we propose PGpath, a pangenome-based method for recommending population-adapted linear reference genomes. PGpath generates a reference from a selected pangenome subpath, preserving pangenome diversity while retaining a linear format compatible with standard downstream pipelines. In this study, we characterize the shared genetic background of samples using population-level k-mer frequency features and reformulate reference recommendation as branch-node prediction in the pangenome graph. To encourage local source consistency, we develop a topology-constrained multi-task neural network incorporating source-consistency relationships between adjacent branch nodes. Experiments on Human Pangenome Reference Consortium (HPRC) population datasets and HG002 variant detection benchmarks showed that PGpath-derived references improved read mapping performance and reduced downstream variant detection errors compared with commonly used references. PGpath provides a practical strategy for translating pangenome diversity into population-adapted linear references for large-scale genomic analysis.<\/jats:p>","DOI":"10.1093\/bib\/bbag547","type":"journal-article","created":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T18:28:31Z","timestamp":1789756111000},"source":"Crossref","is-referenced-by-count":0,"title":["PGpath: recommending population-adapted linear reference genomes from pangenome subpaths"],"prefix":"10.1093","volume":"27","author":[{"given":"Zhengfa","family":"Xue","sequence":"first","affiliation":[{"name":"School of Future Technology, South China University of Technology , No. 777 Xingye Avenue East, Panyu District, Guangzhou, Guangdong 511442 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuwen","family":"Wang","sequence":"additional","affiliation":[{"name":"The Comprehensive Breast Care Center, The Second Affiliated Hospital of Xi\u2019an Jiaotong University , No. 157 Xiwu Road, Xincheng District, Xi\u2019an, Shaanxi 710004 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiajing","family":"Yuan","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Faculty of Electronics and Information Engineering, Xi\u2019an Jiaotong University , No. 28 Xianning West Road, Beilin District, Xi\u2019an, Shaanxi 710049 ,","place":["China"]},{"name":"Shaanxi Engineering Research Center of Medical and Health Big Data, Xi\u2019an Jiaotong University , No. 28 Xianning West Road, Beilin District, Xi\u2019an, Shaanxi 710049 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4875-3488","authenticated-orcid":false,"given":"Tianci","family":"Wang","sequence":"additional","affiliation":[{"name":"The Comprehensive Breast Care Center, The Second Affiliated Hospital of Xi\u2019an Jiaotong University , No. 157 Xiwu Road, Xincheng District, Xi\u2019an, Shaanxi 710004 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoqi","family":"Sheng","sequence":"additional","affiliation":[{"name":"School of Future Technology, South China University of Technology , No. 777 Xingye Avenue East, Panyu District, Guangzhou, Guangdong 511442 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4595-6167","authenticated-orcid":false,"given":"Tinghe","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Future Technology, South China University of Technology , No. 777 Xingye Avenue East, Panyu District, Guangzhou, Guangdong 511442 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3862-6557","authenticated-orcid":false,"given":"Jiayin","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Faculty of Electronics and Information Engineering, Xi\u2019an Jiaotong University , No. 28 Xianning West Road, Beilin District, Xi\u2019an, Shaanxi 710049 ,","place":["China"]},{"name":"Shaanxi Engineering Research Center of Medical and Health Big Data, Xi\u2019an Jiaotong University , No. 28 Xianning West Road, Beilin District, Xi\u2019an, Shaanxi 710049 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2747-7234","authenticated-orcid":false,"given":"Hongmin","family":"Cai","sequence":"additional","affiliation":[{"name":"School of Future Technology, South China University of Technology , No. 777 Xingye Avenue East, Panyu District, Guangzhou, Guangdong 511442 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