{
  "abstract": "Background: Linear-reference read alignment is computationally efficient but is susceptible to reference bias, whereas pangenome graph alignment better represents genetic diversity at substantially greater computational cost. Findings: We developed DCSMAP, a hybrid workflow that combines linear alignment with machine-learning-based selection of reads for remapping to a personalized pangenome graph, followed by local indel realignment. An XGBoost classifier trained on linear-alignment features achieved an area under the receiver operating characteristic curve of 0.9831 on held-out test data. Across 17 runs covering seven Genome in a Bottle samples, two sequencing platforms, and two read lengths, it selected a mean of 14.80% of read pairs for graph remapping. The optimized hybrid workflows reduced variant-calling errors by 46.66% relative to BWA-MEM2 and 8.39% relative to vg personalized-pangenome alignment. Heterozygous allelic balance indicated that the hybrid workflows substantially reduced reference bias relative to BWA-MEM2 linear alignment, yielding reference bias comparable to that of the pangenome alignment. On HG003 DNBSEQ PE150 data, DCSMAP achieved a 2 to 2.5 times speedup over the personalized-pangenome workflow; both workflows used 32 threads. Efficiency gains arose from both the hybrid architecture and tool-level optimizations, which accelerated personalized-graph construction by 2.5 times, graph alignment by 1.2 times, and indel realignment by 2.5 times. Conclusions: Selectively applying graph alignment to reads with potentially unreliable linear placements retains the accuracy benefits of personalized pangenomes while substantially reducing computation. DCSMAP provides an accurate and resource-efficient approach to short-read mapping on a standard CPU server.",
  "abstract_shown": true,
  "affiliations": [
    "BGI Research, Wuhan 430074, China"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "name": "Q. Yang"
    },
    {
      "name": "R. Wan"
    },
    {
      "name": "C. Gong"
    },
    {
      "name": "S. Li"
    },
    {
      "name": "Y. Li"
    },
    {
      "affs": [
        0
      ],
      "name": "Y. Zhang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2g7xq30e2kgmf.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.18.759502",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.18.759502v1.full",
  "id": "2g7xq30e2kgmf",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.18.759502v1",
  "language": null,
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "bioinformatics"
    ],
    [
      "openalex",
      "1312|Genomics and Phylogenetic Studies"
    ]
  ],
  "openalex": "W7222963145",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.18.759502v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-18",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/2g7xq30e2kgmf.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:28:37Z",
      "native_id": "10.64898/2026.10.18.759502",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.18.759502v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "DCSMAP: A Hybrid Linear-Pangenome Framework for Accurate and Efficient Read Alignment",
  "title_text": "DCSMAP: A Hybrid Linear-Pangenome Framework for Accurate and Efficient Read Alignment",
  "type": "preprint",
  "updated_at": "2026-10-30T12:28:37Z",
  "url": "https://research.draconitic.com/paper/2g7xq30e2kgmf",
  "venue": "bioRxiv",
  "versions": []
}