{
  "abstract": "Comparing mutational spectra across experimental groups requires inference at the level of the biological replicate. Error-corrected sequencing (ECS) generates hundreds to thousands of mutations per replicate, yet classical pooled G2 tests treat individual mutations as independent observations and ignore replicate-level variability, inflating statistical significance (pseudo-replication). We present a replicate-aware Ridge-Penalized Dirichlet-Multinomial (RP-DMM) framework that models biological replicates directly and accounts for overdispersion. An L2 (ridge) penalty, with strength chosen by leave-one-out cross-validation, stabilizes spectrum estimates by shrinking weakly supported mutation categories toward a common baseline, and inference uses a parametric bootstrap. We also provide a complementary non-parametric rank-permutation test for sparse datasets. In simulations, the pooled G2 test became increasingly anti-conservative as mutation depth increased, with false-positive rates approaching 100% under the null hypothesis. RP-DMM maintained Type I error near the nominal 5% level and had substantially greater power than the rank-permutation test. Regularization was most consequential for high-dimensional spectra. False positive rates approached the nominal 5% level once the lower-mutation group contained approximately 300-500 mutations, depending on the number of biological replicates. A practical target of approximately 400 mutations in the lower-mutation group provided robust Type I error control across all scenarios examined; below this, the rank-permutation test performed robustly and is preferred. In Duplex Sequencing data from benzo[a]pyrene-exposed mice, RP-DMM distinguished exposure-associated mutational signatures from comparisons with higher mutation counts but null effect size, whereas the pooled G2 test declared all comparisons significant. The cross-validated penalty decreased as mutation depth increased, indicating that regularization strength should be tuned to the data. The framework is implemented in the MutSeqR R package.",
  "abstract_shown": true,
  "affiliations": [
    "Health Canada"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "A. Williams"
    },
    {
      "name": "A. J. Alcaraz"
    },
    {
      "name": "A. Dodge"
    },
    {
      "name": "D. P. LeBlanc"
    },
    {
      "name": "M. J. Meier"
    },
    {
      "name": "C. Yauk"
    },
    {
      "name": "F. Marchetti"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0jkky8ah255yk.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.19.760182",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.19.760182v1.full",
  "id": "0jkky8ah255yk",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.19.760182v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "bioinformatics"
    ],
    [
      "openalex",
      "1306|Cancer Genomics and Diagnostics"
    ]
  ],
  "openalex": "W7223567761",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.19.760182v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-19",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/0jkky8ah255yk.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:32Z",
      "native_id": "10.64898/2026.10.19.760182",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.19.760182v2"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "A robust mutation spectrum analysis framework for high-depth error-corrected sequencing data",
  "title_text": "A robust mutation spectrum analysis framework for high-depth error-corrected sequencing data",
  "type": "preprint",
  "updated_at": "2026-11-05T00:20:43Z",
  "url": "https://research.draconitic.com/paper/0jkky8ah255yk",
  "venue": "bioRxiv",
  "versions": []
}