{
  "abstract": "Current methods for computing sequence similarity across large groups of genome sequences tend to do so at the level of k-mers since this is far more efficient than base-by-base alignment. While these methods use default k-mer lengths, recent work shows that the particular choice of k-mer length greatly affects accuracy, and that using multiple values of k can boost accuracy relative to the best single choice of k, particularly when the goal is to estimate genomic distances that reflect true phylogenetic relationships. Here, we introduce Ok-k-mer, a tool to systematically select weighted k-mer length ensembles for pairwise genomic distance estimation, thereby increasing the accuracy of those estimates. Given a phylogenetic tree, Ok-k-mer ranks genome pairs by Jaccard distance across a range of k and uses a progressive search to maximize the Spearman correlation, {rho}, between weighted-average k-mer ranks and branch distances. Across eight Genome Taxonomy Database datasets, Ok-k-mer ensembles improved correlation over standard default k-mer lengths. In the Chlamydiales taxonomy, a three-length ensemble k=(9,17,22) achieved a 23.9% relative improvement over the best single length (k=20) and a 36.7% improvement over a commonly used default of 31-mers. We demonstrate that Ok-k-mer k-mer length ensembles remain stable (i.e. lengths and weights change only moderately) under genome subsampling within a taxonomy but vary across datasets. Ok-k-mer can annotate all subtrees of a phylogeny, which reveals distinct patterns of ensembles reflecting potential differences in genome evolution. Ok-k-mer provides a framework for moving beyond a \"one-k-fits-all\" k-mer analysis, improving genomic distance estimation in the era of massive-scale genomics.",
  "abstract_shown": true,
  "affiliations": [
    "Johns Hopkins University"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "S. Hwang"
    },
    {
      "name": "B. Langmead"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/c73xt4g163v21.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.11.06.764926",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.11.06.764926v1.full",
  "id": "c73xt4g163v21",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.11.06.764926v1",
  "language": null,
  "licence": "CC-BY-NC",
  "licence_label": "CC BY-NC",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "bioinformatics"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.11.06.764926v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-06",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/c73xt4g163v21.ris",
  "sources": [
    {
      "first_seen": "2026-11-10T00:27:11Z",
      "native_id": "10.64898/2026.11.06.764926",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.11.06.764926v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "Ok-k-mer: Selecting k-mer length ensembles for calculating genomic distance",
  "title_text": "Ok-k-mer: Selecting k-mer length ensembles for calculating genomic distance",
  "type": "preprint",
  "updated_at": "2026-11-10T00:27:11Z",
  "url": "https://research.draconitic.com/paper/c73xt4g163v21",
  "venue": "bioRxiv",
  "versions": []
}