{
  "abstract": "Genetic variation among microbial strains of the same species can profoundly influence their phenotypes, ecological functions, and impacts on human health. Traditionally, the relative abundance of a species has been used to identify associations between the microbiome and disease. However, this approach overlooks intra-species genetic variation and is susceptible to spurious correlations arising from the compositional nature of abundance data and microbial load. Fast, k-mer-based algorithms can now accurately estimate strain-level Average Nucleotide Identity (ANI) in metagenomes. Despite its value as an orthogonal metric for strain-level analysis, existing ANI association methods are limited to case-control designs and rely on arbitrary identity thresholds. To address this, we developed StrainSpy, a statistical algorithm that jointly captures strain variations and replacement across longitudinal and multi-cohort studies without predefined thresholds. In simulations, StrainSpy achieved >98% sensitivity versus 79% for comparable approaches at similar specificity. Analysis of a longitudinal antibiotic study in 12 healthy adults revealed novel strain-level associations across multiple species, including reduced strain-level diversity despite persistence of the species, and showed that a previously reported association with E. coli was driven by reduced microbial load. Across 3,414 metagenomes from 17 colorectal cancer cohorts, StrainSpy identified novel strain-level associations in E. coli accompanied by enrichment of adhesin genes in addition to recovering well established associations. Importantly, machine learning models built on StrainSpy-selected features matched the predictive accuracy of abundance-based approaches, whilst avoiding the confounding effects of microbial load. StrainSpy is available as an R package at github.com/gtonkinhill/strainspy.",
  "abstract_shown": true,
  "affiliations": [
    "University of Melbourne"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 9,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "S. Mallawaarachchi"
    },
    {
      "name": "K. Tandon"
    },
    {
      "name": "N. Rajan"
    },
    {
      "name": "V. R. Marcelino"
    },
    {
      "name": "S. Sandhu"
    },
    {
      "name": "S. Bedoui"
    },
    {
      "name": "D. J. Ingle"
    },
    {
      "name": "A. Gunjur"
    },
    {
      "name": "G. Tonkin-Hill"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5xk6tzh0qmd7t.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.08.30.748153",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.08.30.748153v1.full",
  "id": "5xk6tzh0qmd7t",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.08.30.748153v1",
  "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",
      "microbiology"
    ],
    [
      "openalex",
      "1312|Gut microbiota and health"
    ]
  ],
  "openalex": "W7204949852",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.08.30.748153v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-08-30",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/5xk6tzh0qmd7t.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:11Z",
      "native_id": "10.64898/2026.08.30.748153",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.08.30.748153v2"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:microbiology",
      "subfield": "microbiology"
    }
  ],
  "title": "Accurate detection of metagenomic strain-level associations using average nucleotide identity with StrainSpy",
  "title_text": "Accurate detection of metagenomic strain-level associations using average nucleotide identity with StrainSpy",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:56Z",
  "url": "https://research.draconitic.com/paper/5xk6tzh0qmd7t",
  "venue": "bioRxiv",
  "versions": []
}