{
  "abstract": "Accurate species-level taxonomic classification of microbial amplicon sequences remains challenging due to the difficulty of resolving subtle variations within conserved ribosomal regions. Traditional similarity-based methods struggle with short amplicons and sequences with poor database representation. No existing method performs robustly across the full spectrum of amplicon lengths, limiting applicability across diverse sequencing platforms and variable-region targets. We present Taxonova, a deep learning framework for both short- and long-read ribosomal amplicons. Evaluated across physical and biome-simulated mock communities representing seven biomes (via the accompanying benchmark tool Taxobench), Taxonova attains species-level F-measures of 0.76–1.00 (median 0.94) for short-read and 0.82–0.99 (median 0.97) for long-read amplicons spanning 250 bp to 4,900 bp, reduces the species-level abundance-estimation error (L1 norm) by a median of 0.52 to 1.50 depending on amplicon region, and has a species-level false-positive rate that is a median of 16.2 × 10−4 lower than that of the other classifiers. Species-level assignment is expected to be most reliable for well-characterised communities, such as human-associated microbiomes. To demonstrate real-world applicability, we also compared amplicon-derived taxonomic profiles to paired shotgun whole-genome sequencing from gut, soil and vaginal microbiomes; Taxonova achieves the strongest species-level concordance (Jaccard) in all three studies, with a species-level Jaccard index 0.009 to 0.022 higher than that of the next-best classifier across both high- and low-complexity communities. Taxonova runs at a throughput of roughly 100 sequences per second on a single CPU core, making it suitable for routine deployment in research settings. Author summary: We developed Taxonova to address a long-standing problem in microbiome research: extracting species-level information from short DNA marker sequences. Taxonova abstains when the amplicon region carries too little signal to separate close relatives, returning a genus-level label rather than a guessed species. Taxonova made fewer confident mistakes when tested against seven existing tools on physical mock communities, biome-simulated mocks, and paired amplicon–shotgun datasets, Taxonova runs on ordinary laptops without specialised hardware and is freely available as both a Python package and a web application, lowering the barrier for researchers to obtain species-rank labels from their own amplicon data.",
  "abstract_shown": true,
  "affiliations": [
    "School of Microbiology, University College Cork, Cork, Ireland",
    "APC Microbiome Ireland, University College Cork, Cork, Ireland",
    "Seqbiome Ltd., Cork, Ireland",
    "Department of Electrical and Electronic Engineering, University College Cork, Cork, Ireland"
  ],
  "article_type": "Methods",
  "arxiv": null,
  "author_total": 4,
  "authors": [
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        0
      ],
      "name": "Kardokh Kakabra"
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      ],
      "name": "Shriram Patel"
    },
    {
      "affs": [
        2
      ],
      "name": "Andriy Temko"
    },
    {
      "affs": [
        3
      ],
      "name": "Marcus J. Claesson"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7awqa55k6m8qd.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pcbi.1014912",
  "fulltext_url": "https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014912",
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  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "plos",
      "/Biology and life sciences/Biochemistry/Nucleic acids/RNA/Non-coding RNA/Ribosomal RNA"
    ],
    [
      "plos",
      "/Biology and life sciences/Biochemistry/Ribosomes/Ribosomal RNA"
    ],
    [
      "plos",
      "/Biology and life sciences/Cell biology/Cellular structures and organelles/Ribosomes/Ribosomal RNA"
    ],
    [
      "plos",
      "/Biology and life sciences/Ecology/Ecosystems"
    ],
    [
      "plos",
      "/Biology and life sciences/Evolutionary biology/Evolutionary systematics/Phylogenetics"
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    [
      "plos",
      "/Biology and life sciences/Genetics/Genomics/Microbial genomics/Microbiome"
    ],
    [
      "plos",
      "/Biology and life sciences/Microbiology/Medical microbiology/Microbiome"
    ],
    [
      "plos",
      "/Biology and life sciences/Microbiology/Microbial genomics/Microbiome"
    ],
    [
      "plos",
      "/Biology and life sciences/Taxonomy"
    ],
    [
      "plos",
      "/Biology and life sciences/Taxonomy/Evolutionary systematics/Phylogenetics"
    ],
    [
      "plos",
      "/Biology and life sciences/Taxonomy/Microbial taxonomy"
    ],
    [
      "plos",
      "/Computer and information sciences/Computer architecture/Computer hardware"
    ],
    [
      "plos",
      "/Computer and information sciences/Computing methods/Control sequences"
    ],
    [
      "plos",
      "/Computer and information sciences/Data management/Taxonomy"
    ],
    [
      "plos",
      "/Computer and information sciences/Data management/Taxonomy/Evolutionary systematics/Phylogenetics"
    ],
    [
      "plos",
      "/Computer and information sciences/Data management/Taxonomy/Microbial taxonomy"
    ],
    [
      "plos",
      "/Ecology and environmental sciences/Ecology/Ecosystems"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1014912&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-29",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/7awqa55k6m8qd.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:17Z",
      "native_id": "10.1371/journal.pcbi.1014912",
      "source": "plos",
      "url": "https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014912"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "plos:cell biology",
      "subfield": "cell-biology"
    },
    {
      "field": "biology",
      "origin": "plos:ecology",
      "subfield": "ecology"
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    {
      "field": "biology",
      "origin": "plos:microbiology",
      "subfield": "microbiology"
    }
  ],
  "title": "Taxonova: Deep learning enables fast and confidence-aware microbiome species-level amplicon classification",
  "title_text": "Taxonova: Deep learning enables fast and confidence-aware microbiome species-level amplicon classification",
  "type": "peer-reviewed",
  "updated_at": "2026-10-31T12:20:55Z",
  "url": "https://research.draconitic.com/paper/7awqa55k6m8qd",
  "venue": "PLOS Computational Biology",
  "versions": []
}