{
  "abstract": "Phage tail-like elements (PTEs) -- tailocins, bacterial type VI secretion systems (T6SS), and extracellular contractile injection systems (eCIS) -- are contractile nanomachines that bacteria use to kill their neighbors and compete within their micro-ecosystems. PTEs help shape microbial community composition. Most PTE detection tools only detect a single PTE class. Moreover, most tailocin detection methods are largely restricted to Pseudomonas, leaving a key part of tailocin diversity uncharacterized. In this work, we present PhageTAILor (https://github.com/hjcho-bio/PhageTAILor), an integrative and fully automated pipeline that detects and classifies prophages and 3 PTE classes from bacterial genomes. PhageTAILor combines a 6-detector homology-based candidate search (geNomad, tail-gene, PHROGs-tail, SecReT6, eCIStem, and a divergence-tolerant tail-HMM detector) with a LightGBM classifier comprising 1 multiclass and 3 binary heads, trained on 6,501 bacterial genomes carrying 13,082 prophages and PTEs. A phylogeny-free feature matrix used in our model keeps predictions reproducible between model construction and user inference. PhageTAILor performs strongly at the genome level and generalizes beyond its Pseudomonas-rich training set. On a 76-strain cross-clade benchmark, PhageTAILor detected tailocins at F1 = 0.955. Furthermore, it identified 12 of 13 experimentally validated tailocins spanning five genera versus 2 of 13 for a Pseudomonas-restricted tool TattleTail. PhageTAILor also demonstrated sensitivity equivalent to viral detection tool geNomad while avoiding its higher false-positive rate. Applied to 7,925 plant- and soil-associated bacterial isolates, PhageTAILor showed that prophages in the phyllosphere and tailocins in plant-associated bacteria, whereas eCIS are enriched in soil. PhageTAILor is distributed as an open-source, modular pipeline with a command-line interface.",
  "abstract_shown": true,
  "affiliations": [
    "DOE Joint Genome Institute"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 14,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "H. Cho"
    },
    {
      "name": "S. Hour"
    },
    {
      "name": "S. Roux"
    },
    {
      "name": "C. Coclet"
    },
    {
      "name": "O. Amusat"
    },
    {
      "name": "V. K. Mutalik"
    },
    {
      "name": "A. E. Kazakov"
    },
    {
      "name": "A. Levy"
    },
    {
      "name": "N. Nachmias"
    },
    {
      "name": "L. Aureli"
    },
    {
      "name": "T. S. Sweet"
    },
    {
      "name": "A. Visel"
    },
    {
      "name": "R. M. Ceballos"
    },
    {
      "name": "J. T. R. Basso"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/bq4kf69f0x7y2.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.08.24.746745",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.08.24.746745v1.full",
  "id": "bq4kf69f0x7y2",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.08.24.746745v1",
  "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",
      "2303|Bacteriophages and microbial interactions"
    ]
  ],
  "openalex": "W7204869693",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.08.24.746745v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-08-24",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/bq4kf69f0x7y2.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:49Z",
      "native_id": "10.64898/2026.08.24.746745",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.08.24.746745v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:microbiology",
      "subfield": "microbiology"
    }
  ],
  "title": "PhageTAILor leverages machine learning for phage tail-like elements detection and classification in plant-associated bacteria",
  "title_text": "PhageTAILor leverages machine learning for phage tail-like elements detection and classification in plant-associated bacteria",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:49Z",
  "url": "https://research.draconitic.com/paper/bq4kf69f0x7y2",
  "venue": "bioRxiv",
  "versions": []
}