{
  "abstract": "Accurate bacterial promoter prediction supports genome annotation, but many models use fixed-length promoter windows anchored to transcription start sites (TSSs) rather than promoter-element boundaries. We developed the General Bacterial Promoter Predictor (GBactPro), a convolutional neural network-bidirectional long short-term memory (CNN-BiLSTM) framework using promoter sequences resolved by the Promoter Architecture Scanner. Using Escherichia coli-trained models evaluated across 43 bacterial species, we compared eight input designs and identified the contiguous -35/spacer/-10 core as optimal (mean AUROC = 0.9859). Species-specific models showed broad transfer in a 43 x 43 evaluation. Joint training on all 43 species achieved a mean AUROC of 0.9943 across their held-out test sets and outperformed four established predictors under matched-length benchmarks. In genome-wide E. coli scanning, GBactPro achieved AUROC and AUPRC values of 0.979 and 0.160 under stringent full-containment matching (0-bp tolerance), increasing to 0.997 and 0.323 under a 100-bp tolerance. False-positive predictions overlapped H-NS binding regions more frequently than true positives (24.4% versus 18.8%), and predictions also overlapped promoters derepressed in an H-NS-knockout dataset. Prediction precision varied across four TSS classes. These findings demonstrate that element-resolved promoter definitions improve cross-species prediction and support genome-wide identification of candidate bacterial promoters.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Computer Science, National Chengchi University; Institute of Neuroscience, National Chengchi University"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "name": "H.-Y. Han"
    },
    {
      "name": "Y.-C. V. Kao"
    },
    {
      "name": "S.-T. Kuo"
    },
    {
      "name": "H.-H. D. Chou"
    },
    {
      "affs": [
        0
      ],
      "name": "J.-M. Chang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1zjmzhjvvwz2a.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.18.759981",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.18.759981v1.full",
  "id": "1zjmzhjvvwz2a",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.18.759981v1",
  "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",
      "genomics"
    ],
    [
      "openalex",
      "1312|Genomics and Chromatin Dynamics"
    ]
  ],
  "openalex": "W7223467624",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.18.759981v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-18",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/1zjmzhjvvwz2a.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:23Z",
      "native_id": "10.64898/2026.10.18.759981",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.18.759981v2"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:genomics",
      "subfield": "genetics-genomics"
    }
  ],
  "title": "GBactPro: cross-species bacterial promoter prediction using element-resolved promoter definitions",
  "title_text": "GBactPro: cross-species bacterial promoter prediction using element-resolved promoter definitions",
  "type": "preprint",
  "updated_at": "2026-10-31T00:20:36Z",
  "url": "https://research.draconitic.com/paper/1zjmzhjvvwz2a",
  "venue": "bioRxiv",
  "versions": []
}