{
  "abstract": "Programmable RNA sensors enable sequence-specific detection of RNA inputs for diagnostics, genetic circuit control, and multiplexed molecular readouts. Although readily programmable, their performance varies substantially with the target site selected within a transcript, making target-site selection a major challenge. Here, we report DeSTAR (Detectron Sensor TARget-site ranker), a machine-learning-assisted target-site ranking model for programmable RNA sensors. To train this model, we generated transcript-wide, single-nucleotide-resolution tiling sensor libraries across six diverse RNA targets, enabling generalization to be tested across transcripts rather than within a single RNA. In leave-one-transcript-out evaluation, DeSTAR's top-ranked sites were enriched for high-performing sensors in held-out transcripts, driven predominantly by interpretable sequence grammar in the sensor stem rather than transcript-specific patterns. Prospectively, on an unseen SARS-CoV-2 transcript, the model's top-ranked sites yielded working sensors roughly five-fold more often than random selection. These results support DeSTAR as a generalizable strategy for target-site selection in RNA sensors across transcripts.",
  "abstract_shown": true,
  "affiliations": [
    "Gladstone Institutes and UCSF"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "name": "J. Han"
    },
    {
      "affs": [
        0
      ],
      "name": "S. L. Shipman"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/85sak0xfr5fx9.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.09.758053",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.09.758053v1.full",
  "id": "85sak0xfr5fx9",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.09.758053v1",
  "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",
      "synthetic biology"
    ],
    [
      "openalex",
      "1312|Advanced biosensing and bioanalysis techniques"
    ]
  ],
  "openalex": "W7221535488",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.09.758053v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-09",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/85sak0xfr5fx9.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:19Z",
      "native_id": "10.64898/2026.10.09.758053",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.09.758053v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:synthetic biology",
      "subfield": "systems-synthetic"
    }
  ],
  "title": "High-throughput variant libraries and machine learning enable robust target-site selection for toehold-switch design in Detectrons",
  "title_text": "High-throughput variant libraries and machine learning enable robust target-site selection for toehold-switch design in Detectrons",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:19Z",
  "url": "https://research.draconitic.com/paper/85sak0xfr5fx9",
  "venue": "bioRxiv",
  "versions": []
}