{
  "abstract": "Background: Polyadenylation at the 3′ end of mRNA regulates transcript stability, export, and translation, but poly(A) tail length and polyadenylation-site usage remain difficult to resolve from nanopore sequencing data because of homopolymer-associated errors and complex 3′ end architectures. We developed PolyAnalysis, a signal-level computational framework for estimating poly(A) tail length from raw Oxford Nanopore Technologies signals and linking per-read estimates to confidence-aware analyses of polyadenylation sites, alternative polyadenylation, and repetitive-element-associated 3′ ends.\n\nResults: PolyAnalysis combines CNN–BiLSTM-based region classification with multi-task connectionist temporal classification decoding and adenine-aware training and decoding to infer poly(A)-proximal boundaries, tail length, and model-derived sequence features. Benchmarking against Nanopolish, tailfindr, and Dorado on controlled RNA002 and RNA004 datasets showed competitive tail-length accuracy and high callability. Component-wise ablation and bias-control analyses indicated that region classification, multi-task optimization, adenine-weighted loss, and A-rich decoding jointly contributed to performance. Application to human cell-line and vertebrate transcriptome datasets identified dataset-level variation in tail-length distributions, gene-level heterogeneity, and weak associations with steady-state transcript abundance. Confidence-based filtering separated database-matched, high-confidence novel-candidate and low-confidence polyadenylation sites, while ambiguity-aware endogenous-retrovirus analyses distinguished locus-level from family-level evidence.\n\nConclusions: PolyAnalysis provides a benchmarked framework for nanopore-based poly(A) tail-length estimation and a confidence-aware basis for exploratory 3′ end profiling. Tail-length estimation is the most extensively validated output, whereas sequence-composition, PAS/APA, and ERV-related results remain dependent on model assumptions, sequencing chemistry, and annotation quality and require further orthogonal validation.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "name": "Qiuxiang Tian"
    },
    {
      "name": "Bosheng Song"
    },
    {
      "name": "Quan Zou"
    },
    {
      "name": "Yansu Wang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3ahhgh3tt5kzx.bib",
  "cited_by": 0,
  "doi": "10.1186/s12915-026-02749-7",
  "fulltext_url": null,
  "id": "3ahhgh3tt5kzx",
  "issns": [
    "1741-7007"
  ],
  "landing_url": "https://link.springer.com/10.1186/s12915-026-02749-7",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1312|RNA Research and Splicing"
    ]
  ],
  "openalex": "W7219885609",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-05",
  "publisher": "Springer Science and Business Media LLC",
  "ris": "https://research.draconitic.com/paper/3ahhgh3tt5kzx.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:05Z",
      "native_id": "10.1186/s12915-026-02749-7",
      "source": "crossref",
      "url": "https://link.springer.com/10.1186/s12915-026-02749-7"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "openalex:1312|RNA Research and Splicing",
      "subfield": "molecular-biology"
    }
  ],
  "title": "PolyAnalysis: a deep learning–based framework for nanopore-based poly(A) tail estimation and 3′-end regulation profiling",
  "title_text": "PolyAnalysis: a deep learning–based framework for nanopore-based poly(A) tail estimation and 3′-end regulation profiling",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:19:05Z",
  "url": "https://research.draconitic.com/paper/3ahhgh3tt5kzx",
  "venue": "BMC Biology",
  "versions": []
}