{
  "abstract": "Biomolecular condensates are dynamic membraneless assemblies that organize various cellular processes through liquid-liquid phase separation (LLPS). RNA is an active regulator of biomolecular condensates and influence their formation through sequence-encoded features. Despite its established role in condensate formation, no computational framework exists to identify underlying signatures to predict LLPS-associated RNAs. We develop an interpretable hybrid framework to distinguish reviewed, experimentally supported LLPS-associated RNAs from background RNAs. We uncover biologically meaningful sequence properties associated with condensate participation. Each RNA is represented by 167 handcrafted descriptors capturing composition, k-mer usage, sequence complexity, repeat organization and dinucleotide autocorrelation together with 640-dimensional contextual embeddings from RNA-FM. These representations are integrated through cross-validated stacking with neural-network meta-learner. The framework achieves an AUC-ROC of 0.935, AUC-PR of 0.944 and MCC of 0.782. Feature statistics, ablation analysis and SHAP interpretation reveal a distributed sequence architecture dominated by altered local k-mer usage with additional contributions from low-complexity burden, repeat-associated organization and long-range dinucleotide pattern. Biological case studies including MALAT1 and TMPO-AS1 further connect these patterns with experimentally characterized modes of multivalent RNA-protein assembly. Together, the results show how interpretable machine learning can move beyond prediction to identify sequence features that generate mechanistic hypotheses for RNA-mediated condensation.",
  "abstract_shown": true,
  "affiliations": [
    "Indian Institute of Technology Kharagpur"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "name": "S. Chatterjee"
    },
    {
      "affs": [
        0
      ],
      "name": "R. P. Bahadur"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5614a9qvzht26.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.23.761066",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.23.761066v1.full",
  "id": "5614a9qvzht26",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.23.761066v1",
  "language": null,
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "bioinformatics"
    ],
    [
      "openalex",
      "1312|RNA Research and Splicing"
    ]
  ],
  "openalex": "W7223553761",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.23.761066v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-23",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/5614a9qvzht26.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:34Z",
      "native_id": "10.64898/2026.10.23.761066",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.23.761066v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "Sequence architecture of RNA reveals key molecular signatures driving RNA phase separation",
  "title_text": "Sequence architecture of RNA reveals key molecular signatures driving RNA phase separation",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:34Z",
  "url": "https://research.draconitic.com/paper/5614a9qvzht26",
  "venue": "bioRxiv",
  "versions": []
}