{
  "id": 15433681,
  "title": "RiboGRAM learns multi-scale non-coding RNA grammar for structural and regulatory inference",
  "url": "https://urgent.news/2026/10/18/ribogram-learns-multi-scale-non-coding-rna-grammar-for-structural-and",
  "topic": "ai",
  "section": "AI",
  "published": "2026-10-18T00:00:00.000Z",
  "source": {
    "name": "bioRxiv",
    "slug": "biorxiv",
    "url": "https://www.biorxiv.org/content/10.64898/2026.10.12.758266v1?rss=1"
  },
  "original_language": "en",
  "account": "For centuries, protein-coding sequences have been deciphered using the triplet genetic code, but the arrangement of information within non-coding RNA sequences has remained elusive. A recent study has unveiled a multi-scale \"grammar\" governing non-coding RNA organization, paving the way for advanced structural and regulatory inference.\n\nResearchers have identified two key features of non-coding RNA grammar: multi-scale motif usage and positional gradients from 5' to 3'. By developing a model called RiboGRAM, scientists sought to learn this organizational framework across nine organisms. The findings revealed that non-coding RNAs exhibit scale-dependent divergence from coding sequences, presenting directional U-rich and AU-rich gradients.\n\nThe RiboGRAM model combines multi-scale sequence encoding with contextual modeling to organize RNA families. It reveals motif-associated and pair-like patterns across different layers of organization. Remarkably, non-coding RNA pretraining without structural supervision outperformed coding or messenger RNA pretraining in base-pair recovery. The model's representations enable alignment-free diffusion-based 3D modeling of short RNAs.\n\nIn a human-specific analysis, RiboGRAM identified position-resolved motif organization across protein targets and cell types. The study found sequence-preference shifts associated with reduced cross-cell transferability, highlighting the importance of non-coding RNA grammar for accurate structural and regulatory inference.\n\nBy establishing a learnable grammar of non-coding RNA sequence organization, this research marks a significant step forward in understanding these critical yet enigmatic molecular entities.",
  "summary": "Protein-coding sequences are read through the triplet genetic code, whereas the organization of molecular information within non-coding RNA sequences remains elusive. Here, we identify a non-coding RNA grammar defined by multi-scale motif usage and 5'-to-3' positional gradients, and develop RiboGRAM to learn this organization. Across nine organisms, non-coding RNAs showed scale-dependent…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}