{
  "id": 18500899,
  "title": "Towards a foundation model for raw nanopore signal through discrete tokenization",
  "url": "https://urgent.news/2026/11/06/towards-a-foundation-model-for-raw-nanopore-signal-through-discrete",
  "topic": "science",
  "section": "Science",
  "published": "2026-11-06T00:00:00.000Z",
  "source": {
    "name": "bioRxiv",
    "slug": "biorxiv",
    "url": "https://www.biorxiv.org/content/10.64898/2026.11.05.764536v1?rss=1"
  },
  "original_language": "en",
  "account": "Nanopore sequencing deciphers nucleic acids by detecting alterations in ionic current that encode information beyond the base sequence. While self-supervised pretrained models have flourished in genomic sequence modeling and single-cell analysis, their application to raw nanopore signals, which are continuous and unstructured, remains relatively unexplored. This research introduces Brieno, a methodology that fuses reconstruction-based tokenization with residual vector quantization and a Transformer to extract contextual representation.\n\nThe researchers trained the Transformer on masked-token prediction using raw nanopore signals from 118 million reads. With frozen or fine-tuned features, the model can identify species without relying on reference genomes. Brieno augments the Dorado tool, accurately classifying reads that remain uncategorized in native-barcode demultiplexing. Its performance surpasses Dorado's basecalling accuracy across six genomes, and outperforms both a dedicated 5-methylcytosine caller and a specific 6-methyladenine caller.\n\nFurthermore, Brieno excels in precise CpG methylation detection even with minimal labeled data. Performance improvements across various tasks, when compared to non-pretrained baselines, show that the pretrained contextual representations prove advantageous. Moreover, these representations demonstrate a high degree of generalizability across different sequencing runs. Overall, these findings underscore the potential of self-supervised pretraining in crafting transferable representations for raw nanopore signals.",
  "summary": "Nanopore sequencing reads nucleic acids as changes in ionic current that carry information beyond the base sequence. Self-supervised pretrained models have advanced genomic sequence modeling and single-cell analysis, but remain underexplored for raw nanopore signals, which are continuous and lack a native token vocabulary. Here we present Brieno, an approach combining reconstruction-based…",
  "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."
}