{
  "id": 15540843,
  "title": "DeepD enables efficient genome modeling and controllable long-context sequence generation across domains of life",
  "url": "https://urgent.news/2026/10/18/deepd-enables-efficient-genome-modeling-and-controllable-long-context",
  "topic": "science",
  "section": "Science",
  "published": "2026-10-18T00:00:00.000Z",
  "source": {
    "name": "bioRxiv",
    "slug": "biorxiv",
    "url": "https://www.biorxiv.org/content/10.64898/2026.10.13.758170v1?rss=1"
  },
  "original_language": "en",
  "account": "Genome foundation models have the potential to revolutionize biological research, yet modeling intricate genomes and creating controllable, genome-scale sequences pose significant challenges. DeepD, a novel phylogeny-informed sparse mixture-of-experts model, addresses these issues by operating at the single-nucleotide level across a 1-million-token context, all while minimizing active computation. Trained on a diverse range of whole-genome sequences from every domain of life, DeepD learns both genic and intergenic sequence features, enabling broad sequence-to-function predictions across various species. This model excels in predicting human protein fitness and the effects of disease variants, such as those found in BRCA1 and BRCA2, with a particular emphasis on regulatory regions like promoters and transcription-factor binding sites. DeepD recognizes exon-intron boundaries, transposable elements, and distal cis-regulatory elements through expert routing. In addition to its predictive capabilities, DeepD can generate experimentally validated core promoters with specified transcriptional strengths. The innovative Context-Anchor-Body strategy allows DeepD to generate complete yeast chromosome sequences and megabase-scale Arabidopsis segments, preserving gene structure and order while introducing natural variations within certain ranges. In summary, DeepD represents an efficient genome foundation model that seamlessly combines accurate sequence-to-function prediction with controllable, genome-scale generation, driving forward the predictive and generative design of complex genomes.",
  "summary": "Genome foundation models hold great potential for advancing biological research, but modeling diverse complex genomes and generating controllable genome-scale sequences remain challenging. We present DeepD, a phylogeny-informed sparse mixture-of-experts model operating at single-nucleotide resolution over a 1-million-token context while reducing active computation. Pretrained on unselected…",
  "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."
}