ArticleNature methods2025
Annotating the genome at single-nucleotide resolution with DNA foundation models.
Article in Nature methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- GenART: Reading the genome's language with adaptive "words".Cell reports methods · 2026Article
- Review
- PlantGFM: A Genomic Foundation Model for Discovery and Creation of Plant Genes.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- EvoSNR-Prom: Predicting promoters at single-nucleotide resolution with label-aware transfer learning of the pretrained EVO model.PLoS computational biology · 2026Article
- Systematic contextual biases in SegmentNT potentially relevant to other nucleotide transformer models.Nucleic acids research · 2026Article
- An overview of self-supervised deep learning applications to molecular data.Briefings in bioinformatics · 2026Review
- Improving splice site usage prediction with SPLAIRE.bioRxiv : the preprint server for biology · 2026Article
- Systematic contextual biases in SegmentNT potentially relevant to other nucleotide transformer models.bioRxiv : the preprint server for biology · 2026Article
- TPCAV: Interpreting deep learning genomics models via concept attribution.bioRxiv : the preprint server for biology · 2026Article
- Article
- The DNA dialect: a comprehensive guide to pretrained genomic language models.Molecular systems biology · 2026Review
- Benchmarking foundation models for splice site and exon annotation.bioRxiv : the preprint server for biology · 2026Article
- CLAMP: predicting specific protein-mediated chromatin loops in diverse species with a chromatin accessibility language model.Genome biology · 2026Article
- A comprehensive survey of genome language models in bioinformatics.Briefings in bioinformatics · 2026Review
- GeneCAD: Plant Genome Annotation with a DNA Foundation Model.bioRxiv : the preprint server for biology · 2025Article
- Comparative Analysis of Deep Learning Models for Predicting Causative Regulatory Variants.Genes · 2025Article
- CIMT 2024: Report on the 21st Annual Meeting of the Association for Cancer Immunotherapy.Human vaccines & immunotherapeutics · 2024Article
- Article
- Are genomic language models all you need? Exploring genomic language models on protein downstream tasks.Bioinformatics (Oxford, England) · 2024Article
- Recent advances in deep learning and language models for studying the microbiome.Frontiers in genetics · 2024Review
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Authors and funding
18 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Genome annotation models that directly analyze DNA sequences are indispensable for modern biological research, enabling rapid and accurate identification of genes and other functional elements. Current annotation tools are typically developed for specific element classes and trained from scratch using supervised learning on datasets that are often limited in size. Here we frame the genome annotation problem as multilabel semantic segmentation and introduce a methodology for fine-tuning pretrained DNA foundation models to segment 14 different genic and regulatory elements at single-nucleotide resolution. We leverage the self-supervised pretrained model Nucleotide Transformer to develop a general segmentation model, SegmentNT, capable of processing DNA sequences up to 50-kb long and that achieves state-of-the-art performance on gene annotation, splice site and regulatory elements detection. We also integrated in our framework the foundation models Enformer and Borzoi, extending the sequence context up to 500 kb and enhancing performance on regulatory elements. Finally, we show that a SegmentNT model trained on human genomic elements generalizes to different species, and a multispecies SegmentNT model achieves strong generalization across unseen species. Our approach is readily extensible to additional models, genomic elements and species.
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