ArticleNature biotechnology2025
A DNA language model based on multispecies alignment predicts the effects of genome-wide variants.
Article in Nature biotechnology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers.
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Who cites it
50 citing papers in PubMed.
- Machine learning and statistical methods for molecular quantitative trait loci.Nature reviews. Genetics · 2026Review
- Article
- PlantCAD2: A DNA foundation model for interpreting genomes across flowering plants.Cell genomics · 2026Article
- Prediction of human missense variant effects from functional evidence.Nature genetics · 2026Article
- Base-pair resolution conservation data improves cell type specific sequence-to-expression prediction.Bioinformatics (Oxford, England) · 2026Article
- Bridging precision agriculture and human medicine through comparative genetics.Nature reviews. Genetics · 2026Review
- Integrating deep learning and pangenomics to recover missing heritability from wild structural variations.BMC genomics · 2026Review
- Toward generalizable and interpretable AI in regulatory genomics.Nature genetics · 2026Review
- Genomic constraint and hypervariability correlate with fitness effects in elite tetraploid potato breeding material.PLoS genetics · 2026Article
- Advancing bioinformatics with language models: components, applications, and perspectives.Briefings in bioinformatics · 2026Review
- GraphyloVar: predicting the impact of non-coding variants using a multi-species sequence model.Bioinformatics (Oxford, England) · 2026Article
- Epigenetic conditioning improves sequence-based modeling of gene regulation across cell types and alleles.bioRxiv : the preprint server for biology · 2026Article
- Causal inference clarifies the roles of background selection and mutation rate variation in shaping human genetic diversity.bioRxiv : the preprint server for biology · 2026Article
- CodonMoE: DNA language models for codon-dependent mRNA prediction.Bioinformatics (Oxford, England) · 2026Article
- Generalist biological artificial intelligence in modeling the language of life.Nature biotechnology · 2026Review
- Artificial intelligence in the assessment of epilepsy-related genetic mutations: Learned from GABAEpilepsia open · 2026Review
- Evolutionary transfer learning enables organism-wide inference of mammalian enhancer landscapes.bioRxiv : the preprint server for biology · 2026Article
- Bio-BLIP: A Multimodal Architecture for Transferable Reasoning in Genomic Variant Interpretation.bioRxiv : the preprint server for biology · 2026Article
- Pathogenicity prediction for noncanonical splice-altering variants based on multimodal feature fusion.Briefings in bioinformatics · 2026Article
- Porcine MutBERT: a family of lightweight genomic foundation models for functional element prediction in pigs.Briefings in bioinformatics · 2026Article
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Authors and funding
5 authors.
Funding
Abstract
Protein language models have demonstrated remarkable performance in predicting the effects of missense variants but DNA language models have not yet shown a competitive edge for complex genomes such as that of humans. This limitation is particularly evident when dealing with the vast complexity of noncoding regions that comprise approximately 98% of the human genome. To tackle this challenge, we introduce GPN-MSA (genomic pretrained network with multiple-sequence alignment), a framework that leverages whole-genome alignments across multiple species while taking only a few hours to train. Across several benchmarks on clinical databases (ClinVar, COSMIC and OMIM), experimental functional assays (deep mutational scanning and DepMap) and population genomic data (gnomAD), our model for the human genome achieves outstanding performance on deleteriousness prediction for both coding and noncoding variants. We provide precomputed scores for all ~9 billion possible single-nucleotide variants in the human genome. We anticipate that our advances in genome-wide variant effect prediction will enable more accurate rare disease diagnosis and improve rare variant burden testing.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.