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Predicting genome-wide functional constraints with GPN-Star – Nature

Predicting genome-wide functional constraints with GPN-Star – Nature

Benegas, G., Ye, C., Albors, C., Li, J. C. & Song, Y. S. Genomic language models: opportunities and challenges. Trends Genet. 41, 286–302 (2025). Article  CAS  PubMed  Google Scholar  Benegas, G., Eraslan, G. & Song, Y. S. Benchmarking DNA sequence models for causal regulatory variant prediction in human genetics. Preprint at bioRxiv https://doi.org/10.1101/2025.02.11.637758 (2025). Dalla-Torre,

  • Benegas, G., Ye, C., Albors, C., Li, J. C. & Song, Y. S. Genomic language models: opportunities and challenges. Trends Genet. 41, 286–302 (2025).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Benegas, G., Eraslan, G. & Song, Y. S. Benchmarking DNA sequence models for causal regulatory variant prediction in human genetics. Preprint at bioRxiv https://doi.org/10.1101/2025.02.11.637758 (2025).

  • Dalla-Torre, H. et al. Nucleotide transformer: building and evaluating robust foundation models for human genomics. Nat. Methods 22, 287–297 (2025).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Brixi, G. et al. Genome modelling and design across all domains of life with Evo 2. Nature 652, 1349–1361 (2026).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Clarke, B. et al. Integration of variant annotations using deep set networks boosts rare variant association testing. Nat. Genet. 56, 2271–2280 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Dayhoff, M. O., Schwartz, R. M. & Orcutt, B. C. A model of evolutionary change in proteins. Atlas Protein Seq. Struct. 5, 345–352 (1978).

  • Sullivan, P. F. et al. Leveraging base-pair mammalian constraint to understand genetic variation and human disease. Science 380, eabn2937 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Kuderna, L. F. et al. Identification of constrained sequence elements across 239 primate genomes. Nature 625, 735–742 (2024).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 

  • Benegas, G., Batra, S. S. & Song, Y. S. DNA language models are powerful predictors of genome-wide variant effects. Proc. Natl Acad. Sci. USA 120, e2311219120 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Rao, R. M. et al. MSA Transformer. In Proc. 38th Int. Conf. on Machine Learning vol. 139 (eds Meila, M. & Zhang, T.) 8844–8856 (PMLR, 2021).

  • Frazer, J. et al. Disease variant prediction with deep generative models of evolutionary data. Nature 599, 91–95 (2021).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 

  • Truong, T. Jr & Bepler, T. PoET: a generative model of protein families as sequences-of-sequences. Adv. Neural Info. Process. Syst. 36, 77379–77415 (2023).

    Google Scholar 

  • Yang, K. K. et al. The Dayhoff Atlas: scaling sequence diversity for improved protein generation. Preprint at bioRxiv https://doi.org/10.1101/2025.07.21.665991 (2025).

  • Akiyama, Y. et al. Expanding the scope of protein language modeling to protein-protein interactions with MSA pairformer. Cell 189, 4964–4979 (2026).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Blanchette, M. et al. Aligning multiple genomic sequences with the threaded blockset aligner. Genome Res. 14, 708–715 (2004).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Armstrong, J. et al. Progressive Cactus is a multiple-genome aligner for the thousand-genome era. Nature 587, 246–251 (2020).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Siepel, A. et al. Evolutionarily conserved elements in vertebrate, insect, worm, and yeast genomes. Genome Res. 15, 1034–1050 (2005).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Pollard, K. S., Hubisz, M. J., Rosenbloom, K. R. & Siepel, A. Detection of nonneutral substitution rates on mammalian phylogenies. Genome Res. 20, 110–121 (2010).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Christmas, M. J. et al. Evolutionary constraint and innovation across hundreds of placental mammals. Science 380, eabn3943 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Rhie, A. et al. Towards complete and error-free genome assemblies of all vertebrate species. Nature 592, 737–746 (2021).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Benegas, G., Albors, C., Aw, A. J., Ye, C. & Song, Y. S. A DNA language model based on multispecies alignment predicts the effects of genome-wide variants. Nat. Biotechnol. 43, 1960–1965 (2025).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Kim, A. et al. Identifying independent causal cell types for human diseases and risk variants. Cell Genom. https://doi.org/10.1016/j.xgen.2026.101325 (2026).

  • Landrum, M. J. et al. ClinVar: public archive of relationships among sequence variation and human phenotype. Nucleic Acids Res. 42, D980–D985 (2014).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Rentzsch, P., Witten, D., Cooper, G. M., Shendure, J. & Kircher, M. CADD: predicting the deleteriousness of variants throughout the human genome. Nucleic Acids Res. 47, D886–D894 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Rives, A. et al. Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences. Proc. Natl Acad. Sci. USA 118, e2016239118 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Lin, Z. et al. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science 379, 1123–1130 (2023).

    Article 
    ADS 
    MathSciNet 
    CAS 
    PubMed 

    Google Scholar 

  • Hayes, T. et al. Simulating 500 million years of evolution with a language model. Science 387, 850858 (2025).

    Article 
    ADS 

    Google Scholar 

  • Tate, J. G. et al. COSMIC: the catalogue of somatic mutations in cancer. Nucleic Acids Res. 47, D941–D947 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Chen, S. et al. A genomic mutational constraint map using variation in 76,156 human genomes. Nature 625, 92–100 (2024).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 

  • Notin, P. et al. ProteinGym: large-scale benchmarks for protein fitness prediction and design. Adv. Neural Info. Process. Syst. 36, 64331–64379 (2023).

    Article 

    Google Scholar 

  • Cheng, J. et al. Accurate proteome-wide missense variant effect prediction with AlphaMissense. Science 381, eadg7492 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Gao, H. et al. The landscape of tolerated genetic variation in humans and primates. Science 380, eabn8153 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Ghosh, R. et al. Updated recommendation for the benign stand-alone ACMG/AMP criterion. Hum. Mutat. 39, 1525–1530 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Avsec, Ž et al. Effective gene expression prediction from sequence by integrating long-range interactions. Nat. Methods 18, 1196–1203 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Linder, J., Srivastava, D., Yuan, H., Agarwal, V. & Kelley, D. R. Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulation. Nat. Genet. 57, 949–961 (2025).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Avsec, Ž et al. Advancing regulatory variant effect prediction with AlphaGenome. Nature 649, 1206–1218 (2026).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Amberger, J. S., Bocchini, C. A., Schiettecatte, F., Scott, A. F. & Hamosh, A. OMIM.org: Online Mendelian Inheritance in Man (OMIM), an online catalog of human genes and genetic disorders. Nucleic Acids Res. 43, D789–D798 (2015).

    Article 
    PubMed 

    Google Scholar 

  • Stenson, P. D. et al. The Human Gene Mutation Database (HGMD): optimizing its use in a clinical diagnostic or research setting. Hum. Genet. 139, 1197–1207 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Jaganathan, K. et al. Predicting expression-altering promoter mutations with deep learning. Science 389, eads7373 (2025).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Tomaz da Silva, P. et al. Nucleotide dependency analysis of genomic language models detects functional elements. Nat. Genet. 57, 2589–2602 (2025).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Kanai, M. et al. Insights from complex trait fine-mapping across diverse populations. Preprint at medRxiv https://doi.org/10.1101/2021.09.03.21262975 (2021).

  • Bomba, L., Walter, K. & Soranzo, N. The impact of rare and low-frequency genetic variants in common disease. Genome Biol. 18, 77 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Lee, S., Abecasis, G. R., Boehnke, M. & Lin, X. Rare-variant association analysis: study designs and statistical tests. Am. J. Hum. Genet. 95, 5–23 (2014).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Backman, J. D. et al. Exome sequencing and analysis of 454,787 UK Biobank participants. Nature 599, 628–634 (2021).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Karczewski, K. J. et al. Systematic single-variant and gene-based association testing of thousands of phenotypes in 394,841 UK Biobank exomes. Cell Genom. 2, 100168 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Zhou, J. & Troyanskaya, O. G. Predicting effects of noncoding variants with deep learning–based sequence model. Nat. Methods 12, 931–934 (2015).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Finucane, H. K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228–1235 (2015).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Weissbrod, O. et al. Functionally informed fine-mapping and polygenic localization of complex trait heritability. Nat. Genet. 52, 1355–1363 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Márquez-Luna, C. et al. Incorporating functional priors improves polygenic prediction accuracy in UK Biobank and 23andMe data sets. Nat. Commun. 12, 6052 (2021).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • O’Connor, L. J. & Sella, G. Principled measures and estimates of trait polygenicity. Preprint at bioRxiv https://doi.org/10.1101/2025.07.10.664154 (2025).

  • Karollus, A., Mauermeier, T. & Gagneur, J. Current sequence-based models capture gene expression determinants in promoters but mostly ignore distal enhancers. Genome Biol. 24, 56 (2023).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Fabiha, T. et al. A consensus variant-to-function score to functionally prioritize variants for disease. Preprint at bioRxiv https://doi.org/10.1101/2024.11.07.622307 (2024).

  • Finucane, H. K. et al. Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types. Nat. Genet. 50, 621–629 (2018).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Zhang, Z. et al. Protein language models learn evolutionary statistics of interacting sequence motifs. Proc. Natl Acad. Sci. USA 121, e2406285121 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Song, B., Buckler, E. S. & Stitzer, M. C. New whole-genome alignment tools are needed for tapping into plant diversity. Trends Plant Sci. 29, 355–369 (2024).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Öztürk-Çolak, A. et al. FlyBase: updates to the Drosophila genes and genomes database. Genetics 227, iyad211 (2024).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Qin, Z. et al. Genomic identification and functional characterization of essential genes in Caenorhabditis elegans. Genes Genomes Genet. 8, 981–997 (2018).

    Article 
    CAS 

    Google Scholar 

  • Small, S., Blair, A. & Levine, M. Regulation of even-skipped stripe 2 in the Drosophila embryo. EMBO J. 11, 4047–4057 (1992).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Wong, E. S. et al. Deep conservation of the enhancer regulatory code in animals. Science 370, eaax8137 (2020).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 

  • Lewin, H. A. et al. Earth BioGenome project: sequencing life for the future of life. Proc. Natl Acad. Sci. USA 115, 4325–4333 (2018).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Seplyarskiy, V. et al. A mutation rate model at the basepair resolution identifies the mutagenic effect of polymerase III transcription. Nat. Genet. 55, 2235–2242 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Ye, C., Benegas, G., Albors, C., Li, J. C. & Song, Y. S. GPN-Star model source code. Zenodo https://doi.org/10.5281/zenodo.21501177 (2026).

  • Verbeek, M. M. et al. Mutations in the cyclic adenosine monophosphate response element of the tyrosine hydroxylase gene. Ann. Neurol. 62, 422–426 (2007).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Kircher, M. et al. Saturation mutagenesis of twenty disease-associated regulatory elements at single base-pair resolution. Nat. Commun. 10, 3583 (2019).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Bennett, M. K., Ngo, T. T., Athanikar, J. N., Rosenfeld, J. M. & Osborne, T. F. Co-stimulation of promoter for low density lipoprotein receptor gene by sterol regulatory element-binding protein and Sp1 is specifically disrupted by the yin yang 1 protein. J. Biol. Chem. 274, 13025–13032 (1999).

    Article 
    CAS 
    PubMed 

    Google Scholar 

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