Home NATURALEZA A DNA language model based on multispecies alignment predicts the effects of...

A DNA language model based on multispecies alignment predicts the effects of genome-wide variants

5
0


  • Goldfeder, R. L., Wall, D. P., Khoury, M. J., Ioannidis, J. P. & Ashley, E. A. Human genome sequencing at the population scale: a primer on high-throughput DNA sequencing and analysis. Am. J. Epidemiol. 186, 1000–1009 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Marwaha, S., Knowles, J. W. & Ashley, E. A. A guide for the diagnosis of rare and undiagnosed disease: beyond the exome. Genome Med. 14, 23 (2022).

  • 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
     

  • Trajanoska, K. et al. From target discovery to clinical drug development with human genetics. Nature 620, 737–745 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Meier, J. et al. Language models enable zero-shot prediction of the effects of mutations on protein function. In Proc. Advances in Neural Information Processing Systems 34 (eds Ranzato, M. et al.) 29287–29303 (Curran Associates, Inc., 2021).

  • Brandes, N., Goldman, G., Wang, C. H., Ye, C. J. & Ntranos, V. Genome-wide prediction of disease variant effects with a deep protein language model. Nat. Genet. 55, 1512–1522 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Jagota, M. et al. Cross-protein transfer learning substantially improves disease variant prediction. Genome Biol. 24, 182 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    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
     

  • Dalla-Torre, H. et al. Nucleotide Transformer: building and evaluating robust foundation models for human genomics. Nat. Methods https://doi.org/10.1038/s41592-024-02523-z (2024).

    Article 
    PubMed 

    Google Scholar
     

  • Vaswani, A. et al. Attention is all you need. In Proc. Advances in Neural Information Processing Systems 30 (eds Guyon, S. et al.) 6000–6010 (Curran Associates, Inc., 2017).

  • Armstrong, J., Fiddes, I. T., Diekhans, M. & Paten, B. Whole-genome alignment and comparative annotation. Annu. Rev. Anim. Biosci. 7, 41–64 (2019).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Nguyen, E. et al. HyenaDNA: long-range genomic sequence modeling at single nucleotide resolution. In Proc. 37th International Conference on Neural Information Processing Systems (eds Oh, A. et al.) 43177–43201 (Curran Associates, Inc., 2023).

  • Rentzsch, P., Schubach, M., Shendure, J. & Kircher, M. CADD-Splice-improving genome-wide variant effect prediction using deep learning-derived splice scores. Genome Med. 13, 31 (2021).

    Article 
    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 
    PubMed Central 

    Google Scholar
     

  • 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
     

  • 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
     

  • 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
     

  • Jaganathan, K. et al. Predicting splicing from primary sequence with deep learning. Cell 176, 535–548.e24 (2019).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Rao, R. M. et al. MSA Transformer. In Proceedings of the 38th International Conference on Machine Learning (eds Meila, M. & Zhang, T.) (PMLR, 2021).

  • Paten, B. et al. Genome-wide nucleotide-level mammalian ancestor reconstruction. Genome Res. 18, 1829–1843 (2008).

    Article 
    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 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Landrum, M. J. et al. ClinVar: improvements to accessing data. Nucleic Acids Res. 48, D835–D844 (2020).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

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

    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 

    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 

    Google Scholar
     

  • Notin, P. et al. ProteinGym: large-scale benchmarks for protein fitness prediction and design. In Proceedings of the Advances in Neural Information Processing Systems 37 (eds Oh, A. et al.) (NeurIPS, 2023).

  • Smedley, D. et al. A whole-genome analysis framework for effective identification of pathogenic regulatory variants in mendelian disease. Am. J. Hum. Genet. 99, 595–606 (2016).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Albuisson, J. et al. Identification of two novel mutations in Shh long-range regulator associated with familial pre-axial polydactyly. Clin. Genet. 79, 371–377 (2011).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Kvon, E. Z. et al. Comprehensive in vivo interrogation reveals phenotypic impact of human enhancer variants. Cell 180, 1262–1271.e15 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Arbini, A. A., Pollak, E. S., Bayleran, J. K., High, K. A. & Bauer, K. A. Severe factor VII deficiency due to a mutation disrupting a hepatocyte nuclear factor 4 binding site in the factor VII promoter. Blood 89, 176–182 (1997).

    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
     

  • The Dependency Map Consortium. DepMap 23Q4 public. figshare https://doi.org/10.25452/figshare.plus.24667905.v2 (2023).

  • Karczewski, K. J. et al. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature 581, 434–443 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Agarwal, I., Fuller, Z. L., Myers, S. R. & Przeworski, M. Relating pathogenic loss-of-function mutations in humans to their evolutionary fitness costs. eLife 12, e83172 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zeng, T., Spence, J. P., Mostafavi, H. & Pritchard, J. K.Bayesian estimation of gene constraint from an evolutionary model with gene features. Nat. Genet. 56, 1632–1643 (2024).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Richards, S. et al. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet. Med. 17, 405–424 (2015).

  • Schneider, T. D. & Stephens, R. M. Sequence logos: a new way to display consensus sequences. Nucleic Acids Res. 18, 6097–6100 (1990).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kent, W. J. et al. The human genome browser at UCSC. Genome Res. 12, 996–1006 (2002).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Nair, S. et al. The dynseq browser track shows context-specific features at nucleotide resolution. Nat. Genet. 54, 1581–1583 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Fishman, V. et al. GENA-LM: a family of open-source foundational models for long DNA sequences. Preprint at bioRxiv https://doi.org/10.1101/2023.06.12.544594 (2023).

  • Borgeaud, S. et al. Improving language models by retrieving from trillions of tokens. In Proc. 39th International Conference on Machine Learning (eds Chaudhuri, K. et al.) 2206–2240 (PMLR, 2022).

  • Weiner, D. J. et al. Polygenic architecture of rare coding variation across 394,783 exomes. Nature 614, 492–499 (2023).

    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 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Aw, A. J., McRae, J., Rahmani, E. & Song, Y. S. Highly parameterized polygenic scores tend to overfit to population stratification via random effects. Preprint at bioRxiv https://doi.org/10.1101/2024.01.27.577589 (2024).

  • 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
     

  • Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. BERT: pre-training of deep bidirectional transformers for language understanding. In Proc. 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (eds Burstein, J. et al.) 4171–4186 (Association for Computational Linguistics, 2018).

  • Su, J. et al. RoFormer: enhanced transformer with rotary position embedding. Neurocomputing 568, 127063 (2024).

    Article 

    Google Scholar
     

  • Wolf, T. et al. Transformers: state-of-the-art natural language processing. In Proc. 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations (eds Liu, Q. & Schlangen, D.) 38–45 (Association for Computational Linguistics, 2020).

  • McLaren, W. et al. The Ensembl variant effect predictor. Genome Biol. 17, 122 (2016).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Bolognesi, B. et al. The mutational landscape of a prion-like domain. Nat. Commun. 10, 4162 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Consortium, G. P. et al. A global reference for human genetic variation. Nature 526, 68–74 (2015).

    Article 

    Google Scholar
     

  • Zhou, H. et al. FAVOR: functional annotation of variants online resource and annotator for variation across the human genome. Nucleic Acids Res. 51, D1300–D1311 (2023).

    Article 
    PubMed 

    Google Scholar
     

  • McVicker, G., Gordon, D., Davis, C. & Green, P. Widespread genomic signatures of natural selection in hominid evolution. PLoS Genet. 5, e1000471 (2009).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Gazal, S. et al. Linkage disequilibrium-dependent architecture of human complex traits shows action of negative selection. Nat. Genet. 49, 1421–1427 (2017).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Benegas, G., Albors, C., Aw, A. J., Ye, C. & Song, Y. S. GPN repository. GitHub https://github.com/songlab-cal/gpn (2024).



  • Source link

    LEAVE A REPLY

    Please enter your comment!
    Please enter your name here