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Borzoi decodes the complex DNA signals governing gene regulation

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Borzoi is a deep learning model that predicts RNA sequencing coverage across each exon of every human gene, across different cells and tissues, based on DNA sequence alone.

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Fig. 1: Sourcing DNA sequences alone, Borzoi can predict transcript content and expression levels across tissues for every human gene.

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  • 24 April 2025

    In the version of this article initially published, in the legend to Fig. 1c, definitions of “Dotted traces” and “filled traces,” as current, respectively, were originally in the wrong order and are now amended in the HTML and PDF versions of the article.

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Acknowledgements

S.T.C. is supported by a National Health Medical Research Council (NHMRC) of Australia Investigator Grant Level L3 (APP2017952, 2023-2027) and NHMRC Medical Research Future Fund Genomics Health Futures Mission grant entitled ‘RNA for Rare Disease’ (MRF2015930, 2022-2025). S.T.C. receives additional research funding from Lenity Australia, a not-for-profit philanthropic organization, and the Children’s Medical Research Institute’s Genes for Genes Foundation, a registered charity. S.T.C. in her role as scientific director of Kids Neuroscience Centre (KNC) receives support for KNC Research Operations from the Sydney Children’s Hospitals Foundation, a registered charity.

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Correspondence to Sandra T. Cooper.

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S.T.C. is a volunteer member of ClinGen Expert Panels: Muscular Dystrophies and Myopathies GCEP and Limb Girdle Muscular Dystrophy VCEP. S.T.C. is named inventor of intellectual property (IP) relating to novel methods to identify splicing variants: 1) WO2020097660A1, inventors S. Cooper and H. Joshi; 2) WO2020/181333, inventor S. Cooper. This IP is owned jointly by The University of Sydney and Sydney Children’s Hospitals Network. S.T.C. is director of Frontier Genomics Pty Australia, which has licensed this IP. S.T.C. currently receives no remuneration for this director role.

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Cooper, S.T. Borzoi decodes the complex DNA signals governing gene regulation. Nat Genet 57, 777–779 (2025). https://doi.org/10.1038/s41588-025-02154-w

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