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Models that predict how DNA sequence controls gene activity, used to interpret non-coding cancer mutations.
Enformer and Borzoi are sequence-to-function models that predict how DNA controls gene activity by combining convolution with a transformer over long DNA windows. Enformer (Nature Methods 2021, DeepMind) predicts gene expression and chromatin signals from 200 kb of sequence; Borzoi (Nature Genetics 2025, Calico) extends this to predicting RNA-seq coverage and splicing across 500 kb. Both are used to interpret non-coding variants, including candidate cancer drivers in promoters and enhancers, by comparing predictions for reference and mutant sequence. Their cell-type coverage is bounded by the assays in the training data, so effects in tissues or tumour states that were never profiled cannot be predicted reliably. For a newcomer: these models read a long stretch of DNA and predict how a mutation there would change which genes are switched on.
Enformer and Borzoi combine convolution with a transformer over long DNA windows.
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One technology page on OnCo cites this paper by its DOI; this record gives the citation a page of its own so a reader can follow it without leaving OnCo. Read the abstract above alongside the citing page listed under Related; the record was created automatically from the Europe PMC entry and its figures have not been checked by hand.
Query for this technology: (TITLE:"Enformer and Borzoi" OR ABSTRACT:"Enformer and Borzoi" OR TITLE:"DeepMind, Calico" OR ABSTRACT:"DeepMind, Calico") AND (cancer OR tumor OR tumour OR oncology OR carcinoma OR lymphoma OR leukemia OR leukaemia OR myeloma OR sarcoma OR melanoma OR glioma). Results are unfiltered search hits about Enformer and Borzoi (DeepMind, Calico), not a curated reading list.
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Shares Google DeepMind (and Google Research) and the tag foundation-model.
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Shares Google DeepMind (and Google Research) and the tag foundation-model.