Show Notes
Avsec et al., Advancing regulatory variant effect prediction with AlphaGenome. Nature 649, 1206–1218 ( - AlphaGenome, a 1 Mb DNA deep‑learning model, predicts base‑pair‑resolution genome tracks (RNA‑seq, splicing, chromatin) and scores variant effects, achieving state‑of‑the‑art performance across modalities.
Study Highlights:
AlphaGenome is a unified sequence‑to‑function deep learning model trained on human and mouse genomes that consumes 1 Mb of DNA and predicts 5,930 human genome tracks across 11 modalities using a U‑Net‑inspired encoder, transformer tower and decoder. The model was pretrained with fold splits and distilled into a single student model for efficient variant scoring, enabling base‑pair resolution outputs and splice junction prediction alongside splice site usage and RNA‑seq coverage. Quantitatively, AlphaGenome outperformed or matched external models on 22 of 24 genome track tasks and on 25 of 26 variant effect benchmarks, improving eQTL sign prediction and QTL effect correlations. The multimodal outputs enable mechanistic interpretation of variants, for example recapitulating oncogenic TAL1 enhancer mutations and identifying splice‑disrupting variants.
Conclusion:
AlphaGenome provides a unified 1‑Mb multimodal, base‑resolution sequence model that substantially improves genome track and regulatory variant effect prediction and enables mechanistic, cross‑modality interpretation.
Music:
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Reference:
Avsec, Ž. et al. Advancing regulatory variant effect prediction with AlphaGenome. Nature 649, 1206–1218 (2026). https://doi.org/10.1038/s41586-025-10014-0
License:
This episode is based on an open-access article published under the Creative Commons Attribution 4.0 International License (CC BY 4.0) - https://creativecommons.org/licenses/by/4.0/
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