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Volume 649 Issue 8099, 29 January 2026
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Volume 649 Issue 8099, 29 January 2026

DNA decoder

Genetic variations affect biological processes and can drive disease, but deciphering the precise effects of DNA sequence changes remains a significant challenge. This is especially true when the variations occur in regions that do not encode protein sequences — as is the case for around 98% of the observed genetic variation in humans. Although deep-learning models have shown promise in this area, they are typically limited by a trade-off between input sequence length and prediction resolution. In this week’s issue, Žiga Avsec and colleagues at Google DeepMind present a deep-learning model called AlphaGenome that can predict a wide array of genome features and variant effects with high precision using 1 million base-pairs of DNA sequence. Trained on human and mouse genomes, AlphaGenome can simultaneously predict 5,930 human or 1,128 mouse genomic signals related to functions such as gene expression, DNA accessibility and splicing. The researchers note that AlphaGenome could help pinpoint the causes of genetic diseases, guide the design of synthetic DNA and accelerate fundamental understanding of the genome.

Cover image: Wes Cockx and the Google DeepMind Creative Team.

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