Introducing a new, unifying DNA sequence model that advances regulatory variant-effect prediction and promises to shed new light on genome function — now available via API.

The genome is our cellular instruction manual. It’s the complete set of DNA which guides nearly every part of a living organism, from appearance and function to growth and reproduction. Small variations in a genome’s DNA sequence can alter an organism’s response to its environment or its susceptibility to disease. But deciphering how the genome’s instructions are read at the molecular level — and what happens when a small DNA variation occurs — is still one of biology’s greatest mysteries.

Today, we introduce AlphaGenome, a new artificial intelligence (AI) tool that more comprehensively and accurately predicts how single variants or mutations in human DNA sequences impact a wide range of biological processes regulating genes. This was enabled, among other factors, by technical advances allowing the model to process long DNA sequences and output high-resolution predictions.

An MRI scan revealed a brain tumor located in a difficult area, and performing a biopsy would carry significant risks for the patient, who had initially sought medical help due to double vision. Cases like this, discussed by a multidisciplinary team of cancer specialists, led researchers at Charité – Universitätsmedizin Berlin, along with their collaborators, to search for alternative diagnostic methods.

Their solution is an AI model that analyzes specific features in the genetic material of tumors, particularly their epigenetic fingerprint, which can be obtained from sources such as cerebrospinal fluid. As reported in the journal Nature Cancer, the model classifies tumors both rapidly and with high accuracy.

A new study has revealed compelling evidence that brain criticality—a dynamic balance between neural excitation and inhibition—has a strong genetic foundation and is associated with cognitive performance. The research was published on June 23 in the Proceedings of the National Academy of Sciences.

Led by Prof. Liu Ning from the Institute of Biophysics of the Chinese Academy of Sciences (CAS) and Prof. Yu Shan from the Institute of Automation of CAS, the team analyzed resting-state functional MRI (rs-fMRI) data from the Human Connectome Project S1200 release. The dataset included 250 monozygotic twins, 142 dizygotic twins, and 437 unrelated individuals, providing a robust framework for examining the heritability of critical brain dynamics.

The results showed that brain criticality is significantly influenced by genetic factors, with stronger genetic effects observed in primary sensory cortices compared to higher-order association regions. These findings suggest that the capacity of the brain to maintain near-critical dynamics—previously associated with optimal information processing and cognitive flexibility—is, to a substantial degree, inherited.