DeepMind launches AlphaGenome to predict how DNA mutations affect genes

Alphabet Inc.’s Google DeepMind today introduced AlphaGenome, a new artificial intelligence tool that can comprehensively predict how mutations or variants in human DNA sequences impact gene regulation.

The genome is the complete set of deoxyribonucleic acid, or DNA, within a living cell, which includes all the genetic information necessary for development, growth and functioning. In humans, the genome consists of 23 pairs of chromosomes located in the nucleus of the cell, and it regulates everything, including the response to the environment and susceptibility to disease.

The new AlphaGenome model can take an extremely long DNA sequence as input — up to 1 million letters, also known as base pairs — and predict thousands of molecular properties. These are the recognizable letters of A, T, C and G.

The properties that it can predict include where genes start and where they end in different cell types and tissues, where they get spliced and the number of proteins they produce. Proteins are the building blocks of tissues and enzymes, which are required to take action in the body. It can also tell how close they are to each other or if they’re bound to other proteins.

DeepMind trained the model on a considerable amount of scientific data from large public consortia that include information about gene regulation, including ENCODE, GTEx, 4D Nucleome and others.

Not only can the AI model “see” a large number of DNA letters at once to make predictions about how the genes will behave, but it can also predict the resolution of individual letters. The long sequence length is crucial for covering regions of regulatory genes that are distant from the originating gene.

“Previous models had to trade off sequence length and resolution, which limited the range of modalities they could jointly model and accurately predict,” the DeepMind team said.

This capability makes the AI model useful for predicting “splice” errors. These errors can cause rare genetic diseases like spinal muscular atrophy and some forms of cystic fibrosis.

Think of DNA as the script for a training video, and ribonucleic acid, or RNA, as the raw footage. Before the final cut, the cell “edits” the RNA, removing unnecessary parts and stitching together the important scenes. But sometimes, the editing goes wrong — key scenes are left out, or extra ones are included — resulting in a flawed final product. These mistakes, called splice junction errors, can disrupt how the body works.

According to DeepMind, AlphaGenome achieves state-of-the-art performance across a wide range of genomic prediction benchmarks, including predicting which parts of the DNA molecule will be in close proximity, whether genetic variants will increase or decrease expression of a gene or if it will change a gene-splicing pattern.

“It’s a milestone for the field. For the first time, we have a single model that unifies long-range context, base-level precision and state-of-the-art performance across a whole spectrum of genomic tasks,” said Dr. Caleb Lareau, a researcher at Memorial Sloan Kettering Cancer Center.

DeepMind said that it expects that AlphaGenome will be a powerful research tool for disease understanding by helping to accurately predict genetic disruptions. It could also be used to help guide the design of synthetic DNA with specific regulatory functions and accelerate the understanding of the genome by assisting in the understanding of its crucial functional elements.

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