Google's AI genome system evaluates every possible one-base change - Ars Technica

Overview

Google’s AI genome system evaluates every possible one-base change

Most one-base changes to the human genome do nothing, but a few are significant.

Details

On Tuesday, Google announced Alpha Genome Atlas, a resource that attempts to predict the consequences of every possible single-base variant in the human genome. The human genome is about 3 billion bases long, so trying the other three DNA bases that don’t appear in our reference genome means sending a total of 9 billion bases through Alpha Genome software.

Alpha Genome is designed to identify potential functions of non-coding DNA, which does not encode proteins but makes up the vast majority of the human genome. Some of this non-coding DNA is essential for controlling the activity of the protein-coding portion—it tells the cell where and when to make messenger RNAs, how to process them into mature protein-coding forms, and so on. But much of it appears to be little more than the remains of viruses and other molecular parasites.

Being able to identify the functional portion is very useful, as is having all the analysis done by a single software package. But until biologists start to use it heavily (assuming they do), it won’t be clear what Alpha Genome offers beyond what we could have gotten out of its training data.

While we tend to focus on proteins, the portion of the human genome that encodes proteins is less than 3 percent. Most of the genome is non-coding and contains a mix of things, including centromeres, which help ensure chromosomes are divided evenly between cells, and caps that protect the chromosome ends. There’s also the regulatory DNA that controls gene activity, along with the signals that help determine what should and shouldn’t be included in mature messenger RNAs produced by genes. Other sequences help control how the DNA is packaged inside the cell.

But most of the non-coding DNA is junk—the remains of ancient viral infections, DNA-level parasites, genes that have been inactivated by mutation, and so on. Figuring out what’s useful and what’s not has been an ongoing challenge for biologists for many reasons.

First, the proteins that interact with DNA aren’t that picky about the sequences they stick to, potentially binding at random throughout the genome and tolerating a certain degree of mutation. Many of these proteins are also cell-type specific; there’s a different population of DNA-binding proteins in liver cells, nerve cells, immune cells, and so on. In many cases, having many different protein binding sites in a compact space matters more than the presence of any one of them.

We’ve developed various software tools that identify individual sites of interest in non-coding DNA. But this is exactly the sort of problem that AI is good at solving: one involving probabilities that are imprecise and rely heavily on context. So Google developed the Alpha Genome AI system, which evaluates sequences for their potential function.

(For the biology geeks that don’t want to sort through the paper, Alpha Genome attempts to identify “gene expression, transcription initiation, chromatin accessibility, histone modifications, transcription factor binding, chromatin contact maps, splice site usage, and splice junction coordinates and strength.”)

The system is currently limited to sequences from mice and humans. It has also only been trained on a limited number of cell types that biologists have studied exhaustively. Still, it can be useful. If a researcher studying a gene finds changes in its non-coding regions, it can be difficult to tell whether they’re likely to be significant. Alpha Genome can provide a hint about their significance, along with a hypothesis about why. And its predictions are generally as good as or better than those from specialized software tools.

Google has now used this system to evaluate all possible single-base changes in the human genome. In other words, if the first base on chromosome 1 is an A, the system evaluates what changes if you swap in a G, C, or T. It then moves on to the next base and repeats the process.

Let’s be clear: Nobody on Earth probably has the reference genome sequence that Google is using as its baseline. Each of us likely differs from it at millions of bases in our genome, and most of us probably carry some combinations of insertions, deletions, duplications, and flipped sequences. Many of these are of no consequence because they fall within sequences that don’t have any functional significance. At the same time, many of the sequences that are non-functional are identical in all humans, simply because there hasn’t been enough time since we’ve had a common ancestor to accumulate changes.

So only a small fraction of the changes that Google evaluated are likely to both show up in an actual human genome and have a functional significance.

Why would Google bother? There are a couple of reasons this could be useful. First, it essentially pre-calculates the potential mutations researchers might be interested in. So if someone finds a mutation from some genome sequencing, they can get an immediate sense of its potential consequences. The second is that it provides researchers the chance to search across the entire genome for changes that have a specific type of impact.

But much of that information could have been obtained simply by looking at the ENCODE dataset that was among the data the model was trained on. The greater potential here is developing the system to the point where it can perform an analysis we can trust on things it has no training data for, such as the Neanderthal and Denisovan genomes or cell types we don’t have ENCODE data for. At the moment, it’s not clear whether we’re there yet.

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Key Takeaways

• Google’s AI genome system evaluates every possible one-base change

• Most one-base changes to the human genome do nothing, but a few are significant

• On Tuesday, Google announced Alpha Genome Atlas, a resource that attempts to predict the consequences of every possible single-base variant in the human genome

• Alpha Genome is designed to identify potential functions of non-coding DNA, which does not encode proteins but makes up the vast majority of the human genome

• Being able to identify the functional portion is very useful, as is having all the analysis done by a single software package