Machine learning spots treasure trove of elusive viruses

Many viruses are difficult to study because they cannot be grown in the lab. Artificial intelligence could speed up metagenomic studies that look for species unknown to science. 

Although viruses influence everything from human health to the degradation of trash, they are hard to study. Scientists cannot grow most viruses in the lab, and attempts to identify their genetic sequences are often thwarted because their genomes are tiny and evolve fast.

In recent years, researchers have hunted for unknown viruses by sequencing DNA in samples taken from various environments. To identify the microbes present, researchers search for the genetic signatures of known viruses and bacteria — just as a word processor’s ‘find’ function highlights words containing particular letters in a document. But that method often fails, because virologists cannot search for what they do not know.

A form of AI called machine learning gets around this problem because it can find emergent patterns in mountains of information. Machine-learning algorithms parse data, learn from them and then classify information autonomously.

For the latest study, Simon Roux, a computational biologist at the DOE Joint Genome Institute (JGI) in Walnut Creek, California, trained computers to identify the genetic sequences of viruses from one unusual family, Inoviridae. These viruses live in bacteria and alter their host’s behaviour: for instance, they make the bacteria that cause cholera, Vibrio cholerae, more toxic. But Roux, who presented his work at the meeting in San Francisco, California, organized by the JGI, estimates that fewer than 100 species had been identified before his research began.