To answer the question, "Where's Waldo?" readers need to look for a number of distinguishing features. Several characters may be spotted with a striped scarf, striped hat, round-rimmed glasses, or a cane, but only Waldo will have all of these features. As described July 22, 2019, in Nature Microbiology, a team led by scientists at the U.S. Department of Energy (DOE) Joint Genome Institute (JGI), a DOE Office of Science User Facility, developed an algorithm that a computer could use to conduct a similar type of search in microbial and metagenomic databases. In this case, the machine "learned" to identify a certain type of bacterial viruses or phages called inoviruses, which are filamentous viruses with small, single-stranded DNA genomes and a unique chronic infection cycle. "We're not sure why we systematically manage to miss them; maybe it's due to the way we currently isolate and extract viruses," said the study's lead author Simon Roux, a JGI research scientist in the Environmental Genomics group.

How do you look for a needle in a haystack, when you are not sure what the needle looks like? This is the problem that faces scientists as they try to deal with increasingly complex datasets. One answer is to turn machine learning loose on the enormous volumes of data they have captured. The problem of finding relevant data in genetic databases is one that Simon Roux, a researcher working at the U.S. Department of Energy's Joint Genome Institute, faced when investigating the role that an obscure and little-understood family of viruses plays in the environment. There are many types of virus, called bacteriophages, that infect bacteria.