Hunting for a particular genetic mutation in hundreds of thousands of specimens can be an expensive and time-consuming process. In the past several years, faster multiplex DNA sequencing machines have sped up the acquisition of data, but researchers have still been hobbled by having to label each sample with a unique molecular identifier (or bar code) for analysis.

Scientists

are proposing a new take on a very old idea to tackle large data sets simultaneously. The team is applying the Chinese remainder theorem to pinpoint single samples in larger pools, which are arranged in rows and columns.