Deep sequencing is a way to battle sequencing artifacts, and it might seem to obviate time-consuming controls. But, given today’s large-scale studies, standards and controls “provide confidence that your measurement system is working the way you want it to, the way vendors say it should and the way people expect it to,” says Bob Setterquist, whose lab codeveloped External RNA Controls Consortium (ERCC) standards. When studying a low-frequency oncogene variant that might be clinically relevant, “you better be sure that’s not an error,” he says. Controls add an extra layer of data confidence and raise a red flag when they do not work properly. Until a researcher knows what’s amiss, data are not trustworthy. His lab uses controls when monitoring effects of procedural changes such as how a different reagent affects nucleic acid extraction yield in sequencing library prep. When labs set up a new instrument, results might deviate from what was, and controls help to assess the deviation. “That’s just good science,” he says. The spike-in mix is now used for different types of experiments such as RNA-sequencing (RNA-seq).