Citizen science, the movement to draft non-specialists into areas of scientific research, doesn't require the volunteers to put on lab coats. In at least one case, scientists turned a prickly biochemical problem into a game and found that the gamers could typically beat the best computer algorithms out there.

But all that work was done on cases where we already knew the answers, which was how we were able to measure the gamers' success. Now some researchers have taken this approach one step further and created a hybrid project that mixes volunteers with lab-coated workers. 37,000 enthusiasts were given the chance to take on algorithms in designing new RNA molecules. And once the gamers had a chance to vote on the best designs, the winning designs were sent to a lab, synthesized, and tested. After a few rounds of this, players were not only handily beating the computers but providing rules that went into designing the next-generation algorithm.

A consortium of researchers at Carnegie Mellon, Stanford, and Seoul National University put together what they called a Massive Open Laboratory. Operated through a Web portal called "eterna," it provides a few tutorials that allow people to bring themselves up to speed on the base pairing rules that govern the structure of RNA molecules. These structures can fold up a linear RNA molecule into a catalytic form or allow it to bind other molecules and proteins. These structures are essential to basic cell functions, such as turning genes into mature messenger RNAs and then converting these messengers into proteins.

Once the tutorial is done, volunteers can start taking part in challenges like the one shown on top. By setting the composition of an RNA's bases, they can attempt to get it to fold into a structure provided by the researchers. You can then perform further tweaks to make the structure more robust. Through this process, the players get to learn the basic rules of what makes an RNA structure energetically stable.

At least, energetically stable based on calculations. But the Massive Open Lab also had a lab, and after a round of challenges ended, those in lab coats actually synthesized and tested the top molecules. The results were given as feedback to the players, and a new round of challenges began.

In the first round, the current state-of-the-art software did a better job of designing RNA structures than the players did. That didn't last, though. "As the community gained experience with empirical RNA design cycles," the authors write, "performance improved, and community submissions converged to successful designs." By the third challenge, the best player-contributed designs were outperforming the best algorithms; by the sixth round, the median player design was better than the best that the computers could come up with.

The system also allowed the users to create simple rules to help them design effective structures (things like "put a G-C pair at the base of a stem"). The authors checked these and found that many of the design principles hadn't ever been identified in the scientific literature. So they designed an algorithm that could incorporate the user-designed rules. That algorithm also beat the previous state-of-the-art and often approached the best of the human designs.

From here, the researchers behind the Massive Open Lab plan on taking the work in two directions. They intend to try to figure out why, at the biochemical level, the players' rules result in a more stable RNA structure. And, at the same time, they want to set their players onto designing more complicated structures—ones that could bind a specific molecule or perform a catalytic reaction.

PNAS, 2014. DOI: 10.1073/pnas.1313039111 (About DOIs).