Scientists at UC Riverside have developed MassQL, a programming language that enables researchers to uncover hidden environmental pollutants with incredible speed and efficiency. This innovation holds immense potential to transform environmental and health research by making intricate data accessible to non-programmers.
Scientists at UC Riverside have developed a new programming language that promises to revolutionize the way researchers detect environmental pollutants. Named Mass Query Language (MassQL), this new tool allows biologists and chemists to uncover previously unknown chemical compounds quickly, shifting the needle in pollution detection and health research.
Mass spectrometry is a critical method in scientific research, functioning like a chemical fingerprint by identifying the presence and quantity of various molecules in samples such as air, water or blood.
Traditionally, analyzing these vast datasets required extensive programming skills, limiting accessibility. However, MassQL redefines this by enabling researchers to search through massive databases for specific patterns without needing to write a single line of code.
“We wanted to give chemists and biologists, who are generally not also computer scientists, the ability to mine their data exactly how they want to, without having to spend months or years learning to code,” Mingxun Wang, a UCR assistant professor of computer science, who created the language, said in a news release.
The efficacy of MassQL is evident.
Nina Zhao, a UCR postdoctoral student who is now at UC San Diego, utilized MassQL to sift through global mass spectrometry data on water samples. Her focus was on organophosphate esters, commonly found in flame retardants.
“There are quite literally a billion measurements of molecules in this data. You cannot go through it manually,” Wang added. “However, the language acts like a filter, in a sense, for these chemicals, and it pulled out thousands of them.”
Remarkably, Zhao’s research uncovered not only existing chemicals but also previously unclassified organophosphate compounds and some breakdown products. This finding could have profound implications.
“These chemicals can cause a lot of problems for human and animal health, and for entire ecosystems. They were designed to be flame retardants or plasticizers, but they can cause endocrine and sexual system disruptions, as well as cardiovascular problems,” Zhao added.
MassQL’s development involved over 70 scientists who helped fine-tune the language’s usability and accuracy, ensuring it met the diverse needs of both chemists and computer scientists. The resultant tool has the potential for a multitude of applications beyond environmental research.
Detailed in the journal Nature Methods, the paper outlines over 30 use cases for MassQL. These include detecting fatty acids as indicators of alcohol poisoning, searching for new antibiotics to combat resistance, studying bacterial communication and locating “forever chemicals” in playgrounds.
“I thought I could do something to save myself time,” Wang added. “I wanted to create one language that could handle multiple kinds of queries. And now we have. I’m excited to hear about the discoveries that could come from this.”