Clinical OMICS

NOV-DEC 2018

Healthcare magazine for research scientists, labs, pathologists, hospitals, cancer centers, physicians and biopharma companies providing news articles, expert interviews and videos about molecular diagnostics in precision medicine

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Page 27 of 51

26 Clinical OMICs November/December 2018 Christina Bennett Contributing Editor A stubborn, mountainous peak kept appearing in the lab's mass spectrometry analyses. "We had absolutely no idea what it was," recalled Stephen Barnes, Ph.D., director of the Targeted Metabolomics and Proteomics Laboratory at the University of Alabama at Birmingham. He suspected a contaminant. At the time in 2010, however, small molecule databases were quite thin for scientists running metab- olomics experiments. So his lab turned to America's favorite search engine, Google. After some trial and error, the lab entered a highly accurate mass of the unknown mol- ecule into the search field and got a hit, a journal article from Richard Caprioli's lab at Vanderbilt University. The identity of the mystery peak was now known: dimeth- yl-octadecyl ammonium chloride, a molecule frequently found in cleaning products. "I didn't go to databases because at that point it wasn't an obvious thing to do," Barnes said. Building a Treasure Trove Today, that Achilles heel for metabolomics researchers now longer exist, as data- bases that aid in the identification of compounds for metabolomics research have become larger and more widely used. The databases come in many flavors: pay- walled, freely accessible, downloadable, cloud-based, limited user input, full user input, and so on. One database that has grown to become one of the largest is METLIN, a spectral database from Scripps Researchers Grapple with the Utility of a Wide Variety of Metabolomics Databases Valleys AND The Peaks Pan Xunbin / Getty Images

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