Researchers at the University of California Santa Cruz Genomics Institute are in a race against time. Every day, children and adults suffering from cancer desperately wait for potentially lifesaving treatments. Many of these treatments are guided by genomic analysis, as conducted at the Genomics Institute.

For example, the Institute’s Treehouse Childhood Cancer Initiative analyzes pediatric cancer genomic data and provides analysis to clinical partners who treat young patients desperate for help. “We take molecular pathways indicated by our analysis and try to match this information with clinical trials and new drugs that could help patients,” says Isabel Bjork, director of pediatric cancer precision medicine at the Treehouse Childhood Cancer Initiative. “Because these patients are out of options, it’s critical that we react quickly.”

But reacting quickly was challenging for the Treehouse Initiative, and for the UC Santa Cruz Genomics Institute as a whole. That’s because the Institute’s Computational Genomics Lab needed better, faster technology for conducting analysis of RNA sequencing (RNA-seq) data to show the presence of particular RNA molecules in biological samples. RNA—ribonucleic acid—is one of the three major molecules essential for all known forms of life. “We wanted to analyze individual cancer samples in as much detail as possible, which involved going back and studying previously available data,” says Benedict Paten, director of the Computational Genomics Lab at the UC Santa Cruz Genomics Institute. “One of the problems was that much of the previous data had been computed by different institutions using different genomic-processing pipelines. We really wanted to look at the entire data set as a whole, but that required a level of processing capacity and performance we didn’t have.”

Although the Institute considered using its own big-data processing solution to tackle the problem, it soon realized the limitations of that approach. “It would have taken at least three months to process all the data with our in-house solution, and that’s just not fast enough,” Paten says. For instance, the Treehouse Initiative specifically needs to quickly analyze cancer data in its reference compendium. “The compendium is constantly growing, and we’re always trying to find ways to gather data faster,” Bjork says. “However, genomic sequencing and data transfer have always been the holdup. We often just have a few days to get analysis to a clinical partner."