At UCL Genomics Core, Scientists Trust Their Data Analysis and Interpretation to Ingenuity

The University College London genomics core facility provides analytics for many types of experiments through Ingenuity® Pathway Analysis (IPA®) and Ingenuity® Variant Analysis™, web-based applications that allow users to rapidly analyze and interpret the biological significance of their genomics data.

Mike HubankMike Hubank, scientific director of UCL Genomics and senior lecturer at the Institute of Child Health, says that these tools utilize Ingenuity’s massive Knowledge Base of biological findings to create mechanistic hypotheses that can help direct scientists to their next relevant experiment. “The real benefit is the ability to connect things you wouldn’t otherwise have thought about,” Hubank says. “It’s a good tool for hypothesis generation, and there’s a lot to be said for that.”

The genomics core’s data primarily comes from next-generation sequencing instruments as well as gene expression and genotyping arrays. The data they generate serves as the foundation for genome-wide association studies, exome characterizations, and targeted sequencing projects that are focused on human health. “One of the things we really struggle with is analytical capacity, which is where Ingenuity has come in so handy,” Hubank says.

Hubank, a longtime user of Ingenuity IPA, tried out the newer Variant Analysis application in hopes that it would help his team perform faster analyses of all the data they were processing for users. “I was quite surprised when we trialed it,” he says, noting that it helped him to assess candidate lists of variants and identify those likely to be causative much faster than any other approach he has tried.

“Traditionally, once you’ve found a list of candidate variants, then you’ve got to sit there and look at the literature and try to figure out which you think are the most likely ones. That takes a long, long time even for an individual project,” Hubank says. “Variant Analysis cuts out a lot of the time. It was a no-brainer to adopt the tool,” he adds. With these Ingenuity tools, Hubank’s small staff now has the analytical power of a virtual army of bioinformaticians.

Like so many core labs, UCL Genomics historically relied on open-source algorithms, pipelines, and databases to fill its analysis needs. With so many of these free tools available, why would Hubank voluntarily choose a paid solution instead?

“What you think you’re getting free isn’t actually free,” says Hubank, whose team still uses open-source tools such as R, SIFT, and PolyPhen. So-called free tools still require people to develop the pipelines, maintain and update them, keep databases current, and more. “Someone has got to do that, and that someone has to be paid for,” he says.

Indeed, the cost of free tools goes beyond the individuals in each lab tasked with their proper maintenance. Hubank remembers using well-known, highly regarded public databases for various analyses — only to find out after the fact that the database hadn’t been kept current, and that the analysis would have to be thrown out because it didn’t take critical new information into account. “Often you’re using an out-of-date product without even knowing that it’s out of date,” Hubank says.

Thanks to many years of experience with IPA and his more recent use of Variant Analysis, Hubank has come to the conclusion that “you get what you pay for with analysis.” Now he doesn’t have to worry about working with obsolete data. The Ingenuity Knowledge Base, the deeply integrated database at the heart of all the Ingenuity analysis tools, is manually curated by a team of expert analysts to ensure that it incorporates the most recent information. “I’ve got confidence that it’s being maintained and kept up to date,” Hubank says. “It’s worth paying for that.”

Ingenuity’s web-based applications don’t replace the need for smart and dedicated scientists who are passionate about their research.  “It doesn’t do all the work for you,” Hubank says. “But it puts you in a better place to start attacking that problem.”

This blog is excerpted from a profile of Mike Hubank’s work at UCL Genomics. To read more, including how he sees real value in using IPA together with Variant Analysis, click here.