Michael Edwards, a bioinformatics expert and assistant professor at the University of Colorado, Denver, is always looking for the big picture. A biologist by training, he gravitated toward computational biology and bioinformatics as he encountered technologies that generated more data than anyone knew how to handle.
Edwards was in graduate school when microarrays first came out. During his PhD studies at the University of Wisconsin, he remembers a paper published by the lab — one of the first major articles using this new tool for the study of longevity effects of caloric restriction — that included no statistics beyond fold changes. With gene chips, he saw immediately, “you’re looking at a lot of information, and how do you make sense of it?”
Today, his expertise in handling enormous data sets has made him the go-to collaborator for teams that generate long gene lists with no clear path forward. When this happens, Edwards has his own go-to expert: Ingenuity Pathway Analysis (IPA) from QIAGEN. “We’re able to measure a lot of things, so the challenge is figuring out how to use all of this information to get to the big picture,” he says. “That’s what IPA allows me to do: bring the biology into the data.”
In a recent project, Edwards worked with scientists who had gathered a great deal of information from sequencing bladder tumors. They had emerged with a list of some 425 mutated genes, representing the most extensive list of mutations for this type of cancer, but it was unclear how to proceed. How did all these genes fit together? What was the common theme? The sheer number of genes made the idea of interpretation quite daunting.
“They came to me with their gene list, and I put it into IPA, which started building pathways and likely signaling avenues,” Edwards says. “The genes started to group based on biological functions — chromosome structure or cell cycle maintenance, for example — and they would build these networks within themselves.” He and his collaborators went back to look at the tumors and found that key components of cell signaling would have to be mutated in order to produce a bladder tumor. “They were amazed,” he says. “I get that response quite a bit. People don’t really know that there’s software out there that can do this.” Without IPA, he adds, this project would have ended with publication of the list of 425 genes and no information about relationships between genes or how they function to create tumors.
As an IPA power user, there are several features that Edwards finds particularly useful for his research, including Causal Network Analysis and multi-hop to help reveal upstream and indirect regulators. “What a lot of us in bioinformatics are finding is that some of the truly important events in cellular signaling are probably invisible in the transcriptome,” he says. Tracking down these master regulators based on transcriptome data is possible with IPA and was “a huge step” in accelerating Edwards’ work. “These tools are very good at getting at the skeleton that connects all of the gene expression data,” he adds.
IPA is more than a convenient way to query PubMed, Edwards says. “You’re searching the Ingenuity Knowledge Base, which is immense and has lots of relationships that you might never find with just a regular literature search.”
Want to find out more? Read the full case study of Mike Edwards and how IPA is accelerating his research efforts.