Project Description

Case Study: Queen’s University, Belfast

Gary Hardiman is a Professor, School of Biological Science at Queen’s University, Belfast. Gary was born in Galway, the Republic of Ireland, but spent most of his career in the U.S. where he was at UCSD, and then more recently the Medical University of South Carolina in Charleston. He moved back to Northern Ireland a little over a year ago, and is now affiliated with the School of Biological Sciences and the Institute for Global Food Security. He is a professor in the school and holds a chair in systems biology in the institute. His research centers around multi-omics data analysis across a variety of different topics: from cancer to climate change, to addiction.

Gary began using Advaita’s iPathwayGuide at the Medical University of South Carolina (MUSC). He knew of Advaita from array work that was done a while back and was impressed with the founder and the company. When he moved back to Ireland, he decided to bring iPathwayGuide with him. He says: “What I really like about Advaita compared to other tools that are out there is that Advaita is far superior to anything else I’ve seen. Your data gets uploaded, gets analyzed up on the cloud, and then you can actually share the results of that with anyone anywhere on the planet. And that’s very, very nice because it means you’re not hunched over one physical computer in a defined location or you’re not doing it with complex licensing so that other people can see the analysis. For me, that was key. Because even though I’m in Belfast, I’m back and forth to the U.S. very frequently. I still have a lot of grants back in the U.S. that I’m involved with, research going on over there that I’m involved with, so I need to be able to interact and share data with people. Advaita really facilitates this.”

Gary adds: “The other thing that I liked about Advaita and why I wanted to bring it with me, was I really like the visuals. I interact with and collaborate with a lot of people that aren’t bio-statisticians by background. And the way the data is presented to non-specialists is something that allows them to see what’s going on in a very intuitive way. Advaita tells them a story. It is a wonderful story-telling bioinformatics product.”

Gary says that his users come with expertise in a particular disease, maybe a particular type of cancer, but may not be familiar with the genomics bioinformatics tools. But they will see the data presented to them as pathways or lists of genes that are up and down-regulated. And it’s not an Excel sheet. It’s a set of graphics that are dynamic. He says: “This is a perfect way of interacting with people that are remote.” He goes on to add that he likes the meta-analysis capabilities where “users can run three or four different experiments and then do a meta-analysis on those and look for things that are similar or different.”

Gary says that his key issue was dealing with data analysis across the university, across different disease types, different datasets, but being able to present the data in a manner that physicians could understand. He believes that Advaita was exceptionally useful for that. He adds that the flexibility and the visuals of the software are great, the ability to share data is fantastic. He says: “An MD from the comfort of his own office could sit and point and click and look at his or her favorite genes and that was very, very nice. And then the meta-analysis capability is sort of one of the things I use quite a lot myself whenever I’m looking at data that’s of interest to me.”

He also says that perturbation analysis is vastly superior with iPathwayGuide. He adds that it’s nice to be able to take your data and see if a gene is up or down-regulated and if it is perturbed or what actually happens to all the things that are downstream of it in a biological pathway. He says: “In terms of accuracy of analysis, I suppose that that’s one of the tools that I like about Advaita and it’s probably one of the reasons that I have it here in the lab.”

Gary states that iPathwayGuide is affordable and cost-effective. He says that, while it is not free, it offers value that you can’t get with free software and that is essential to his desire for quality, flexibility, ease-of-use and data-sharing.

Gary goes on to say: “It is actually because people who do what I do often will have a list of genes from a differential [expression] experiment or RNA sequencing experiment and you’re trying to figure out, what’s the key thing going on here with the biology. And Advaita will very, very quickly get you into the crux of what’s going on. Now, often I look at a data set and the condition being studied might be something that I’m not an expert on. For instance, it might be a particular type of cancer. It could be any type of sort of biological study. But you bring someone that sort of knows that area fairly well. And I think that what you see is that they’ll see their favorite genes there and they’ll be like yes, this makes great sense. And then the other thing that I’ve seen is that they’ll see things in data that they wouldn’t have expected or would not have anticipated. Or, the other thing too that I’ve seen over the years is that some people come with sort of a hypothesis or predefined idea of what they’re expecting and it’s maybe not the most important thing in the data that they’re looking at. And I think Advaita nicely presents that to them. So, it does tell a story. And that is an essential capability.”

Finally, Gary says that he recommends Advaita for both core facilities and independent researchers. This is a product that is accurate, easy, flexible and is the best bioinformatics story-telling product available.

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