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GenoLogics CEO Discusses Pfizer, Illumina Deals, Challenges of Research Informatics


Michael Ball
GenoLogics this week announced two agreements for its data-management software: a three-year, global agreement with Pfizer; and a partnership with Illumina under which GenoLogics' Geneus system will be used in combination with Illumina’s Genome Analyzer.
While the company has recently taken steps to expand into the translational informatics software market [Bioinform 05-30-08], the two new agreements are a sign that the firm still has strong roots in the research informatics area.  
BioInform caught up with GenoLogics CEO Michael Ball who was attending this year’s American Society of Human Genetics meeting in Philadelphia. Ball has 20 years experience in sales, marketing, and business development with technology companies.
The following is an edited version of the conversation.

What will GenoLogics be doing for Pfizer?
With Pfizer, the first phase, which we announced, is primarily around research informatics, providing a data-management platform for all their biological research worldwide. It starts with two [sites] and the plan is to expand out to multiple sites worldwide over time and is in the discovery side as well as the development side. It’s a mix, but it’s all preclinical.
Imaginably [Pfizer] already had a [data-management] system in place, so what are you replacing to help them integrate a whole host of different data types?
The value they see, I guess, is [an ability to] move toward one software platform and one that is configurable so it works just as well in a lab doing gene expression as in a lab doing proteomics or imaging or cell biology. If you do have a platform that meets the very specific needs of all those different research groups yet [is] built on one standard database and data model, then you can achieve that integration of data. 
What if scientists in one location want to share a bunch of complex things, like different types of files with each other? How could that work?
That is part of the solution. … Different investigators can collaborate either across geographies or across different experiments they are doing, or scientific disciplines.
One of the key problems in multi-lab integration is everybody is using different ontologies. One of the advantages we have, is we’re basically pre-populating some standard ontologies that Pfizer has chosen into every system that we deploy in these labs.
Now they are starting to get consistency in how they aggregate data and capture data in the different labs.
The press release mentions the informatics solution will permit auditability. What does that mean?
Probably a better word would be traceability. When you get a meaningful result, you need to be able to go back and understand all the steps, the sample [itself], the annotations around that information that got you to a certain end point. So with a LIMS and a data management system, we give that traceability so you can really go back and understand all the relationships that got you to an end result.
In a separate announcement, you said that Illumina has chosen Geneus as its lab and data-management system [for the Genome Analyzer], so you are adding second-generation sequencing to your areas of research and translational informatics?
There’s a lot of hype around next-gen sequencing. The technology is advancing very quickly and one of the problems less talked about is how do you manage all the data that is being created. They came to us after we had been an Illumina Connect partner, of which they have many, for over a year, and said, ‘The big problem we are having is that we are generating all this data for our customers, [and] they need to pipeline it into analytics tools. Can you help us with that?’
So we worked very closely with them for the last six months to develop a much deeper integration into their sequencing system and really automate the whole workflow and pipelining component of data management. 
[Geneus] is not currently delivered with Illumina’s system, but we do integrate with GenomeStudio [Illumina’s new data analysis software suite for the Genome Analyzer, [BioInform 10-24-08], and we’ve done a lot of work with that. It’s very complementary to the software that is already built by Illumina.
The nature of the relationship is that they bring us into the relationship with the customers and then we work with them on the software side. All the integrations are built and all the hooks are there. Essentially, we can automate the entire workflow. 
You decided to spend six months on the integration work. That’s a considerable investment on your part, right?
We felt that this was an area where, from a data-management perspective, there probably is the most challenge in terms of our customers. When we talk to them, they can describe challenges in a lot of areas but one of the most sticky ones was around the data being created by [high-throughput] sequencing. We do much more than data management with sequencing, but we felt that as a starting point, that was a good problem to solve, maybe even the biggest problem to solve.
Once you do that you can get into the discussion, ‘Well now I have got my genomics sequencing data figured out, what about my expression, my genotyping, proteomics, and imaging [data]?’ Then there is a natural lead-in into how we would integrate all those different pieces. So we see this as part of an overall data integration strategy.
So who is the typical user you might be able to help? Does it have to be a large sequencing center, [or] might it be a smaller lab?
Absolutely. According to Illumina, a majority of their systems are going to smaller labs, not the big genome centers, and that is where the [data-management] problem is more acute, because they generally don’t have the informatics expertise to address the problems, unlike the massive informatics teams who can grapple with these problems [at genome centers]. The smaller core labs and research groups don’t have that capability.
What can researchers see when they implement Geneus in the second-generation sequencing pipeline?
There is a visualization part in the sense that you can see graphically everything from when you start with a sample, information about that sample, what workflow it has gone through, what reagents have been added, when it was sent through the sequencer, [and] afterward, what steps you have taken in terms of primary analysis. So you can see all that in our system and track the status of samples and the results associated with those samples.
In what way are these two announcements indicative of where GenoLogics is going right now?
Both are centered around research informatics, mostly around managing research data. The other shift is that we have announced a suite of products in the biomedical software area, which is around managing samples in biorepositories and clinical annotation.
The challenge there is you have groups trying to do translational research and every one of them is grappling with, ‘How do I get good clinical information with my sample before I send them to the research groups?’
Before it goes into a genome sequencing center and we start getting results, I need to know all the context around that sample, so we have software now that allows them to aggregate clinical annotations, track those samples, and then feed them into those research labs, and then aggregate all those results at the end.

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