We'll learn how HudsonAlpha leverages modern IT infrastructure and big-data analytics to power a pioneering research project incubator and genomic medicine innovator.
Listen to the podcast. Find it on iTunes. Get the mobile app. Read a full transcript or download a copy.
To describe new possibilities for exploiting cutting-edge IT infrastructure and big data analytics for potentially unprecedented healthcare benefits, we're joined by Dr. Liz Worthey, Director of Software Development and Informatics at the HudsonAlpha Institute for Biotechnology in Huntsville, Alabama. The discussion is moderated by BriefingsDirect's Dana Gardner, Principal Analyst at Interarbor Solutions.
Here are some excerpts:
Gardner: It seems to me that genomics research and IT have a lot in common. There's not much daylight between them -- two different types of technology, but highly interdependent. Have I got that right?
Worthey: Absolutely. It used to be that the IT infrastructure was fairly far away from the clinic or the research, but now they're so deeply intertwined that it necessitates many meetings a week between the leadership of both in order to make sure we get it right.
Gardner: And you have background in both.
Worthey: My background is primarily on the biology side, although I'm Director of Informatics and I've spent about 20 years working in the software-development and informatics side. I'm not IT Director, but I'm pretty IT savvy, because I've had to develop that skill set over the years. My undergraduate degree was in immunology, and since then, my focus has really been on genetics informatics and bioinformatics.
Gardner: Please describe what genetic informatics or genomic informatics is for our audience.
Worthey: Since 2003, when we received the first version of a human reference genome, there's been a large field involved in the task of extracting knowledge that can be used for society and health from genomic data.
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| Worthey |
It’s also information about which drugs they should and shouldn't take; information about which types of procedures, surveillance procedures, what colonoscopies they should have. And so, the clinical aspects of genomics are really developing the analytical capabilities to extract that data in real time so that we can use it to help an individual patient.
On top of that, there's also a lot of research. A lot of that is in large-scale studies across hundreds of thousands of individuals to look for signals that are more difficult to extract from a single genome. Genomics, clinical genomics, is all of that together.
Parallel trajectory
Gardner: Where is the societal change potential in terms of what we can do with this information and these technologies?
Worthey: Genomics has existed for maybe 20 years, but the vast majority of that was the first step. Over the last six years, we've taken maybe the second or third step in a journey that’s thousands of steps long.
We're right on the edge. We didn’t used to be able to do this, because we didn't have any data. We didn't have the capability to sequence a genome cheaply enough to sequence lots. We also didn't have the storage capabilities to store that data, even if we could produce it, and we certainly didn't have enough compute to do the analysis, infrastructure-wise. On top of that, we didn’t actually have the analytical know-how or capabilities either. All of that is really coalescing at the same time.
Gardner: Let's dive into that a little bit. What are the hurdles technologically for getting to where you want to be, and how do you customize that or need to customize that, for your particular requirements?
Worthey: There are a number of hurdles. Certainly, there are simpler hurdles that we have to get past, like storage, storage tied with compression. How do you compress that data to where you can store millions of genomes at a price that's affordable.
A bigger hurdle is the ability to query information at a lot of disparate sites. When we think about genomic medicine, one of the things that we really want do is share data between institutions that are geographically diverse. And the data that we want to share is millions of data points, each of which has hundreds or thousands of annotations or curations.
When
we think about genomic medicine, one of the things that we really want
do is share data between institutions that are geographically diverse.
Those are fairly complex queries, even when you're doing it in one site, but in order to really change the practice of medicine, we have to be able to do that regionally, nationally, and globally. So, the analytics questions there are large.
We have 3.2 billion data points for each individual. The data is quite broad, but it’s also pretty deep. One of the big problems is that we don’t have all the data that we need to do genomic medicine. There's going to be data mining -- generate the data, form a hypothesis, look at the data, see what you get, come back with a new hypothesis, and so on.
Finally, one of the problems that we have is that a lot of algorithms that you might use only exists in the brains of MDs, other clinical folks, or researchers. There is really a lot of human computer interaction work to be done, so that we can extract that knowledge.
There are lots of problems. Another big problem is that we really want to put this knowledge in the hands of the doctor while they have seven minutes to see the patient. So, it’s also delivery of answers at that point in time, and the ability to query the data by the person who is doing the analysis, which ideally will be an MD.
Cloud technology
Gardner: Interestingly, the emergence of cloud methods and technology over the past five or 10 years would address some of those issues about distributing the data effectively -- and also perhaps getting actionable intelligence to a physician in an actual critical-care environment. How important is cloud to this process and what sort of infrastructure would be optimal for the types of tasks that you have in mind?
Worthey: If you had asked me that question two years ago, on the genomic medicine side, I would have said that cloud isn't really part of the picture. It wasn't part of the picture for anything other than business reasons. There were a lot of questions around privacy and sharing of healthcare information, and hospitals didn’t like the idea.
They're very reluctant to move to the cloud. Over the last two years, that has started to change. Enough of them had to decide to do it, before everybody would view it as something that was permissible.
Cloud is absolutely necessary in many ways, because we have periods where lots of data that has to be computed and analytics has to be run. Then, we have periods where new information is coming off the sequencer. So, it’s that perfect crest and trough.
If you don't have the ability to deal with that sort of fluctuation, if you buy a certain amount of hardware and you only have it available in-house, your pipeline becomes impacted by the crests and then often sits idle for a long time.
It's kind of our poster child for many of the new technologies that are coming out that look at both of those, that allow you to run things in-house and then also allow you to run the same jobs on the same data in the cloud as well. So, it’s key.
Gardner: That brings me to the next question about this concept of genomics as a service or a platform to support genomics as a service. How do you envision that and how might that come about?
Worthey: When we think about the infrastructure to support that, it has to be something flexible and it has to be provided by organizations that are able to move rapidly, because the field is moving really quickly.
It has to be infrastructure that supports this hypothesis-driven research, and it has to be infrastructure that can deal with these huge datasets. Much of the data is ordered, organized, and well-structured, but because it's healthcare, a lot of the information that we use as part of the interpretation phase of genomic medicine is completely unstructured. There needs to be support for extraction of data from silos.
My dream is that the people who provide these technologies will also help us deal with some of these boundaries, the policy boundaries, to sharing data, because that’s what we need to do for this to become routine.
Data and policy
Gardner: We've seen some of that when it comes to other forms of data, perhaps in the financial sector. More and more, we're seeing tokenization, authentication, and encryption, where data can exist for a period of time with a certain policy attached to it, and then something will happen if the data is a result for that policy. Is that what you're referring to?
Worthey: Absolutely. It's really interesting to come to a meeting like HPE Discover because you get to see what everybody else is doing in different fields. Much of the things that people in my field have regarded as very difficult are actually not that hard at all; they happen all the time in other industries.
A lot of this -- the encryption, the encrypted data sharing, the ability to set those access controls in a particular way that only lasts for a certain amount of time for a particular set of users -- seems complex, but it happens all the time in other fields. A big part of this is talking to people who have a lot of experience in a regulated environment. It’s just not this regulated environment and learning the language that they use to talk to the people that set policy there and transferring that to our policy makers and ideally getting them together to talk to one another.
Gardner: Liz, you mentioned the interest layers in getting your requirements to the technology vendors, cloud providers, and network providers. Is that under way? Is that something that's yet to happen? Where is the synergy between the genomic research community and the technology-vendor platform provider community?
This
is happening fast. For genomics, there's been a shift in the volume of
genomic data that we can produce with some new sequencing technology
that's coming.
Worthey: This is happening fast. For genomics, there's been a shift in the volume of genomic data that we can produce with some new sequencing technology that's coming. If you're a provider of hardware or service user solutions to deal with big data, looking at genomics, as the people here are probably going to overtake many of those other industries in terms of the volume and complexity of the data that we have.
The reason that that's really interesting is because then you get invited to come and talk at forums, where there's lots of technology companies and you make them aware of the work that has to be done in the field of medicine, and in genomic research, and then you can start having those discussions.
A lot of the things that those companies are already doing, the use cases, are similar and maybe need some refinement, but a lot of that capability is already there.
Gardner: It's interesting that you’ve become sort of the “New York” of use cases. If you can make it there, you can make it anywhere. In other words, if we can solve this genomic data issue and use the cloud fruitfully to distribute and gather -- and then control and monitor the data as to where it should be under what circumstances -- we can do just about anything.
Correct me if I am wrong, though. We're using data in the genomic sense for population groups. We're winnowing those groups down into particular diseases. How farfetched is it to think about individuals having their own genomic database that would follow them like an authenticated human design? Is that completely out of the bounds? How far would that possibly be?
Technology is there
Worthey: I’ve had my genome sequenced, and it’s accessible. I could pick it up and look at it on the tools that I developed through my phone sitting here on the table. In terms of the ability to do that, a lot of that technology is already here.
The number of people that are being sequenced is increasing rapidly. We're already using genomics to make diagnosis in patients and to understand their drug interactions. So, we are here.
One of the things that we are talking about just now is, at what point in a person’s life should you sequence their genome. I and a number of other people in the field believe that that is earlier, rather than later, before they get sick. Then, we have that information to use when they get those first symptoms. You are not waiting until they're really ill before you do that.
I can’t imagine a future where that's not what's going to happen, and I don’t think that future is too far away. We're going to see it in our lifetimes, and our children are definitely going to see it in theirs.
The
data that we already have, clinical information, is really for that one
person, but your genome is shared among your family, even distant
relatives that you’ve never met.
Gardner: The inhibitors, though, would be more of an ethical nature, not a technological nature.
Worthey: And policy, and society; the society impact of this is huge.
The data that we already have, clinical information, is really for that one person, but your genome is shared among your family, even distant relatives that you’ve never met. So, when we think about this, there are many very hard ethical questions that we have to think about. There are lots of experts that are working on that, but we can’t let that get in the way of progress. We have to do it. We just have to make sure we do it right.
Gardner: To come back down a little bit toward the technology side of things, seeing as so much progress has been made and that there is the tight relationship between information technology and some of the fantastic things that can happen with the proper knowledge around genomic information, can you describe the infrastructure you have in place? What’s working? What do you use for big-data infrastructure, and cloud or hybrid cloud as well?
Worthey: I'm not on the IT side, but I can tell you about the other side and I can talk a little bit on the IT side as well. In terms of the technologies that we use to store all of that varying information, we're currently using Hadoop and Mongo DB. We finished our proof of concept with HPE, looking at their Vertica solution.
We have to work out what the next steps might be for our proof of concept. Certainly, we’re very interested in looking at the solutions that they have in here. They fit with our needs. The issue that’s been addressed on that side is lots of variants, complex queries, that you need to answer really fast.
In-house solution
We developed in-house solutions that we're using right now that allow humans to come in and look at that data and select the terms from it. So, you’d select disease terms. And then, we have in-house solutions to map them to the genomic side. We're looking at things like HPE’s IDOL as a proof-of-concept (POC) on that side. We're talking to some EHR companies about how to hook up the EHR to those solutions to our software to make it a seamless product and that would give us all that.
In terms of hardware, we do have HPE hardware in-house. I think we have 12 petabytes of their storage. We also have data direct network hardware, a general parallel file system solution. We even have things down to graphics processors for some of the analysis that we do. We’ve a large deck of such GPUs because in some cases it’s much faster for some other types of problems that we have to solve. So we are pretty IT-rich, a lot of heavy investment on the IT side.
Gardner: And cloud -- any preference to the topology that works for you architecturally for cloud, or is that still something you are toying with?
We
not only do the research and the clinical, but we also have a lab that
produces lots of data for other customers, a lab that produces genomic
data as a service.
Worthey: We're currently looking at three different solutions that are all cloud solutions. We not only do the research and the clinical, but we also have a lab that produces lots of data for other customers, a lab that produces genomic data as a service.
They have a challenge of getting that amount of data returned to customers in a timely fashion. So, there are solutions that we're looking at there. There are also, as we talked at the start, solutions to help us with that in-flow of the data coming off the sequencers and the compute -- and so we're looking at a number of different solutions that are cloud-based to solve some of those challenges.
Gardner: Before we close, we’ve talked about healthcare and population impacts, but I should think there's also a commercial aspect to this. That kind of information will lend itself to entrepreneurial activities, products and services, a great demand in the marketplace? Is that something you're involved with as well, and wouldn’t that help foot the bill for some of these many costly IT infrastructure investments?
Worthey: One of the ways that HudsonAlpha Institute was set up was just that model. We have a research, not-for-profit side, but we also have a number of affiliate companies that are for-profit, where intellectual property and ideas can go across to that site and be used to generate revenue that fund the research and keep us moving and be on the cutting-edge.
We do have a services lab that does genomic sequencing in analytics. You can order that from them. We also service a lot of people who have government contracts for this type of work. And then, we have an entity called Envision Genomics. For disclosure, I'm one of founders of that entity. It’s focused on empowering people to do genomic medicine and working with lots of different solution providers to get genomic medicine being done everywhere it’s applicable.
Listen to the podcast. Find it on iTunes. Get the mobile app. Read a full transcript or download a copy. Sponsor: Hewlett Packard Enterprise.
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