Transcript of a discussion on how HudsonAlpha leverages modern IT infrastructure and big data analytics to power research projects as well as pioneering genomic medicine findings.

1. How HudsonAlpha Innovates on IT for Research-Driven
Education, Genomic Medicine and Entrepreneurship
Transcript of a discussion on how HudsonAlpha leverages modern IT infrastructure and big data
analytics to power research projects as well as pioneering genomic medicine findings.
Listen to the podcast. Find it on iTunes. Get the mobile app. Sponsor: Hewlett
Packard Enterprise.
Dana Gardner: Hello, and welcome to the next edition to the Hewlett Packard Enterprise
(HPE) Voice of the Customer podcast series. I’m Dana Gardner, Principal Analyst at Interarbor
Solutions, your host and moderator for this ongoing discussion on IT Innovation -- and how it's
making an impact on people's lives.
Our next IT infrastructure thought leadership case study explores how the
HudsonAlpha Institute for Biotechnology engages in digital transformation for
genomic research and healthcare paybacks.
We'll now hear how HudsonAlpha leverages modern IT infrastructure and big-
data analytics to power a pioneering research project incubator and genomic
medicine innovator.
Here to describe new possibilities for exploiting cutting-edge IT infrastructure and big data
analytics for healthcare innovation, we're joined by Dr. Liz Worthey, Director of Software
Development and Informatics at the HudsonAlpha Institute for Biotechnology in Huntsville,
Alabama. Welcome, Liz.
Dr. Liz Worthey: Thanks for inviting me.
Gardner: It seems to me that genomics research and IT have a lot in common. There's not a lot
of daylight between them -- two different types of technology, but highly interdependent. Have I
got that right?
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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. Maybe you can tell us a little bit about that.
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
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Gardner

2. IT Director, but I'm pretty IT savvy, because I've had to develop that skill set over the years. My
undergraduate 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.
A [human] genome is 3.2 billion nucleotides in length, and in there, there's a lot
of really useful information. There's information about which diseases that
individual may be more likely to get and which diseases they will get.
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: It seems that there's been a parallel trajectory between what the IT data technologies
have been capable of and what benefits we can apply back to society. Tell me about where we are
in that process. Are these early days? Are we halfway through the journey? Where is the 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.
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Worthey

3. As we are doing genomics, and that technology and the sequencing side has come up, the
compute and the computing technologies have come up at the time. They're feeding each other,
and genomics is now driving IT to think about things in a very different way.
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 for 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.
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 5 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
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When we think about genomic
medicine, one of the things that
we really want do is share data
between institutions that are
geographically diverse.

4. 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.
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But it’s also important to have stuff in house, because sometimes, you want to do things in a
different way. Sometimes, you want to do things in a more secure manner.
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.
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5. 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 underway? Is that something that's yet to
happen? Where is the synergy between the genomic research community and the technology-
vendor platform provider community?
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.
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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.

6. 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 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.
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
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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.

7. 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.
On the other side, one of the technological hurdles that we have to meet is the unstructured data.
We have electronic health record (EHR) information that’s coming in. We want to hook up to
those EHRs and we want to use systems to process that data to make it organized, so that we can
use it for the interpretation part.
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 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
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?
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
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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.

8. returned to customers in a timely fashion. So, there are solutions that we're looking at there.
There are also, as we talked in the start, solutions to help us with that in-flow of the data coming
off the sequencers and the compute, and we're looking at a number of different solutions that are
cloud-based to solve some of those challenges.
Gardner: Before we close out, 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 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 can 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.
Gardner: Well, it's been a fascinating discussion. Thank you for sharing that, and I look forward
to tracking the close relationship between IT and genomics, because as you say, they are self-
supporting, reinforcing, and both very powerful in their impacts in society. Thank you very
much.
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You've been learning how the HudsonAlpha Institute for Biotechnology engages in digital
transformation for genomic research and for healthcare paybacks. We’ve heard how
HudsonAlpha leverages modern IT infrastructure and big data analytics to power research
projects as well as medicine pioneering findings.
So please join me in thanking our guest, Dr. Liz Worthey, Director of Software Development and
Informatics at the HudsonAlpha Institute for Biotechnology in Huntsville, Alabama. Thank you
so much, Liz.
Worthey: Thank you very much. Thanks for inviting me.
Gardner: And I will also thank our audience as well for joining us for this Hewlett-Packard
Enterprise Voice of the Customer Podcast.
I'm Dana Gardner, Principal Analyst at Interarbor Solutions, your host for this ongoing series of
HPE-sponsored discussions. Thanks again for listening, and do come back next time.
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9. Listen to the podcast. Find it on iTunes. Get the mobile app. Sponsor: Hewlett
Packard Enterprise.
Transcript of a discussion on how HudsonAlpha leverages modern IT infrastructure and big data
analytics to power research projects as well as pioneering genomic medicine findings. Copyright
Interarbor Solutions, LLC, 2005-2016. All rights reserved.
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