Mike Pazin: You’re going to hear three different parts
of presentation.
And first, what you’re going to be hearing about -- yes.
So, first, what you’re going to hear about from me, on behalf of ENCODE team, the rest
of the colleagues, is a little bit of information about the ENCODE background.
I’ll tell you something about the approaches, the goals, and the achievements of ENCODE.
And then, I’ll take brief questions there.
And after that, I’ll move on to tell you about the Functional Genomics Workshop that
took place in March.
You already heard that mentioned in Eric Green’s director report.
And then, Elise Feingold will take over and do the heavy lifting and tell you about the
functional genomics concepts that are coming up for council.
So, first, I’d like to tell you a little bit about why we think this is important.
Functional genomics is, of course, central to NHGRI goals.
In part, this is because non-coding DNA is very important for disease and also for gene
regulation.
So, the vast majority of common disease findings and the vast majority of common disease heritability
has been reported to lie outside of protein coding regions.
Noncoding DNA variants are known to cause human diseases and alter human traits.
For instance, if we look at Fragile X syndrome, that’s a Mendelian disorder where, essentially,
all of the heritability is found -- is a noncoding mutation at a single gene.
Or if we look at ALS, a neurological disorder, the vast majority -- the heritability associated
with the largest variant is a noncoding variant -- again, about 30 percent of the heritability
with one variant.
So, in order to understand this, we don’t have a genetic regulatory code like we have
for the genetic code.
So, we need functional genomics experiments to better understand the noncoding portion
of the genome.
So, ENCODE is such a project, run by NHGRI.
ENCODE is the Encyclopedia of DNA Elements.
And the twin goals of ENCODE, as you’ve heard, are to identify all of the candidate
functional elements in the genome -- that’s, of course, an aspirational goal -- and then,
also, to share this catalog as a freely available resource.
This is for anybody who has internet.
No login, no fee for purchase; taxpayers have already paid for this.
And this is very useful for the study of genetic basis of human disease.
We also think that this is useful for people studying gene regulation.
So, quickly, the timeline of ENCODE -- the project began in approximately 2003, starting
off looking at one percent of the human genome.
It’s funny, looking back now and thinking how bold and audacious that was, to look at
one percent of the genome.
By 2007, the project was looking at essentially the entire human genome.
And the -- and the sister project, modENCODE, was looking at the worm and fly genomes.
By 2009, there was a mouse project.
And the current round of ENCODE, which started in 2012, looks at the human and mouse genomes.
So, ENCODE does this by having been built upon decades of research into gene regulation.
All right?
So, many mechanistic studies over the years have learned biochemical signatures from events
that appear to be causal for gene regulation.
What ENCODE is doing, and other projects and investigators as well, is to reverse-engineer;
to say, “If we use these signatures, can we predict where genes may be transcripts
from the genes and regulatory regions, such as promoters, enhancers, and insulators?”
So, I’d like to tell you some of the accomplishments of ENCODE.
We’ve released thousands of data sets.
And these are released pre-publication by the consortium.
They’re high-quality data containing replicates.
And the data are uniformly processed -- the consortium data.
We share software.
We do this through the ENCODE portal and also through standard resources, such as GitHub.
We’re working with partners at other projects to increase data interoperability.
We’re doing this with, for instance, Common Fund Roadmap Epigenomics and the International
Human Epigenome Consortium.
And what we’re trying to do is standardize the ontologies that are being used across
projects, the metadata that’s being used, and even the data standards, to make all of
this data more useful to people outside of these projects.
We’ve also developed an informed consent to allow unrestricted access -- sharing of
genomic data from participants.
The main thing I’d like to emphasize to you as an accomplishment of ENCODE are the
publications.
As you heard from our director early -- earlier, there are hundreds of publications from the
consortium.
But I’d like to echo his words: what’s most important is the data are being widely
used by the community.
We’ve tracked at least a thousand publications from the community using ENCODE data.
And I’d emphasize these are not citations of ENCODE; that’s a far larger number.
These are publications that actually have ENCODE data in them.
And they span a wide range of topics: human disease, basic biology, and methods development
and software.
Fully one-third of these publications are in human disease, emphasizing, I think, the
translational value of the project.
If we look at the human disease publications, they span, as you might expect or hope, a
wide range of diseases that affect the human condition, diseases [spelled phonetically]
that are funded by a range of institutes across the NIH, including at NHGRI itself.
So, does anybody have any questions here about ENCODE background, before I move on to the
workshop report?
Okay.
So, the workshop took place on March 10th and 11th of this year.
We had a few thousand hits from different viewers as the workshop took place.
I’d like to begin by acknowledging, first, people that did important work on this; our
workshop organizing committee, first and foremost.
Eric Boerwinkle, Carol Bult, John Lis, and Aviv Regev did a fantastic job.
I’d also like to thank my colleagues at NHGRI for working on this.
The participants in this workshop spanned broad ranges of science, as I think you would
like to see.
We have people that develop technology and work on particular diseases.
We have people that work in standalone labs and people that are members of lots of different
consortia, including CHARGE, GTEx, and IHEC.
We had NHGRI council members and ENCODE external consultant panel members.
We also had people that were funded from NHGRI, other NIH institutes, NIH intramural program,
as well as a number of international funding agencies.
So, we think we had broad representation.
And there were two main objectives in this workshop.
First one is scientific, to discuss opportunities for understanding genome function using large-scale
genomic studies.
And the second, which we spent less time on, was an implementation topic: how could NHGRI
structure topics to address these opportunities?
So, the workshop was videocast live.
We had a few thousand viewers as the workshop was taking place.
The materials are archived, so the agenda is available, the slides, and the videos,
at the NHGRI website.
And the workshop report will be coming soon.
The workshop report that council members have today is a draft.
It’s pretty close to final, but the final report should be out soon.
So, I’d like to begin with the ending, the recommendations from the workshop.
We heard that there was a strong need to continue genome-wide identification of functional elements.
We heard that it was important to add in functional characterization -- functional characterization
efforts for these elements.
We heard that it was important to begin to apply functional genomics assays directly
to disease studies, something that had intentionally been out of scope of ENCODE in previous rounds.
We also heard the need for increased community participation, primarily at the level of community-supplied
samples for the consortium; also for the community to -- also for the consortium to take in community
data.
So, the workshop was structured around four basic types of topics.
There was background information.
We heard about mapping functional elements.
We heard about disease studies.
And we also heard about basic biological questions.
For each of these, we heard some scientific presentations from members of the community
followed by discussions.
And if you look at the background presentation, some of the key messages that we heard are:
ENCODE is very useful for studies of disease and biology, as evidenced by hundreds of publications
from the community using ENCODE data.
We heard that systematic mapping of data and diverse cell types continues to be important.
We heard this is important beyond simply aggregating opportunity samples.
We heard that it’s important to profile samples that either are involved in disease
or in the disease state.
We heard that it’d be important to have better information about functional connections
between regulatory elements and genes.
Increasingly, people doing disease studies are becoming aware that a variant doesn’t
necessarily lie in the gene it’s linked to; it may be one, two, three, four, five
genes away.
And finally, we heard about the importance of a systems biology approach to integrate
this knowledge.
From there, we moved on to the mapping topic.
And we heard again about the importance of unbiased mapping of elements.
We heard again about the importance of 3D interaction maps.
This is one technique that can be used to make predictions of which regulatory elements
may be acting with -- on which genes.
We heard about the importance of single-cell and small-cell-number assays for a variety
of types of work.
There was a plea for getting beyond mapping and understanding what elements do.
This would help us to understand their causality and understand -- and better understand whether
they may or may not be participating in human traits and human diseases.
Again, a plea for better community participation.
And we heard a lot of interest in maintaining high data standards, maintaining efforts for
interoperability, and data visualization tools being very important.
So, we moved on to disease.
We’ve heard a lot of interest in getting a better understanding of the noncoding portion
of the genome.
A lot is known and a lot is being done about the protein coding portion of the genome already.
We heard that it’s important to both map and to functionally characterize genomic elements.
And then, we heard diverging views, two very different ideas on how best to do this.
There was a lot of interest in prioritizing elements to characterize -- or in samples
to characterize based on their known role in disease.
There are also a group that strongly advocated we should do this by looking at samples and
variants spread widely across biology.
And the idea was brought up that -- for instance, think about the lesson of what we learned
from GWAS.
If we had just looked for variants in the places we thought to look, we wouldn’t have
found much of what it was that we found.
There is call for better predictions of the effective genetic variation on gene expression.
Typically, today, one finds a variant; it’s not straightforward to say, what might this
be doing in the expression of some nearby gene?
Again, we heard about systems biology approaches, including a nice vignette about how systems
biology would have made a line of research much simpler, had those findings been in place
at the time the work was done.
And we heard a plea for better sharing between the clinical and the research enterprises.
And we heard that this is primarily a matter of policy rather than technology, but it’s
holding back research in the area.
Finally, we heard about specific biological problems.
We heard about the value of systematic perturbations in order to learn about the connections between
genes and also genes to regulatory elements.
We heard that it’s very important to maintain high data standards and that developing new
data standards and pipelines for new assays would be an important thing to bring to the
community.
Some said that one of the most valuable things that ENCODE has done is develop standards
and processing methods for what are now pretty standard data types.
We heard again about it being important to characterize elements that had been mapped
by ENCODE, and that this would help us to better understand the ENCODE catalog that
we already have.
We heard again about the importance of bringing in community participation at the level of
bringing in samples and existing data.
We heard that what would -- might be useful for some clinicians is having a gene-centered
view, being able to say, “I think this gene is important in this disease or for this patient.
What can I find out about ENCODE from this?”
And finally, we heard divergent views on the types of data that different users would need.
We heard that some users find it very important for their work to have a large number of cell
types that are profiled, even if it’s only by one or two assays.
Other users find it very important to have a large number of assays that are done, even
if it’s only in one or two cell types.
And there are also users that require a variety of assays across a variety of cell types.
So, we’re trying to simultaneously support these different user communities.
So, in summary, where this left us as -- the highest priority is to expand beyond cataloguing,
to understanding function of genomic elements.
We think we could accomplish this by continued mapping efforts, a new functional characterization
effort, increased community participation, and adding in direct disease studies.
And where this would help NHGRI and the community is that this would enhance genetic studies
of human disease.
So, I’ll stop there with the workshop and the background.
Any questions before I hand this off to Elise?
Thanks.
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