Michael Pazin: I’m going to give you a really brief tour
of ENCODE.
I’d like to start off by letting you know our objectives for this.
We’d like to tell you about something about ENCODE and Roadmap epigenomemics so that you
can learn how to use it in your work.
We’d also like to hear from everybody, what are ways we can make different projects more
useful, either at the interface level or in the way we’re actually doing the stuff.
I’d also like to thank my colleagues in ENCODE.
You can see an ENCODE consortium photo on the bottom and also two of my program colleagues,
Elise Feingold has been with ENCODE since its inception.
So, ENCODE is the Encyclopedia of DNA Elements and the goal of project is to identify all
of the functional elements of the genome.
This is of course an aspirational goal: There’re no way to actually know whether this is complete
or how complete it is.
And we’re also trying -- want to make this resource freely available to the community.
This is a major portion -- a major idea behind the project and we’re hoping that people
can use it in studies of the disease, in studies of gene regulation.
ENCODE is built on decades of research in gene regulation.
What we have done is to take assays that have been used for many, many years to study gene
irregulation and look at the mechanism of gene regulation and either use other people
version of genome wide assays or develop our own, to apply these assays look at the whole
genome.
Since these assays are mechanistically linked to gene regulation, we and other projects
are using them to reverse engineer the function of the genome.
We’re trying to determine whether our genes, the transcripts that come from those genes,
and also the regulatory elements that can control the action of those genes.
So, one important goal for NHGRI, an important goal for ENCODE is done to understand the
whole genome.
Today, I’m going to focus on the noncoding portions of the genome because we think it
is relatively understudied.
And bulk studies of the genome, for instance, GWAS studies, find that most of the associations
are in noncoding regions of the genome.
Also, if you look at individual genes studies, you can find gene like Fragile X, an [unintelligible]
disorder, where the known variant is a noncoding variant that counts accounts for almost all
of the heritability.
And even polygenic diseases like ALS or the variant that accounts for the largest known
chunk of heritability is a noncoding variant.
So, the idea of how ENCODE might help people is if we do look for variants, the way we
understand them is to put them in context.
We associate them with things like maps or drawings, and ENCODE is trying to make those
maps richer.
So here is a cartoon on the slide showing a variant that’s been associated with allergy
and asthma.
And you can see the arrow pointing to the SNP and showing where it is in its genome
context.
That’s great start but what we can do with ENCODE is we can add a different biochemical
assays genome wide and from this, we can see that it appears that this variant lies within
what could be a regulatory region.
So that’s one additional piece of information that you can get.
It turns out that this regulatory region is especially prominent in a particular cell
type.
So that’s another piece of information that you could get from ENCODE.
This regulatory -- this candidate regulatory element lies within a gene but what you can
learn from ENCODE is you can make a prediction of what gene it might work with.
And what’s predicted is that it works with these neighboring genes.
Now, I’m showing you this example because this locus has been extensively studied in
the mouse where it’s well known that, in fact, this is a locus control region that
regulates those neighboring genes.
But you could make this prediction from ENCODE human data.
That’s how that works.
So, you probably heard me say a lot about prediction and ENCODE can tell you that you
might be able to figure out things.
Primary use for ENCODE is hypothesis generation, I would say.
People are using ENCODE to go from associated variants to what might be causal variants.
They’re using ENCODE to say, from associated variants, what might be the target cell type
that the variant works in because many diseases affect multiple cell types.
You might ask from a target variant, what are the regulatory regions?
What are the upstream regulatory factors that are working to control that gene?
And you might also use ENCODE to make predictions about how that candidate regulatory element
is working.
And I would -- and at the bottom of the slide, the focus of a lot of what you will here today
is that using germline genetics studies but I would point out that if you have an epigenomic
cohort, ENCODE should work well with that.
If you work on epigenomes then you know that there are additional caveats that one would
need to apply and also, this works quite well with sematic variants, again there are additional
caveats that one would have to apply.
So, we think ENCODE is quite useful.
We see it being used in a lot of different ways and we maintain on our portal, a list
of publications that have used ENCODE data and ENCODE has been in a number of talks from
the sessions that this ASHG meeting as well.
It’s been in over 1,000 papers from the community.
That means people that are reusing ENCODE data that don’t have ENCODE funding.
And a large fraction of these, about one third of them are inhuman disease.
So I think attests to the translational value of the resource.
So ENCODE has released thousands of data sets and they’re shared freely.
They are available through the ENCODE portal, ENCODEproject.org.
There’s no login or controlled access required.
They are available to everybody.
We’re also releasing software that we’ve developed and sharing software that our colleagues
outside of ENCODE have developed but we’ve found to be useful.
We’re working really hard to increase data interoperability.
We’re working, for instance, with Roadmap Epigenetics and other IF [spelled phonetically]
projects to try and standardize our interfaces and data.
And I’d like to say a little bit about IHEC, the International Human Epigenome Contortionism.
ENCODE is one of the members of IHEC.
You’ll also hear from Roadmap next, which is an IHEC member.
And IHEC has its own data portal.
Again, these slides are shared, so you don’t need to write down the URLs now but I’d
encourage you to go see their portal and see the data from other projects, such as the
Canadian projects, Blueprint, and DEEP and so forth.
And lastly, again, I don’t expect anybody to be writing this down.
We’re sharing the slides.
But there are a lot of different ways to access ENCODE.
There are a number of useful URLs here.
Again, the portal is ENCODEproject.org.
We have tutorials on the ENCODE portal.
We also have tutorials available at the NHGRI website, genome.gov.
There are links to get to IHEC resources as well.
And you can also join the ENCODE mailing list, if you wish, to hear the latest updates.
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