Jill Moore: All right, thank you for coming out this morning.
My name is Jill Moore.
I’m a graduate student in Zhiping Weng’s lab at UMass Medical School and today I’m
going to be talking about two different topics.
The first is the encyclopedia that the ENCODE consortium is working, and the second is varying
annotation using two different tools from ENCODE consortium groups.
So the first question is where is this encyclopedia?
So as Mike Pazin already talked about, ENCODE stands for Encyclopedia of DNA elements, and
so far ENCODE data producers have generated thousands of experiments in humans including
DNase-seq, transcription factor ChIP-seq, histone mark ChIP-seq, and a wide variety
of other experiments.
But the question remains is how do we actually integrate these experiments and assays together,
and how do we integrate data from other groups such as Roadmap so we can actually annotate
these functional regions in the genome?
And then how do we build and visualize this encyclopedia from these functional regions?
So this is a list of different genomic annotations that you can look at, and it certainly isn’t
the entire list.
There’s some that are quite on the simple side in the terms of they only require one
type of experiment or one type of assay to look at, such as gene expression or the process
peaks from DNase-seq or ChIP-seq, but then you have more complicate annotations that
require you to merge and combine different data sets.
So for example if you want to annotate regions in the genome like ChromHMM does, which we’ll
talk about later today, or perhaps predict target regions of regulatory elements.
And what we’re going to be talking about today is actually annotating candidate enhancers
and promoters.
So right now we’ve been working on this and this is our current pipeline for doing
so, and this work is all done by Michael Purcaro in our lab.
And our first step was to actually define what we call DNase master peaks.
So these peaks are a set of unique, non-overlapping peaks and they’re representative of DNase
peaks that you see in a region.
And so these peaks are going to span all data sets and they collectively cover about 20
percent of the genome.
So we incorporate both ENCODE and Roadmap data together to generate these master peaks.
And so this is done by John Stam’s lab and this is just a quick visual of how this is
done.
So you have these different cell types here as well as tissue types from Roadmap and you
have all of these called DNase peaks.
So you actually merge them so if you look visually so that they’re all on top of each
other, and this is your linear genome, and we are going to pick out a representative
peak for this entire cluster.
So this case we would use the peak with the highest signal which in this case is this
peak that’s labeled 48.
So by doing this we are able to get a representative peak for all of these DNase clusters of peaks
across cell and tissue types.
We next -- after we have this list of master peaks, we separate them into two groups.
We have the TSS-proximal group.
So these are all the master peaks that are within a two kilobase window of a TSS, and
then we have all the other peaks which are the distal peaks.
Our next step is to then annotate these peaks using other data sets.
So one option that we do is intersecting these master peaks with transcription factor ChIP-seq
peaks from all different cell types, and the other step is actually looking for enrichment
in histone marks signal.
So the way we actually do this is we look to see is this peak, compared to just random
regions in the genome that we select, enriched in a particular signal.
And what this looks like, this is an example here done in GM12878 cells.
This is looking at distal DNase master peaks and the enrichment in this signal compared
with the background.
So the background here is this light green.
So you can see that most of these random regions that we select are enriched in H3K27, but
these TSS distal master peaks that we see are actually highly enriched in H3K27 acetylation
signal.
So by essentially creating this background distribution, we can create a cutoff and select
anything that’s above this cutoff as being enriched in this signal.
So for this process we focused on four different histone marks.
We did H3K4me3, K9 acetylation, K27 acetylation, and K4me1, and each one of these has slightly
different properties.
So for example H3K4me3 is known to be enriched at actively transcribed promoters whereas
H3K27 is usually enriched at active enhancers.
So right now this is -- these are the current annotations that you can use and apply to
your own research.
So we have two groups of regulatory elements and so we have proximal regulatory elements
and distal regulatory elements, and these are based on the proximal and distal DNase
master peaks.
We also have two groups that incorporate transcription factor binding so these are the proximal and
distal master peaks that are annotated with the transcription factor peaks.
And then finally we have candidate promoters and candidate enhancers, and these are the
proximal and distal master peaks with the enrichment in the histone marks as well.
So the next question is how can you access these annotations?
So this is the ENCODE portal that Mike Cherry just showed, and here if you look under the
data tab, there’s a tab that’s labeled annotations.
And so if you go to that tab, you’ll actually see a little brief explanation on what I’m
talking about as well as an option to visualize any of these annotations on the UCSC Genome
Browser and WashU Genome Browser.
And here you actually have a listing of all the tracks that I’m talking about and you
can actually download them locally if you want as big BED files, or you can download
them to your local cluster server as well.
So this is just a quick visualization of what these tracks look like on the UCSC Genome
Browser.
So here you actually have a gene here with the transcription start site and on the top
is proximal regulatory elements in the dark green.
In the light green, we have distal regulatory elements, and then we have candidate promoters
here and each one of these tracks is enrichment in a different histone mark.
We have distal regulatory elements that are annotated with the histone marks so we call
these candidate enhancers which you can see each line is a different enrichment and histone
mark.
And then up here, we actually have all the transcription factor data that’s been intersected
with the regulatory elements.
So we both have proximal and distal as well.
If you want more information about any of these particular regulatory elements, so this
is clicking on one of the proximal elements, you can actually see which cell lines -- cell
types and tissue types went into creating this element.
So for example for this proximal regulatory element, all these different cell types were
the individual peaks that went into creating that master peak.
These are some other useful tracks to incorporate into your analysis that are on the UCSC Genome
Browser, including the different gene tracks, the GENCODE annotations.
And there’s also other ENCODE tracks such as the integrated regulation, and then also
the genome segments from tools, so just ChromHMM and Segway.
Michael’s also incorporated this data on the WashU EpiGenome Browser and so if you
prefer to use this, we also have all the tracks on here.
And once again, they’re separated into proximal and distal regulatory elements and candidate
enhancers and candidate promoters.
If you click on one of these regions for more information, you’ll once again see which
cell types and tissue types went into creating this particular element.
So this slide is just for your own information if you want to use these genome browser links.
And so these are just some future directions of the project.
Michael’s hoping to make this whole project open source and he’s currently working on
generating mouse annotations as well.
So in case you use mice as a model organism, you can actually use an analogous encyclopedia
that’s created the same way as the human one.
And then finally, we want to add more data.
So we want to actually refine our use of transcription factor data since certain transcription factors
bind and enhancers versus promoters, we’d like to incorporate this data into our project.
And then finally incorporate other data such as IA-seq and the 3D contact data like ChIA-PET
and Hi-C. And then finally also use tools such as ChromHMM and Segway to further define
and annotate our encyclopedia.
So now I’m changing gears a bit.
I’m going to be talking about variant annotation using RegulomeDB and HaploReg.
So the motivation behind this is that I’m sure you’ve heard at this conference and
even earlier today in this session is that the majority of variants reported by genome
wide association studies are non-coding regions of the genome.
And often the variant that’s reported by the GWAS is not necessarily the causal variant.
So the motivation is that by using ENCODE data you can actually annotate non-coding
regions of the genome and try to figure out how these variants are contributing to the
phenotype that you’re interested in.
So these are two tools from ENCODE groups.
The first is RegulomeDB and the second one is HaploReg.
And so RegulomeDB is from Mike Cherry and Mike Snyder’s labs and what’s nice about
this tool is that it actually gives you a score for how likely a variant is to disrupt
transcription factor binding.
So -- and these scores are based on different lines of evidence that overlap your variant
of interest.
So for example, there’s evidence such as is your variant at -- has your variant been
annotated as an eQTL?
Does it overlap a transcription factor binding site?
Does it overlap a DNase peak?
So depending on the different combinations of these lines of evidence, you will actually
have a different score for each one of these variants, and the lower your score, the higher
the letter, the more likely it is to affect transcription factor binding.
So if you actually go to the website this is what it looks like, and you’re able to
enter your data in a variety of ways.
You can enter your SNP IDs directly, you can enter a range that you’re interested in,
or you can also upload BED files and VCF files if you have a lot of SNPs that you want to
investigate.
So in this case I just chose a region on chromosome two from 20,000 to 30,000, and when you click
submit, your results will be presented as follows.
So in this particular region, there was 44 SNPs and it will rank them by highest or most
significant score to lowest score.
So if you want to find out more information about one of these SNPs, you can just click
the score itself, and it will bring you to a page that has a whole bunch of data on it.
So it starts at the beginning by having just a small snapshot of the UCSC Genome Browser,
and here you have ENCODE tracks so for example DNase hypersensitivity sites, transcription
factor data, and some other information like conservation and other SNPs in the region.
As you scroll down, you’ll first see data about protein binding.
This is where you’ll find information for which transcription factors are bound and
overlap this region.
So in this case we have a protein CEBPB which is bound in this region and you actually have
cell type information as well.
You also have a tab called motifs and so there are two types of motifs here.
There are motifs that are calculated just based on sequence which are the PWM category.
So this is based just on the probability weight matrix for the motif, and then you also have
a category called footprinting, which refers to DNase footprints.
So for those also you have cell type information as well as the actual logo, and then you can
have a reference as well for where these PWMs or these footprints were originally reported.
We also have a table for chromatin structures.
This is where you’ll find DNase-seq, and in some cases FAIRE-seq information.
For all these cases, you’re going to have cell type information as well as the original
source of the data, and then any other additional information such as possible chemicals that
were added for the assay.
Finally, you’ll also have histone modification data.
In this case, you actually have ChromHMM annotations which you’ll learn about a little bit later
in this session.
So we have the predicted regulatory state as well as what tissue type this states predicted
it in.
So this is all from the Roadmap data.
So for example in this case, you have this variants and enhancer that’s predicted in
the esophagus.
So if you want to more systematically analyze your variants, you can also download all this
data.
So you -- and if you go to the downloads page, you can download the entire database so you
can investigate a little bit more systematically.
And also if you go to regulome.stanford.edu/gwas, you’ll actually see curated sets of variants
associated with different phenotypes and diseases, and you can download for example an entire
list of annotated variants associated with aging or asthma.
So you can also -- if you focus on a particular disease, you can also go about investigation
that way.
We’re now talking about HaploReg which is a tool from Manolis Kellis’ lab and currently
it’s on a version four.
So previously it was version two and three so this just a recent version, and it works
essentially the same way at the beginning, where if you want to investigate a certain
SNP or certain region, you can query that in the box.
So here I’m actually looking up the same SNP that we just looked at in RegulomeDB.
This time when you hit submit, however, you’ll notice that there’s actually a list of SNPs
as opposed to just one SNP that you queried.
And this is because HaploReg will list all other variants that are in LD with the variant
that you queried.
So here the variant that we queried is in red and by default it uses a European LD structure
and it reports all variants that have an r2 of over 0.8.
So this is where you can actually investigate not only the SNP you’re interested in, but
possible SNPs that are also an LD that may be driving this association.
So when you look on this page here, it’s a summary of each of the SNPs and so you have
information for example about the LD in both r2 and d1 for the queried variant.
You have information about the reference allele, the alternative allele, and the frequencies
in different populations.
And then you also have an overview of the different data types that your variant intersects.
So for example, if it’s predicted to be a promoter region or an enhancer region by
ChromHMM, you have that information there as well as if it’s overlapping a DNase hypersensitivity
site or transcription factor binding site.
So if you want more information on a SNP, you can actually just click on it and you’ll
be brought to a detailed view for that SNP.
So once again this is actually where all the information is as well if you want more information.
So you have details about the sequence, for example the reference alternative allele.
You have information about the closest gene, both annotated by GENCODE and RefSeq.
You also have this nice chart which is new in version four where you have every single
epigenome that was looked at by the Roadmap Epigenomics project as well as a description
of this tissue and then whether or not it overlaps an enhancer or promoter or any of
the ChromHMM annotated regions.
So using two different models which you’ll hear about later on.
So for example, in these cases it’s predicted to be an enhancer in these particular cell
and tissue types.
So it actually gives you the entire matrix all together so you can look all at once.
Also you have data once again about proteins that are bound.
We see once again CEBPB and then we also have information about eeQTL studies as well.
And the final piece that I just want to talk about is these regulatory motifs.
So once again just as we saw in RegulomeDB that this overlaps a motif for CEBPB as well,
but what’s nice is that you also have calculations for the log odd score for both the reference
allele and the alternative allele.
So if you actually want to see how much this SNP may be changing the motif, you can actually
have a quantitative assessment of that.
And so this is just a page talking about the different options that you can use.
It’s really important that you make sure to use the correct population during your
investigation.
So you can see there’s the different HapMap and causal genomes populations listed there,
and you can also change the r2 value that you want to use.
And the final feature I want to talk about is you can actually investigate gene sets
as a whole that have been -- sorry, variant sets as a whole that have been associated
with different diseases.
So these curated lists where you can look at individual studies or combinations of studies
for a particular phenotype.
So in this case, we’re going to be looking at all these SNPs that are associated with
asthma in a European population.
And when you run your analysis, you’ll see results like we saw before, but you’ll also
have this nice table where you’re actually looking for enrichment and enhancers that
have been predicted in different cell and tissue types.
So here you can actually see there’s these different cell types and tissue types and
you have the observed number of SNPs for your particular category in overlapping these regions,
as well as the expected number if you looked at all SNPs or the expected number if you
looked at other SNPs in the GWAS database.
And so you can see, for example your asthma SNPs, there’s enrichment in one of the cell
lines which is might be clinically relevant to what you’re looking at.
So just to finish up, I’d like to really point out Michael Purcaro who did a lot of
the work with the encyclopedia, as well as Sowmya who’s recently graduated from our
lab, as well as all of the other members of the ENCODE consortium, as well as the Stam
Lab who did all the work with the DNase master peaks.
So if there’s any questions, feel free to come up to me afterwards.
Also if you want more information about the tutorials, I have more in-depth tutorials
for both of these topics that are on the ENCODE website.
[applause]
[end of transcript]