Michael Purcaro: Well, thank you very much for letting me discuss
these projects today.
I’m the current programmer on these two things.
This is sort of the primordial ENCODE encyclopedia, and Factor Book is a product of the lab from
some papers that were written back in 2012, and I’ve been working on updating the code
base.
So, let’s start out with encyclopedia.
So --
[inaudible]
Michael Purcaro: Louder?
Okay.
I’ll talk that way, then.
So, ENCODE has been tremendously successful getting all of these human experimental datasets
up.
We’ve hundreds and hundreds of datasets over several different assays.
What the first three letters of ENCODE -- E-N-C are for encyclopedia, and we don’t quite
have that yet.
And this is something that’s becoming an increasing concern.
And we’ve been discussing, at some of the ENCODE Consortium meetings and some of the
phone calls.
So, we’d like to integrate this data together and actually figure out the functional elements
and build and visualize the encyclopedia.
In my mind, I kind of see the visualization, some day, as Google Maps for the genome.
You can have a very, very far zoomed out view that has long-range interaction information,
high C3D contact, sort of, information, and then, you can zoom all the way down to a single
based pair and look up SNIP data and whatnot.
So, I don’t know how to do that, yet, but we can start out with something a little bit
simpler.
To do a functional annotation, there’s actually a very nice analogy made by this paper from
-- it was coauthored by Snyder and Gerstein.
You can kind of think of building functional annotations as doing signal analysis.
You start with a raw signal.
You smooth it.
You normalize it.
You threshold it, and then you start actually segmenting it and building up pieces and pieces
with higher and higher amounts of information and data.
Right now, on the ENCODE project site, there’s already a few different annotations available,
and there’s different levels of complexity.
You could just look at transcription start sites.
You could just look at uniformly processed peaks that you can now produce from the last
session.
You can look at 3D contact information.
Luca will talk about using hidden Markov models encouraging them to actually [spelled phonetically]
do the segmentation.
You could do dynamic baiting nets.
Today, though, I’m going to talk about making an annotation using -- for candidate enhancers
and promoters.
Now this is going to be based upon DNase histone marks, and both of these give information
about the chromotin structure.
DNase may include regulatory elements besides enhancers and promoters, but for a DNase peak
that’s been -- that has an enhancement of -- for instance H3K27ac, you have a pretty
good likelihood there’s actually going to be an enhancer.
So we’re going to build up an annotation on DNase histone marks, and hopefully display
it in the browser.
Based upon both the data availability, and based upon many of the factors that have,
kind of, been hinted at this morning, we’re using four different histone marks -- 4me3
to annotate promoters, 9ac for promoters and enhancers, 27ac for enhancers, as well as
for me1.
Now, to start with, we have the Stam lab take all the DNA experiments available from ENCODE
and Roadmap and merge them together.
This gives us a unified view of chromatin accessibility across all the cell types we
have.
Now, this includes all the Stam and Crawford DNase datasets and all the biological and
technical replicates, but we didn’t just do a bed tools merge on all these data sets.
We did something a little bit different.
We take the DNase datasets and look at a region where all the peaks overlap.
So, [unintelligible] here, we have N [spelled phonetically] different cell types and some
of these cell types have no peaks, but others have, you know, more or less, overlapping
DNase peaks.
The diagram is perhaps a bit overly simplified, but -- so, you can merge these into a pile
that kind of looks like this.
Instead of taking the entire pile as one region, what we call a DNase-hypersensitive region,
we’re going to select something we call the master peak, and that’s the peak with
the strongest signal.
That peak will become the representative peak for that region, and we will discard the rest
of the peaks in that area.
The weak peaks shall not inherit the region.
Now, from these master peaks, we have a few advantages.
We get this set of unique non-overlapping peaks.
We get this representative peak for this area, and that peak, we annotate with all the cell
types that are involved in that region.
These peaks can span all the datasets, and when you actually merge them all together,
they cover about 20 percent of the genome.
From this track -- from this set of master peaks, we can then divvy these up into transcription
start site proximal and transcription start site distal peaks.
The TSS-proximal -- they’re within 2kb of a GENCODE TSS annotation.
Okay.
So now, we can actually start looking at annotating candidate promoters.
For us, a candidate promoter is going to be two things.
One, they’re going to be a TSS-proximal master peak, and then two, they’re going
to be -- have an annotation with a TF binding site.
For now, we use all the ChIP-seq TFs, all either gap peak or narrow peak, whatnot, from
ENCODE.
So, we did a humongous bedtools merge, and build an annotation track.
These peaks are also TSS-proximal.
For candidate enhancers, we do something similar but with one extra step.
For enhancers, we’re going to use the distal peaks, like I alluded to before, and we’re
going to annotate them with TF peaks.
But we’re also going to look at 27ac enrichment.
So, given the master peak, we’re going to go into the 27ac signal file, look at that
region, look at the 1kb region, and compute the percentile of that signal over background.
Background being, you know, randomly chosen segments of that signal track, you know, ignoring
other locations with DNase peaks and in the ENCODE blacklist.
So, if this 27ac region is in the 95th percentile, we’re going to call that a candidate enhancer.
Now, as a little bit of a justification, we can do just what I talked about on one cell
line -- cell type.
We can look at genome 12a78.
We can look at enrichment of TSS-proximal DNase peaks and see how they’re enriched
in 27ac peaks.
And we find that, yeah, for the actual DNase peaks, they have very strong enrichment.
Same thing happens for the distal peaks.
Now, how well do the master peaks fare?
You can take all of 27ac peaks, for all the different cell types we’re using, and you
can intersect those with the master peaks for each particular cell type.
So, for instance, we may have pancreas here, and we’re going to take a gap peak file
for 27ac.
And we’re going to intersect that with the master peaks that have the pancreas cell type
in them.
And we see that more than three-quarters of the 27ac peaks also intersect one of our DNase
master peaks.
Swapping that round, we can look at the master peaks for each cell type and see what percentage
of those peaks are covered by 27ac, and we see that that percentage is a good bit lower.
There are other things going on with those peaks, as we kind of alluded to before.
So, that’s it.
That’s how we actually build these tracks.
It’s a bunch of Python and shell script [unintelligible] in bedtools.
So, where can we actually get these tracks?
As Mike and Eurie mentioned, you can get them from the ENCODE project site under annotations,
and you can actually go the genome browser right now, if you want, or to the WashU browser
and look through them and look for your favorite region.
You can also directly download all the tracks that we actually produced, and these are big
bed files.
And Eurie has committed them into the ENCODE project, and they have a unique accession
number.
So, I cannot change these on you under your feet.
You can always go back and get the exact file that you were using.
These are the links typed out.
As mentioned yesterday, I prefer using the track hub, because it actually is super easy
to generate, and you can look in here and actually see how to colorize your tracker,
fiddle with the tracker a little bit.
We also produce tracks for WashU called hammock tracks.
These tracks are really designed, specifically, for annotation, and you can actually embed
JSON data into the track file and give the user a little bit more information.
There’s some more information about the hammock track here, and I’ll have a little
demonstration shortly.
So, this is what it looks like in the UCSC genome browser, and if you actually click
on the one of the annotations, you can get things like, for instance, if you clicked
on DNase, you can get all the cell types involved from that region, just as promised before.
As kind of a side note, when using these tracks, you may want to enable these other tracks
in UCSC.
These can actually be quite helpful to figure out what is actually going on in the region
if you haven’t already done this.
Now, for WashU, this is what the annotation tracks look like, and you can actually click
on each peak.
And like you saw in the genome browser, you can also get the list of cell types.
You just don’t have to open up a separate window.
So, the next steps for this are, I actually have to open source, finish up, and clean
up the code a bit more.
I need to generate this, kind of, primordial encyclopedia for mouse annotations, and then,
we need to figure out what other data to add.
All right.
Changing gears slightly, Factor Book.
How many people here have actually used Factor Book?
One?
Three.
Five.
Okay.
So, I’m at the last 10 or 15 minutes of my discussion.
This is not a track-based approach to looking at things in ENCODE data.
Here, we're looking at data summarized on TFs, in particular.
It’s not easy to show this in the genome browser.
This is all ChIP-seq TF data from ENCODE, and we’re trying to pull together a bunch
of, sort of useful analyses that people can look up.
There are two different sites now for Factor Book.
One is the original one, which is a bit aged now.
And then this is one I’ve re-implemented.
On the main page, you get a matrix of cell types versus the TFs.
The number in the intersection is the number of ChIP-seq experiments from ENCODE that we
have.
Right now, you can click on the TF.
At some point, you’ll be able to click on the cell type also.
And then you get a page that has a bunch of information.
One part of that is about the function of the TF, and this we’ve distilled from a
bunch of public websites.
And, you know, possibly we have the protein family, consensus sequence, multilevel consensus
sequence.
If you have a PDB image, we try to pull that in for you.
In addition to that, we have these average histone profiles.
So, what happens is, we take a 2kb window around every summit -- every peak, and then,
we actually compute the average histone signal for that.
We split these apart, kind of like in the encyclopedia in the proximal and distal sites,
and then you can get, for instance here, proximal H3K24me2 or you can get distal H3K4me3.
And those are color coded individually.
And this is sort of a little interactive JavaScript chart that you can use to explore the data
a bit.
We also provide profiles of the average nucleosome positioning.
This is derived from MNase-seq data for genome 878 and K562, and this is split again into
proximal and distal.
Proximal is within 1kb of a TSS, and distal is everything else.
We also run MEME-ChIP on the top 500 peaks, and we pull out the top five logos.
And we are going to give you a little bit of information.
So, for instance, this is the top on motif.
Out of the top 500 ChIP-seq peaks -- 84 had this sequence.
This was the e-value for mean ChIP, and this is the consensus sequence.
And lastly, for Factor Book, we have a bunch of these heat maps.
So, our interest, and maybe it was alluded to a little bit before, is in comparing this
TF to other histones in the same cell type, and other TFs in the same cell type.
So, in here, if you look here, let’s say we’re in A549, and we’re looking at CTCF.
We can compare CTCF against the histone control and all the other histone marks we have in
A549.
Each row here is a ChIP-seq peak.
These have been sorted, highest peak to lowest, and this is -- for histone, this is a 10kb
window.
We then show the enrichment as a heat map on a normalized scale over the 10kb window
for each histone mark.
Likewise, for TF, we show binding strength across all the other TFs available in this
cell type.
This is a little bit narrow window.
This is 2kb, and this chart is perhaps a touch old.
The beta site has a little different orientation of this, but it’s the same idea.
We have ChIP-seq peaks.
We’re going to lump those to 10kb, 10,000 peaks, sorted by ChIP-seq strength, and then
the comparison against all other histone and TFs.
The first release of Factor Book had about 550 ChIP-seq datasets.
The new release has over 650 in more cell types, and we’re also now introducing it
for mouse.
This still needs a little bit more work, on my part, but that will become released very
soon.
So, the next steps -- Factor Book will also be open sourced, and this will go in sync
with ENCODE project as due TF ChIP-seq datasets are released from ENCODE.
We will immediately import them into Factor Book.
And as, kind of, a new development, we’re also giving this a full rest API.
So, for instance, all the profile information, you can actually download via JSON and use
yourself.
You’ll be able to download all the motifs yourself and use in your own programming -- genome
projects, very similar to how you’d parse JSON from ENCODE project.
And last, I really would plead if you have any new ideas for Factor Book that you’d
like to see, just to e-mail me -- really want to make this a more useful tool and hopefully,
some of the community can really use for a wide variety of things.
And that’s all I have.
And particularly, I’d like to thank Sowmya, who really built the primordial encyclopedia.
And I took over for her.
And that’s all I have for now.
[applause]
Male Speaker: So, we have time for some questions.
Male Speaker: Do you calculate the profiles on the fly or
do you have to recalculate them?
Michael Purcaro: It’s all pre-stored.
Male Speaker 3: Okay.
So, every time -- okay.
Male Speaker: Quick question from me.
So, the things [unintelligible] the Factor Book is that in sync with the current ENCODE
data --
Michael Purcaro: And beta?
For the most part, yes.
Male Speaker: Okay.
Cool.
Thanks.
Michael Purcaro: For www, no.
Male Speaker: Okay.
Female Speaker: So, when you define the proximal from the
TSS, is the TSS specific for a major transcript, or how do you decide that?
Michael Purcaro: Oh.
That’s a good question.
I’m not sure.
I’d have to look that up for you.
Female Speaker: Okay.
Male Speaker: Cool.
So, thanks, Michael, again.
I just want to make sure that you know that all the slides in this session can be downloaded
from the conference website, ENCODE2015.org, and all the handouts are there.
So just in case you didn’t follow all the procedures in previous sessions or this session,
you can always go to the website and download the PowerPoint and the PDF files.
If the PowerPoint is not there yet, they will be available after conference.
[end of transcript]