 Good morning, everybody. My name is Yan Li, and I'm Dr. Fonsa at Angkor-Heslav, and I'm a grad student. I'm going to present two web-based browsers today. One is the encode element browser, which is a suite of four tools that look at two different datasets. One is gene expression, and the other one is cis-regulatory elements. And note when I say cis-regulatory elements, they're putative cis-regulatory elements. So they're not known to be true cis-regulatory elements without any functional validation experiments. And the next one is this 3D genome browser, which look at Hi-C data, as well as Chiapet data, which visualizes a 3D genome in a two-dimensional plane. So what are the goals of having these resources? So number one is we want to query the most relevant encode data. And by this, I mean we want to really zoom in into the datasets we're interested in, such as the most relevant tissues or the genes that we're interested in. As I understand, most people here are interested in certain genes rather than the entire genome. The next one is to visualize complex data. And this is especially true for the Hi-C that the datasets which are comprised of complex interactions in the 3D space. And the final goal is to provide additional layer of evidence to identify the target genes of cis-regulatory elements, which is putting together the power of both identifying potential functional cis-regulatory elements as well as how they're structurally linked to any target gene. So these tools, as I said, are web-based and they require JavaScript. And I recommend that it supports HTML5 as well. And you will get all this if you have a modern browser, an updated browser. And I recommend Chrome, the latest versions of Chrome, to avoid any technical difficulties. So part one is the encode element browser. Okay, so this is meant to be a live demo. So if you want to work on your computer with me as I go along, that's fine. So this part is basically described how to actually get to the first part, the encode element browser. So first we have to go to the encode portal, the encodeproject.org. Click on data, and then click on annotations. And the first link you see around the center of the page will be the link to the element browser. So if you click that, you'll bring a website like this. And as I said, this browser is a suite of four tools, and it is available for human and mouse. So we're going to work on human for today, but keep in mind that the steps are identical for mouse. So the first part, the first tool number one is option one, which if you insert a gene, it will give you the gene expression of the gene in RPKM across the encode cell types. So here in this demo here, I will enter the gene ikzf1. So if you notice as you start enter, it will prompt you for the gene that you can select. So you don't have to put the whole gene every time. And if you click submit, and it will show up the results. So here are the results. So the first part is the gene ID. So it has many synonyms in synonymous denotations. So we support gene IDs, uniprot ID, as well as RefSeq ID. I mean by the gene ID, I mean gene symbol. So any of those will be fine. And of course, I also show synonyms on symbol. Unfortunately, the symbol is not supported, but we'll get to that in the future. So there's a bar graph. Here's the bar graph of the gene expression in RPKM across different tissues. And here's the raw data. And then the gene of interest that we're actually entered is the ikzf1 gene is the ECROS family zinc finger protein 1, which is involved in immunohematopoiesis. So as you expect, actually, these genes show up in... This is too small to read, but if you go to your website and click on it, it will enlarge the picture. And as you can see, you will see CD20 cells, GM12878, K562, and monocytes and so on. So this is very... So this is consistent on what we know of this gene. So this is the first resource. And the second resource is actually combines number two and three, in which we look at the DHS, the DNA-sensitive sites as well as the transcription factor binding sites that are available in the regions designated by the user. And these sites are found by experiments from the John Stem's lab and Dr. Crawford's lab, but they're compiled by Dr. Won's lab from UMass. And it's a fast and easy way to really determine the citrogatory pelletive, citrogatory elements, and their tissue specificity. So in the first option, you enter the region of interest. You don't have to do this with me if you don't want to because it's a complex number. But if you enter the region of interest, and this is... Spoilers is actually the gene that we just entered, the Echorus gene. The result is going to show you the DHSs as well as TFBSs in those regions. Here are the genetic coordinates as well as the relevant tissues that actually have these regions of interest, these elements of interest. But this is very tedious in entering numbers. So the second part of the tool is actually you can enter the gene itself as well as a region that you would want to purview. So here we enter the AKZF1 gene as well as... We want to look at all the DHSs and transcription factor bindings within 1KB. We can do submit and we see the results similar to the last option but for centered on the gene of interest. So a new functionality that I added just last night actually is that you don't want to copy and paste this, right? So if you want to parse it where you want to save it, you can actually save it here as a comma-separative file and export to Excel. So this is a new functionality that I just implemented that is not actually in my presentation. So the final one, the final tool is the DHS linkage finder. And what DHS linkage is... So a pair of DHS is said to be linked if one proxel DHS which is around a TSS and a distal DSS which is far away from the TSS shows consistent activity across many different tissues. So in this example here, so we have two proxel DHSs here and then one distal DHS. So if you look at the activity for the DHSB, you can see that it is more correlated and more consistent with a distal DHS compared to DHSA. And because these two distal DHS and proxel DHSB are highly correlated and have a high-purescent correlation, we can say that these two pair of DHS are linked. And when they're linked, it's possible that the distal regulates the proxel. But of course, without further functional experiments, we wouldn't know for sure. But this is a good indicator that we're on the right track that this is potentially functional cis-relatory element. So here, for option four, it is also gene-based. So we enter the gene that we have been interested in, IKZF1. And then here is the results. So it shows you the proxel DHS as well, which is correlated to the gene. So these should be the same. That is, have above 0.7 cut-up for purecent correlation for distal DHS. And then if you're interested in one of these regions to see exactly what tissue it is relevant in, it's actually linked to two number two. So if you click on the correlation coefficient here, we actually get back to the option two to look at what DHS and transcription factor bind you under. And we see that there are two possible transcription factor that binds to IF that could regulate aqueous. And the tissues that they show up in is actually consistent with the tissue that aqueous shows up in. So this is a really good way to really hone in your hypothesis and to look for potential cis-reactory element for your gene of interest. So on the other hand, what do we want to look at? We have a gene of interest and what could be our cis-reactory element? We use a different approach with the spatial interaction approach. So here we harness the power of proximity ligation method, especially high C for the 3D genome browser here. And so to get here, we go to 3dgenome.org and then we click on high C interactions. This is for high C and we enter our gene of interest. We enter our gene of interest or region of interest if you prefer. And here are the results for the... Oh, and by the way, up here you can select the data set which have high quality data set. We have the 1KB data set by raw et al from the Lieberman-Aden lab and which have high resolution interaction, 3D genome interaction data set. And we can see here, we can navigate this region with moves left or right or we can zoom in or out. We could adjust the intensity. Here is the high C heat map and this is contextualized by the UCSC genome browser. So this is showing how I identify a region of interest and these regions have high interaction values. So they show up with high intensity in the high C, in the contact matrix. And then with the region of interest, we can double click to zoom in to the region of interest. And you can see the genome browser also zoom in along with the high C heat map to contextualize the region. And then, as I said, you can adjust the intensity of the data set. Here you can see you can either use the slide bar or you can either use the little arrows with the text box or you can directly enter the values of interest as shown here and click refresh. So you could adjust the intensity to look for the very subtle interactions in the localized context. And this is showing how you could interact with the UCSC genome browser as you would normally. And then if you screw up the alignment to the high C map so here I'm modifying a tract. And now the alignment is off now. So I can just basically click on align UCSC genome browser. So this is showing how it's not aligned and then I align. With that button I can automatically align the genome browser. So in here you can use your own data, not just the data that is provided by our server. And for this, if you convert your high C contact matrix into the binary upper triangular matrix files or boiler files you could directly visualize your data without having to upload your text files directly onto the server. And then since I run out of time, we also have the 4C and CHEAPED visualization. You enter your gene of interest. It will show you with that gene as the anchor, the potential interactions nearby that gene. And this is paired with the CHEAPED data to see what is interacting with the TSS of the gene of interest and this is also contextualized by the UCSC genome browser. So with these tools you can actually identify cis-vitro elements as well as their target genes and put them before the hypothesis or for their biological validation. So thank you.