 So the first exercise has to do with looking at gene expression. So SPI-1 is a transcription factor that's involved in blood cell development. So we can use screen to look at the gene expression for this gene. So we can enter it in the search box and search in human. And here we can look at the gene expression. So once again, this is from RNA-seq experiments. These are polyA selected RNA-seq. So here we can see that we have high expression in blood cell lines. So this is a blood cancer cell line as well as, for example, spleen tissue and other blood cell lines going down here. We also have high rampage expression. So rampage captures the 5' end of RNA transcripts. You can actually get transcript level quantification on not just gene level. So we can see here that the transcripts for this gene have really high expression in different spleen tissue. We can actually look to see how many transcripts there are for this gene here. There's actually four of them. If you look at different transcription start sites, you can actually see there's different expression levels. So this one's actually much higher than the previous one. And they continue to go up. So that we answered the transcript question. In general, it's all expressed in blood cells and spleen tissue. The next step was to find the CREs that overlapped the gene, which ones were active in B cells. So if we go to our CRE search results, we can see that you can display different regions around the gene. The default is all the ones that overlap the gene body. We want to just pick the CREs that are active in B cells. We can actually search for B cell in this box here. And there's actually different types of B cells, depending on what assays have been done on different people. We do have one sample that has all four assays. So this would be the best one, probably, to use to investigate. So now we've mirrored our list down to just three CREs that are active in B cells. The next step was to filter those that have a H3K27ACZ score greater than two. You can use this using these slider bars here, so you can either slide it or you can directly enter it into the box. And here you can see it's narrowed down your search result to two. There was a couple questions about what is a good Z score. By default, we use a Z score of 1.64, which is the 95th percentile, but there may be situations where you want a more stringent or more lax Z score. So for example, some of my analysis, I use a Z score minimum of two. And sometimes I can clean up some of your results. So the next step was actually selecting one of these CREs. This is a promoter-like CRE, and looking and analyzing its activity. So if we select this CRE, we can see it has really high H3K4M3 and H327ACZ scores in all the different blood cells, particularly B cells, also high DNAs. You'll notice that you do have different cell and tissue types, depending on the assay. And this is just because we're limited. For example, DNAs is performed in some cell types, but not performed in others. So for example, if you see one in one table, but not the other, you can make sure you can even search, for example, to see if you have it or not. So the next step was looking to see the CRE overlapping peaks. We can look at the TF-HISMA and intersection for this. And so you can actually look to see that it overlaps polymerase 2. If you click on the bar, and we bring up all the experiments and the links to the ENCODE project to actually look at the experiments. And you can see all these are done in lymphoblastoid cell lines, once again keeping in the theme of it's all blood related. And finally, we have the orthologous mouse CRE. We can look at the tab here and click on the CRE. This will bring us to the orthologous one and mouse, which is near the orthologous gene. So that's an overview for the first part. The second part is using the GWAS app. So this was from the Landmark paper in 2014 that looked at a really large cohort of patients with schizophrenia. So this is one of the studies that we've preloaded into screen. If we click on this study here, we can look for enrichments in certain tissues. And as you can see here, all the top ones are all from brain, particularly the temporal lobe, the midfrontal area. So the top enriched one here is the temporal lobe. If we click it, it will be all the CREs that have high signal in the temporal lobe as well as the SNPs that overlap them. So then the next part was about sorting the table and selecting the CRE at the top. So once again, all of these columns are sortable. We can sort, for example, by just gene. If we click this one on the top here, which we then will bring to this table here. So even though this CRE overlaps the SNP, like with schizophrenia, we can see it's actually active in a lot of these immune cell types, which is interesting because we saw enrichment in brain. Instead, if we actually look at the nearby linked genes, we see that there's actually a polymerase 2-CHIAPET connection between the CRE and the gene BCL11B. So it's different than the nearest gene. It's instead linked to its nearest protein coding gene. We can look at the ethylgous mouse CREs as well. And here once again, here it doesn't have the non-coding gene in the mouse genome. Instead, the closest gene is BCL11B. If we click on this CRE, it's actually a very different activity pattern. We see that it's active across brain tissues instead. So even though in adult humans, it's active in a lot of these blood cell lines in mouse, it seems to be active during brain development. And then finally, I think we've looked at the differential gene expression app. So we can retrieve this by going to the CRE results page. And from now, as you'll notice, we have all these delta symbols. Just a quick look at the gene expression first. We can see that it's really highly expressed in both developing thymus and forebrain tissue. So it makes sense that the CRE would be active in brain, since the gene's also expressed in brain. But then there's also that immune component as well with high expression in the thymus. So if you go to the back here, you go to the gene expression app, differential expression app. We can pick what cell types we're interested in. So for example, high in, let's say pick forebrain. We can see that this gene here, BCL11B, it's much higher expressed at later time points of brain development. And it's actually quite correlated with all these enhancer-like CREs as well. So once again, so the theme here is that you can use screen and use the data in the encode encyclopedia to try to develop hypotheses, try to narrow down your data. A lot of you have these large lists of genes or SNPs that you're interested in. And so you can use this data to try to narrow down your focus and figure out what you want to experimentally validate.