[SOUND] [MUSIC] Hi, my name is Max. Today, I'm going to show you how to use Enrichr. Enrichr is a web-based, gene list enrichment analysis tool that has a large collection of gene set libraries, an alternative way to rank enriched terms and various types of interactive visualizations. Library tab will provide a list of gene set libraries we have created and collected. These libraries are available for download for use and analysis. You also can trace back to the original sources of these libraries. To download the library, just press the red icon on the rightmost column. To find knowledge about gene of interest from information start within this gene set librarys, you can use the find gene function in Enrichr. Input the name of the gene using Entrez gene symbols or select the gene from our complete list. Then your gene of interest will be shown in different categories as a preview. Expanding the tree branches, you can view the relationships between the genes and, in functional terms, across many of the biological categories in Enrichr. Enrichr allows three options for data input to its main function in enrichment analysis. You can input a regular gene list a quantitative gene set or a bed file which is a text file generated from a typical Chip seek processing pipeline. To process your BED file data, select choose file and upload your file. For the other two ways of input, you can cut and paste your list, or upload a file. Enrichr upload file function will recognize were we uploaded the BED file, a regular gene list or quantitative set. In the text area, your input file, you can paste regular or quantitative gene sets. Quantitative gene sets have weights for each gene in range from zero to one, where a weight of zero means that the gene is not a member of the set and one means maximum weight, maximum membership level. This allows a more refined analysis that takes into account the weights and ranks of genes from the input list. The regular list of gene input options simply takes the gene names in each row. Enrichr has examples for each input type so you can mimic the formats for your data. If you check the contribute check box at the bottom left corner of the input form your list will become searchable by others and will be added to the crowdsourcing gene sets. If you decide to contribute to the list you will be asked to enter a brief description of it and decide whether you are okay with people contacting you by email about your gene set. Once you've submitted your input you will be presented with the following screen. It provides results for each category, transcription, pathways, and ontologies, diseases and drugs, cell types, miscellaneous, legacy, and crowd. Each category contains a set of gene set libraries. Start clicking on library names to see the enrichment results for each library. If you click on the C-shaped icon on the top right corner you can, view your input gene lists that you are submitting. This is a most useful when uploading a BED file. Here what happens when click on the gene list icon. If you click on the share icon on the top right corner, you will be provided with a permanent link to the enrichment analysis results. You can use this link to return back to earlier results or you can share the results pages with your colleagues, or use it as a reference in the publications. As I mentioned, gene set libraries in Enrichr are divided into several categories, and here we are looking at these gene set libraries under the transcription categories. In general, lists or signatures from gene expression data would benefit from the analysis provided here. The analysis will identify potential upstream regulators such as transcription factors. Here are the libraries under the Pathway category. Data from experiments as well as genomics can benefit from analysis using this category, which can used to place genes and proteins in pathways and complexes. Libraries in Legacy category have a dated version But they are kept in Enrichr for sake of provenance. We keep them so we can repeat the results even after the library was updated. As the gene set libraries from the crowdsourcing category were created with the help of students from the Network Analysis in Systems Biology course. We hope to add new libraries here with your contribution. Let's return to analysis results. After clicking the gene set library name for example, here I click ChEA which is a gene set library we created by collecting gene sets from the published ChIP seq studies is purely targets of transcription factors You're presented with a bar chart. Bar represents the terms in the library and are sorted by. The brighter and longer the bar, the more enriched the term is within the gene set. Here is a transcription factor profiled in paper with PubMed ID. In the cell type in this organism. So putative targets of the transcription factor E2F1 are also enriched with this simple gene list for this gene set line, library. The combined score is alternative score for ranking in which terms? In the paper that describes [INAUDIBLE] that this math is better for prioritizing terms when compared to the Fischer test. You can read more about this method in paper published by Biomath Central in Bioinformatics in 2013. You can sort bar graph by Different score method by clicking on it. Click on the bars again and you'll see them sorted by p value, which is computed using Fisher exact test. And, once again, now the bars are sorted by their rank by scores. This is a z score computed by assessing that deviation from the expected rank. If you click on the cog shaped icon on the top right corner, you can change the color of the bar graph. It also works for network and grid utilization, which I will demonstrate in a moment. Here, I change the bar graph to blue. The table view option of the enrichment results gives an explicit view of the values, s course and compliance course. You can assort terms by ascending or descending, or by clicking on header of column. Filter the results by a specific term using the search box or downloads and turn the table into Excel. Hovering over the terms in the table displays R11 genes between the input and genes that are belong to the term. Hyperlink provides you the entire results table in tab-delimited format by clicking the link Export entries to table. The table also has R11 genes in An additional column. In brief view of terms from a gene set library represent by a squares of a canvas. The terms on the canvas are organized based on the term gene content similarity where an area of high similarity is made brighter. The grid can be clicked to set view, where and which term highlighted with circles colored from bright white to grey, based on P-values. You can export these images and view them for publications and presentations. Z-score and P-value next to the cameras is 25. How close Enrich turns out on the canvas, compared to what you would expect by chance. This was done as an attempt to see if Enrich terms are clustered and the overlapping genes share terms. So this current p value shows how dense or clustered the top ten [INAUDIBLE] enrichment terms are on the grid. In the network, use it he top ten terms are displayed as the network where the nodes represent the enriched terms, and the links represent the gene content similarity among the enriched terms. Enriched provides the ability for users to have an account, although this is not a requirement. The benefits of having an account is that you can have a private collection of gene sets so you don't have to keep uploading your gene sets over and over. Log in and click on the username on the top right corner gives you a view of your previously uploaded gene sets, you can filter the results for particular gene sets by searching them. Clicking on the set name will return you to analysis results for that input list. If you have questions and suggestions, we provided tell it tutorial and frequently ask questions under Enrichr's help tab. You can also use the feedback form to ask questions and give a suggestion. I hope you enjoy using Enrichr and find it helpful for your research. [MUSIC]
