Genomic analysis made possible

Step 1) Click the “Start Analysis” button on the home page.

Step 2)

Choose the "Analyse microarray data" option

Step 3) You will be redirected to a page to set-up your analysis. Choose a title, a description and the Excel file containing the gene-expression values of your samples.

Typically, this Excel file has biological samples as columns and genes or probes as rows, as the file below.

Step 4) You will receive confirmation that your file has been successfully uploaded and you will receive an email notification when your analysis is ready. This can take up to 24 hours as there is some manual checking to ensure the data is in the correct format.

Step 5) After you receive the email you will be suggested to add clinical annotations/phenotypes to your analysis. In this example I will add one column with the factor “Smoking Status” and the corresponding Smoking Status values of our samples. We will use this factor to compute the differential expression analysis.

- First I’ll create a new annotation column by duplicating the “Sample” column (select the “Sample” column, and click the “duplicate” icon).

- Second, double click the new column header to edit it and double cklick the cells to change their values.

Step 6) Export to Gene-E to compute a differential gene expression analysis.

Select the Gene-E option in the “Analyze” button, a .jnlp file, which is a small Java executable will be downloaded to your computer.

Step 7) Open the downloaded .jnlp file will open Gene-E. Note this screen shots are taken with a mac and may vary slightly depending on the OS and Java version you have. Also, if you are having trouble opening Gene-E make sure your system is compatible by looking at this page.

Note Gene-E opens with the clinical annotations we added further, the “Smoking status” phenotype is shown just bellow the samples and before the genes. As in your Excel file, columns are represented by samples and rows by genes. Every square in the matrix represents the gene-expression value of a gene (row) for a given sample (column). Red means highly expressed and blue means lower expressed. You can change the scale and colors in the Gene-E settings menu. We will use this factor to compute a differential gene expression analysis. Click on the magnifier/heatmap icon on top as shown in the image bellow.

Step 8) Select the factor and values you want to compare as show in this example.

Step 9) The result opens in another tab with the list of over-expressed genes and their statistical results. moving your mouse in the heatmaps displays information in the Info box on the top right. Note the new heatmap contains statistical results.

Step 1 Export your analysis results to IReport™ (Documentation). Press the “Analyze” button, select the “Differential Expression (Cuffdiff)” option, and press the “Create Ingenuity® IReport™” button.

Step 2 Send your data to your Ingenuity® IReport™ account. Create an account if you don’t already have one.

If you do not have an iReport account create one here

Go back to InSilico DB an insert your Ingenuity email

Study title, analysis title and description are filed in for you, you can edit them if necessary. Click Send.

Processing takes a couple of minutes.

You can either wait for this window to appear or check your email and follow the link provided by Ingenuity® IReport™

Log in IReport™

The first window is a summary view that shows global results about your analysis

One can see this differential analysis shows 85 differentially expressed genes (DEGs). Clicking the square header in the DEGs table will order them according to gene expression fold change.

Clicking on a gene (here TFF1) will bring you to the gene view, providing detailed information.

Going back to the Summary Tab, the right panel of shows overrepresented pathways, processes and diseases.

Hovering your mouse over a pathway will highlight the genes involved in this pathway in the volcano plot.

Clicking on a pathway will lead to the pathway view, including graphical view and relevant publications.

Step 1 Export your analysis results to IGV (IGV Documentation). Press the “Analyze” button, select the “BAM files” option (BAM files contain sequence reads that are aligned to a genome of reference), and press the “Open in IGV” button. This will download a small file with a .jnlp extension

Step 2 Open the .jnlp file you just downloaded to launch IGV. Make sure the right versions of Java are installed in your computer. The following screenshots show the steps I followed to update Java on my computer.

Step 3 IGV opens with a global genome view. You can either double click the screen to zoom in or type a gene or chromosomal location in the search bar. In this tutorial we will look for gene TFF1 as it appeared to be differentially expressed gene in our iReport tutorial.

The upper band shows the chromosomal location currently displayed. Each line represents a sample and is divided in two sub-tracks for the coverage and the aligned reads. Different attributes coming from InSilico curations are shown with color bars on the right of the sample names and Refseq genes are pictured in blue below the samples. For a complete description of the IGV main window, see IGV Documentation.

Step 4 Note the difference in coverage for samples which are Estrogen Receptor positive VS samples which are Estrogen Receptor negative. The phenotypes are shown as color bars next to the sample names, phenotype labels are shown at the top and different phenotype labels have different colors. Hovering your mouse over the colors bars displays the phenotype labels. In this example pink bars represent positive Estrogen Receptor samples and light blue represent negative Estrogen Receptor samples.

Note negative Estrogen Receptor samples have no coverage for the TFF1 gene (the gene is represented at the bottom of the screen – blue wide boxes represent the exonic regions of the gene).

I will select the negative Estrogen Receptor samples and remove them from our display to concentrate on the positive ones. Select the samples by clicking on their name, Select several samples by holding the control key on your keyboard while clicking several samples’ names. press the right click and select the “remove tracks” option.

Step 5 To zoom on a specific region, click and drag your mouse over the desired chromosomic region region, this will render a purple rectangle over the desired area. Press the Enter key to zoom in and see the reads in detail. This can be helpful to make sure reads were cleanly aligned. Also, note the scale at the top-left corner of each track ([0-1484],[0-6572],[0-69]…) which represents the coverage of each sample for that given region. Sample GSM665133 has a much lower coverage than the rest.

Step 6 Let’s do a Sashimi plot which is used to visualize the the different expressed transcripts. First, adjust the region over which you want to do a sashimi plot, zoom out to get back to the global gene view showing three exons, select the samples of interest, right click and select the sashimi plot option.