# Viewing DESeq2 results and creating a gene list Once we have performed DESeq2 to identify differentially expressed genes, we can create a list of significantly differentially expressed genes using cutoff thresholds. * Double click the **DESeq2** data node to open the task report The task report shows genes on rows and the results of the DESeq2 on columns (Figure 1).

*Figure 1. Viewing the DESeq2 task report*

To get a sense of what filtering thresholds to set, we can view a volcano plot for a comparison. * Click ![Volcano icon](https://1384254481-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FJVEESmJAPppJ3ijFq5aR%2Fuploads%2Fgit-blob-0356976f14f78eaf8131d71634d7494c100b8750%2Fvolcano-icon.png?alt=media) next to the 5uM vs. 0uM comparison A volcano plot will open showing p-value on the y-axis and fold-change on the x-axis (Figure 2). If the gene labels are on (not shown), click on the plot to turn them off.

*Figure 2. Viewing DESeq2 results with a volcano plot*

Thresholds for the cutoff lines are set using the Statistics card (Left panel > Configure > Statistics). The default thresholds are |2| for the X axis and 0.05 for the Y axis. * Switch to the browser tab showing the DESeq2 report * Click **FDR step up** * Click the triangle next to FDR step up to open the FDR step up options * Leave **All contrasts** selected * Set the cutoff value to **0.05**. Hit **Enter**. This will include genes that have a FDR step up value of less than or equal to 0.05 for all three contrasts, 5μM vs. 0μM, 10μM vs. 0μM and 5μM:10μM vs. 0μM. FDR step up is the false discovery rate adjusted p-value used by convention in microarray and next generation sequencing data sets in place of unadjusted p-value. * Click **Fold-change** * Click the triangle next to Fold-change to open the Fold-change options * Leave **All contrasts** selected * Set to From **-2** to **2** with **Exclude range** selected. Hit **Enter**. Note that the number of genes that pass the filter is listed at the top of the filter menu next to Results: and will update to reflect any changes to the filter. Here, 28 genes pass the filter (Figure 3). Depending on your settings, the number may be slightly different.

*Figure 3. Applying filters to the DESeq2 results*

* Click

to create a data node with only the genes that pass the filter This creates a Filter list task node and a Filtered feature list data node (Figure 4).

*Figure 4. Filter list and a new Feature list node are added to the pipeline*

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