Learning Outcomes

• Understand the basics of running the RNA-seq pipeline in Foundry.

RNA-seq Pipeline Run Tutorial

Attaching Data to Foundry

1. Navigate to the Data tab and click on the URL section. Enter the following URL:

   https://www.viafoundry.com/test_data/fastq_mouse/
   

   then click Connect.

   

2. Select all files by checking the 'select all' check-box, then click Continue with Selected Files.

   

3. From the "Dataset Type" dropdown, select Paired List. Enter .1.fastq.gz as the "R1 Pattern" and .2.fastq.gz as the "R2 Pattern". Click Add All Files.

   

4. Provide a name for the data collection (this name will be used for future runs) and click Save Files to Dataset.

   

Submitting a Run

1. From the Pipelines tab, search for RNA-seq Pipeline and click on the corresponding card.

   

2. On the RNA-seq Pipeline page, click Run, then select Create New Run.

   

3. Choose a project (or create a new one), assign a name to the run, and click Create.

   

4. On the run page, select your environment in the Run Environment section.

   

5. In the "Inputs" section, next to "reads (Optional)", click the File button.

   

6. In the "Change Input File" window, find your dataset using the collection name in the Filter By Dataset box. Check the box to select all files, then click Save.

   

7. In the "Inputs" section, set the "mate" to pair and the "genome_build" to mousetest_mm10.

   

8. Leave the remaining inputs at their default settings.

9. Click Run in the top right corner, then select Start. The RNA-seq pipeline run typically takes several minutes to complete.

10. Go to the Log tab and click on log.txt to track the progress of your run.

    

Viewing Results

1. Once the status bar in the top right changes from blue "Running" to green "Completed", navigate to the Report tab to view the final reports.

   

2. In the "multiqc" directory, click on multiqc_report.html to access the report. You can open it in a new window, view it in full screen, or download it using the buttons at the top right.

   

3. In the "summary" directory, click on overall_summary.tsv to review the alignment statistics.

   

4. In the "rsem_summary" directory, click on genes_expression_expected_count.tsv and switch to "Default View" to check the RSEM quantifications.

   

5. Alternatively, switch to the "App View" and launch the DEBrowswer app to explore Differential Expression.

   

   

Demo Dataset Limitation

To expedite the tutorial, the demo dataset has been downsampled to include only a few genes. As a result, the differential expression analysis graphs may appear sparse.

Congratulations! You have successfully run an RNA-seq pipeline in Foundry!