TITLE: Wastewater QC workflow in GalaxyTrakr (SSQuAWK)
AUTHORS: Jasmine   Amirzadegan, Tunc  Kayikcioglu, hugh.rand  , Ruth  Timme, Maria   Balkey

[DESCRIPTION]
PURPOSE: 



Step-by-step instructions for checking sequence quality for SARS-CoV-2 wastewater samples using SSQuAWK: SARS - CoV - 2 Sequence Quality Assurance Workflow and Kontraption. The SSQuAWK workflow, implemented in a custom Galaxy instance, will produce quality assessments for raw reads (Illumina MiSeq paired-end fastq files).  

SCOPE: This protocol covers the following tasks:

1. Set up an account in GalaxyTrakr
2. Create a new history
3. Upload data
4. Execute the SSQuAWK workflow
5. Interpret the results


[STEPS]
SECTION: Account set up
1. Create a GalaxyTrakr account here: https://account.galaxytrakr.org/Account/Register

  

SECTION: Account set up
1.1. Log into your GalaxyTrakr account: https://galaxytrakr.org

  

SECTION: Create a new history
2. Create a new history. 

We recommend creating a new history for each new MiSeq Run and including the flow-cell ID and date in the history name. 

Save your MicroRunQC output here and any other relevant analyses, like serotyping, or AMR detection. 

After all the analysis output from this run is saved to your internal data network or computer, older history's should be purged/deleted so as not to occupy the limited storage space in your account. In some cases it may be useful to save, for a limited time, multiple histories or to run analyses concurrently in multiple histories. In these cases you need to pay attention to your % usage bar (shows % used of allocated storage space) in the upper right corner of the GalaxyTrakr page. If you need additional space you can contact galaxytrakrsupport@fda.hhs.gov and request additional storage.
SECTION: Create a new history
2.1. Create a new history with the "+" symbol in the upper right hand corner. Name your history and press "enter" on your keyboard to save the name.

   

SECTION: Upload data
3. This section will describe the process for uploading raw fastq files into your active History panel. After the files have been uploaded they will stay in your account until they are deleted. 
SECTION: Upload data
3.1. Upload sequence data to your history, using either of the two options circled in red below.

A window will appear in the middle of your screen. This is where you select your files using the "Choose local files"  button at the bottom of the window. The "Choose local files" button is circled in green. These fastq files should be paired (two per sample).

After you've selected your files, press "Start" to initiate your data upload to GalaxyTrakr. The "Start" button is circled in blue.


   

SECTION: Upload data
3.2. As the file uploads complete, each row will turn green. If samples are shown with yellow background, then are still uploading.

  

SECTION: Upload data
3.3. You have just upload a set of forward and reverse reads.  For further analysis these files need to be paired properly so the platform knows which R1 and R2 files go with each sample.  GalaxyTrakr does this by creating a List of Dataset Pairs.

Within your newly created History panel, click the "check box," then select all the files you just uploaded by clicking "All" or by individually selecting the ones you want to pair.




    

SECTION: Upload data
3.4. Check all the files belonging to a pair. In this example, all the files belong to a pair, so I will use the "All" button (circled in red).

Then, use the "For all selected…" dropdown (circled in green), and click on "Build List of Dataset Pairs" (circled in blue). 

  
  

SECTION: Upload data
3.5. GalaxyTrakr will automatically pair the files, but it's good to double check. 

Paired reads will pair in the middle column and turn green.

If everything looks good, then choose a name for your pairs (circled red) and "Create List" (also circled red).

  

  

Alternatively, instead of auto-pairing you can click "choose filters" and  select the appropriate filter for the pairing:

  
SECTION: Upload data
3.6. This paired dataset will now be available for analysis in your history panel. You can run multiple analyses on the same dataset in a history rather than upload the same sequence data to a new history to perform additional analyses. This will help you use your allocated storage space efficiently.

   

SECTION: Run the &lt;&gt; workflow
4. Add the SSQuAWK* workflow to your own "workflows" panel.  You only have to do this step once for each new workflow you need.

*SSQuAWK: SARS - CoV - 2 Sequence Quality Assurance Workflow and Kontraption

SECTION: Run the &lt;&gt; workflow
4.1. Navigate to the “Shared Data" drop down and choose workflows 




  






Then, from the SSQuaWK drop down menu, select import.

  


SECTION: Run the &lt;&gt; workflow
4.2. Navigate to the "Workflow" tab in the top ribbon (boxed in red). The workflow will be imported there.

   

SECTION: Run the &lt;&gt; workflow
4.3. To use the workflow, press the 'play' button (boxed in red) on the right

   

SECTION: Run the &lt;&gt; workflow
4.4. Select the paired list you created earlier by selecting the folder icon (boxed in red), and then the list of pairs (boxed in green).

Click Run Workflow (boxed in blue). 

  

Running the workflow can take some time depending on the number of samples you are analyzing. Once GalaxyTrakr adds the workflow invocation to the queue, you can choose to log out of GalaxyTrakr and log back in at a later time to see if the job is completed.


  
 
SECTION: Run the &lt;&gt; workflow
4.5. Upon completion of the pipeline, the NGSQC_outfile will be green. Click on the “Eye” icon to view the output in the GalaxyTrakr window.

   

SECTION: Interpret the results
5. Download and interpret the results:
SECTION: Interpret the results
5.1. Click “NGSQC_outfile” (boxed in red) and then the floppy disc save icon (boxed in blue). The tabular file can be opened in a text reader or converted to a format that can be opened on excel.

   

SECTION: Interpret the results
5.2.  The SSQuAWK output file includes the following metrics:

Parameter Input Description Contigs Assembly Number of contigs in the de-novo SKESA assembly. Contigs smaller than 200 base-pairs (bp) are not counted.  Length Assembly Total length of all contigs > 200bp. This should approximate the size of the genome for the target organism. EstCov Assembly Mean coverage for contigs in the SKESA assembly. N50 Assembly Sequence length of the shortest contig at 50% of the total genome length MedianInsert Read Distance between forward and reverse reads. Calculated by mapping reads to SKESA assembly using bwa. MeanLength_R1 Read Mean length of forward read MeanLength_R2 Read Mean length of reverse read MeanQ_R1 Read Mean Q-score of forward read MeanQ_R2 Read Mean Q-score of reverse read Scheme Assembly PubMLST (pubmlst.org) database scheme (e.g. senterica for Salmonella enterica) ST Assembly Sequence Type  Loci Assembly gene (allele number) – for example aroC(118) 

SECTION: Interpret the results
5.3. Example output for 3 pairs run through the SSQuAWK workflow:
 
                                    File Contigs Length EstCov N50 MedianInsert MeanLength_R1 MeanLength_R2 MeanQ_R1 MeanQ_R2 Scheme ST                 FSL-R9-8346_S75_R_001.fastq.fasta 14 2876874 87.8 512255 408 147.6 147.7 33.1 32.7 lmonocytogenes 389 abcZ(52) bglA(1) cat(12) dapE(71) dat(2) ldh(1) lhkA(5)   FSL-R9-8348_S76_R_001.fastq.fasta 11 2832172 84.2 1464158 388 147.9 147.9 33.2 32.9 lmonocytogenes 795 abcZ(7) bglA(10) cat(18) dapE(6) dat(5) ldh(7) lhkA(1)   FSL-R9-8350_S78_R_001.fastq.fasta 14 2884629 64.2 450082 390 147.2 147.2 33.1 32.7 lmonocytogenes 37 abcZ(5) bglA(7) cat(3) dapE(5) dat(1) ldh(8) lhkA(6)   FSL-R9-8352_S79_R_001.fastq.fasta 12 2902520 85.9 1460419 390 148.1 148.2 33.1 32.8 lmonocytogenes 391 abcZ(7) bglA(6) cat(62) dapE(28) dat(5) ldh(2) lhkA(1)