TITLE: Wastewater QC workflow in GalaxyTrakr (SSQuAWK4)
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 CFSAN's custom Galaxy instance (GalaxyTrakr) 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 and reference files
4. Execute the SSQuAWK workflow
5. Interpret the results

Protocol and SSQuAWK workflow version history:
Protocol V8 SSQuAWK version 4: Protocol version 8 has minor text corrections. The SSQuAWK version 4 workflow required minor edits on an updated backend system of GalaxyTrakr, and thus is now labeled as "SSQuAWK4.0.2" in GalaxyTrakr. All SSQuAWK4 protocol steps listed in this guide are applicable to SSQuAWK4.0.2.
Protocol V7 SSQuAWK version 4: Protocol now includes a QC determination guidance table. The SSQuAWK version 4 workflow required minor bug edits on the backend, thus is now labeled as "SSQuAWK4.0.1" in GalaxyTrakr. All SSQuAWK4 protocol steps listed in this guide are applicable to SSQuAWK4.0.1.
Protocol V6 SSQuAWK version 4: Best practice guidance on fastq.gz file uploads and new QC metric.
Protocol V5 SSQuAWK version 3: Previous protocol version had broken links for FASTA and BED files, this version fixes the links. 
Protocol V4 SSQuAWK version 3: Metrics now reported with fewer softwares, fewer underlying GalaxyTrakr jobs, and about 50% fewer underlying GalaxyTrakr steps. Cleaner output table formats now include QC placeholder columns for SRA metadata template.
Protocol V3, SSQuAWK version 2: Addition of 5 new genome mapping metrics
Protocol V2, SSQuAWK version 1: Addition of a detailed 12 minute video tutorial
Protocol V1, SSQuAWK version 1: Basic protocol steps with screenshots

Participation was supported by the: 
Research Participation Program at the U.S. Food and Drug Administration administered by the Oak Ridge Institute for Science and Education (ORISE) through an interagency agreement between the U.S. Department of Energy and the U.S. Food and Drug Administration. 
Joint Institute for Food Safety and Applied Nutrition (JIFSAN), University of Maryland by the Food and Drug Administration (FDA) of the U.S. Department of Health and Human Services (HHS).
The American Rescue Plan Act of 2021, Congress provided temporary funding for FDA to develop the capacity to sequence SARS-CoV-2 RNA from wastewater samples and to conduct a sampling and sequencing project through 2022.

[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 sequence set with details and date in the history name. 

Save your SSQuAWK output here with any other relevant analyses. 

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 Sequence 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 Sequence 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 highlighted in green. These fastq.gz files should be paired (two per sample).


  

SECTION: Upload Sequence Data
3.2. Before initiating the file upload, double check that the file "Type" is appropriately set.

Do not use the "Auto - Detect" option. 

Sequence data uploaded using the "auto - detect" option may be subject to file corruption. 

This can result in various downstream analysis issues, including empty output metrics and job errors.

  
Instead, use the "Type (set all)" dropdown to select the correct file type.

  
Once the file type is set, press "Start" to initiate your data upload to GalaxyTrakr. The "Start" button is circled in green.

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

SECTION: Upload Sequence Data
3.4. 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 Sequence Data
3.5. 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 Sequence Data
3.6. 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 Sequence Data
3.7. 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: Upload reference data
4. To the existing history, also upload (1) the provided reference.fasta file and (1) a primer.bed file. 

 

 
  
  

SECTION: Upload reference data
4.1. SSQuAWK is only compatible with the 22903 nt reference genome file obtained from NCBI 'NC_045512.2'. It is provided here for your convenience:


  

SECTION: Upload reference data
4.2. The primer.bed file should correspond to the SARS - CoV - 2 enrichment primer panel kit used.


QIAseq Direct kit:   
SNAP standard kit:   
SNAP additional coverage kit:   
NEB VarSkip Short (version 1) kit:   
NEB VarSkip Short (version 2) kit:   
ARTIC v4 primer schemes:   

SECTION: Run the SSQuAWK workflow
5. Access the SSQuAWK4.0.2* workflow with the "workflows" panel.  

*SSQuAWK4.0.1: SARS - CoV - 2 Sequence Quality Assurance Workflow Kontraption, version 4.0.2

SECTION: Run the SSQuAWK workflow
5.1. Navigate to the “Shared Data" drop down and choose workflows 




  






Then, from the SSQuaWK4.0.1 drop down menu, select "Run".

 
 
 
  
 
  


SECTION: Run the SSQuAWK workflow
5.2. Select the paired list you created earlier by selecting the folder icon (boxed in red), and then the list of pairs (boxed in green).

Boxed in gold: Select the reference fasta file from your history.

Boxed in blue: Select the bed file from your history.

Click Run Workflow (boxed in purple). 

 

 
 
  


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 SSQuAWK workflow
5.3. Upon completion of the pipeline, the output file for SSQuAWK4.0.2 will be green. Click on the “Eye” icon to view in GalaxyTrakr window.



   
  
  

SECTION: Interpret the results
6. Download and interpret the results:
SECTION: Interpret the results
6.1. Click the output file text for "SSQuAWK4.0.2" (circled in red) and then the floppy disc save icon (circled in blue). The tabular file can be opened in a text reader or converted to a format that can be opened in Excel.

  


 
   

SECTION: Interpret the results
6.2.  The SSQuAWK4.0.1 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
6.3. What is nucleotide coverage?! Let's look at 2 simple pictures

  

 
SECTION: Interpret the results
6.4. Example output for the first 3 pairs run through the SSQuAWK4.0.2 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)    

SECTION: Interpret the results
6.5. QC metric guidance for QC attributes on SRA metadata

 

SECTION: Video Tutorial
7. Thanks for using SSQuAWK!