TITLE: Quality control assessment for microbial genomes: GalaxyTrakr MicroRunQC workflow
AUTHORS: Ruth Timme, Yesha Shrestha, Tina.Pfefer, Paul Morin, Maria  Balkey, Errol Strain

[DESCRIPTION]
PURPOSE: Step-by-step instructions for checking WGS sequence quality for bacterial pathogens. The MicroRunQC workflow, implemented in a custom Galaxy instance, will produce quality assessments for raw reads (Illumina paired-end fastq files) and draft de novo assemblies, along with reporting the sequence type for each isolate. This workflow will work on most microbial pathogens, so we advise laboratories to upload their entire MiSeq/NextSeq run through this workflow.   

SCOPE: This protocol covers the following tasks:

1. set up an account in GalaxyTrakr
2. Create a new history/workspace
3. Upload data
4. Execute the MicroRunQC workflow
5. Interpret the results

Version updates:
V3: updated with Cronobacter thresholds
V4: MicroRunQC updated to V1.1 Includes updates to skeza and mlst methods, as well as adjusted assembly QC thresholds for E.coli. Added Enterobacter QC thresholds to threshold table.

[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. Click on the + icon in the upper right History panel

  
SECTION: Create a new history
2.2. Name your new History by clicking on the “Unnamed history”, type in desired name and hit enter.  We recommend including the run cell ID and the date the run was started.

  
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. Click on the Upload Data icon on the top of the left web page to start an upload process.
   

SECTION: Upload data
3.2. Select "Type (set all):auto-detect." Choose local file button and navigate to the desired fastq files, then click "start" to upload files. These files should be paired (two per sample/isolate).
   
As the file uploads complete, each row will turn green. Samples in yellow are still in process.
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/isolate.  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. Click "For all selected" and choose "Build List of Dataset Pairs"

   

SECTION: Upload data
3.5. A new window will open to help you pair the fastq files properly.  Note how your paired reads are named.

Select Clear filters, then click Auto-pair.

   
If auto-pairing does not work, you can click "choose filters" and  select the appropriate filter for the pairing:

e.g. choose "_R1 "and "_R2 "

  
SECTION: Upload data
3.6. Paired reads will pair in the middle column and turn green.

Name your dataset:  Example, "pairedSet-<FlowCell>-<date>"

Click Create list.

  
SECTION: Upload 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.

You can re-name this PairedList by clicking on the name.

   
SECTION: Run the MicroRunQC workflow
4. Add the MicroRunQC workflow to your own "workflows" panel.  You only have to do this step once for each new workflow you need.

SECTION: Run the MicroRunQC workflow
4.1. Navigate to the “Shared Data" drop down menu, choose workflows and locate MicroRunQC_v1.1

 
From Dropdown, select "Import"

   
SECTION: Run the MicroRunQC workflow
4.2. To see the new imported workflow, click “Workflow” tab on the top panel. 

Click "Bookmarked" box to make it available in the left panel under "Workflows"

  
SECTION: Run the MicroRunQC workflow
4.3. From the Workflow menu on the left panel, select MicroRunQC_v1.1

   
SECTION: Run the MicroRunQC workflow
4.4. Select paired list dataset you created earlier.

Click Run Workflow. This can take some time depending on the number of samples you are analyzing. If you choose to you can log out of GalaxyTrakr and log back in at a later time to see if the job is completed.

   
SECTION: Run the MicroRunQC workflow
4.5. Upon completion of the pipeline all tiles in the history bar will be green.

In the “Filter on Data ##”, click on the “Eye” icon to view the output table in the GalaxyTrakr window.

   

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

  
SECTION: Interpret the results
5.2.  The MicroRunQC output file includes the following columns:

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) 
 
**This output should be saved either to your LIMS or to a spreadsheet linked to the sequencing run and samples.
SECTION: Interpret the results
5.3. Example output for 1 Salmonella and 5 Listeria isolates. 
 
rain IDLab ConfirmationSchemeSTFDA1216271-C001-001Listeria monolisteria_25FDA817806-S073-001Listeria monolisteria_2321FDA746634Listeria monolisteria_2-FDA1213377-C001-002Listeria grayi--FDA933376-S060-005Listeria innocualisteria_21489FDA1213835-C001-001Salmonellasenterica_achtman_2214
 
 
                File  Contigs  Length  EstCov  N50  Median Insert  Mean Length_R1  Mean Length_R2  Mean Q_R1  Mean Q_R2  Scheme  ST                         FDA1216271-C001-001  16  2911949  36.7  476210  321  148.4  148.4  36.4  34.6  listeria_2  5  abcZ(2)  bglA(1)  cat(11)  dapE(3)  dat(3)  ldh(1)  lhkA(7)    FDA817806-S073-001  20  3068354  179.6  525438  329  234.7  235.2  36.7  31.9  listeria_2  321  abcZ(5)  bglA(6)  cat(8)  dapE(62)  dat(6)  ldh(7)  lhkA(34)    FDA746634  30  3052888  41.4  293947  320  148.4  148.4  36.5  36  listeria_2  -  abcZ(2)  bglA(1)  cat(11)  dapE(3)  dat(3)  ldh(1)  lhkA(~7)    FDA1213377-C001-002  20  2672180  155.1  473181  270  147.3  147.3  37.2  36.1  -  -                         FDA933376-S060-005  9  2881869  213  1498790  303  232.1  232.2  37  36.2  listeria_2  1489  abcZ(250)  bglA(21)  cat(83)  dapE(298)  dat(20)  ldh(458)  lhkA(216)    FDA1213835-C001-001  37  4832365  34.4  294936  354  149  149  36.6  35.7  senterica_achtman_2  214  aroC(14)  dnaN(72)  hemD(21)  hisD(12)  purE(6)  sucA(19)  thrA(15)  
 
The listeria database includes multiple species, including Listeria monocytogenes and L. innocua.  If users want to investigate which Listeria DB corresponds to the resulting ST types, they can query the Institut Pasteur mlst database: 

Example: query Listeria ST type here: https://bigsdb.pasteur.fr/cgi-bin/bigsdb/bigsdb.pl?db=pubmlst_listeria_seqdef&page=query
SECTION: Interpret the results
5.4. Quality control threshold guidelines for the GenomeTrakr surveillance network. These are also relevant for NARMS and VetLIRN contributors. 

*MicroRunQC users should follow QC threshold guidelines established by their respective surveillance coordinating body(s).