SARS-CoV-2-sequence-analysis-tutorial-using-Galaxy

The aim of this project is to create a tutorial on SARS-CoV-2 sequence analysis using Galaxy and ARTIC protocol Illumina sequencing data.

For some background, the US CDC COVID-19 Genomic Epidemiology Toolkit provides videos on "What is genomic epidemiology?" and "The SARS-CoV-2 genome". SARS-CoV-2 is the virus that causes the disease COVID-19. The ARTIC sequencing protocol is described in this video by Dr Josh Quick, taken from the CLIMB-BIG-DATA / ARTICnetwork workshop in January 2021.

Galaxy, Galaxy Training Network and the Galaxy Training materials

Galaxy is a web based environment for bioinformatics. The Galaxy Training Network (GTN) is an informal collaboration of trainers who use Galaxy and contribute to the Galaxy Training materials.

For an introduction to Galaxy, consult the intro material.

Contributing to the Galaxy Training tutorials

The Galaxy Training website is built from a GitHub repository using the Jekyll site builder. There are some guides on to how to write tutorials and contribute. Contributions can either be added via the command line (with a local copy of the training materials) or via the Github web interface.

You will need:

1. A GitHub account

2. A fork of the Galaxy training material repository https://github.com/galaxyproject/training-material. This will be provided as part of the hackathon, don't make your own fork for now.

3. Either a local copy of the fork or a copy opened with gitpod.io. a. To make a local copy use git clone. Warning: This is a 2 GB download. You can reduce that size to about 1 GB by doing a shallow clone, i.e. git clone --depth=1. b. Using gitpod will be explained.

There is an existing tutorial, From NCBI's Sequence Read Archive (SRA) to Galaxy: SARS-CoV-2 variant analysis that we can use for inspiration. The source for that material is here.

Hands-on experience with Galaxy

(the checkboxes are for when the content for the tutorial is (1) written in draft and (2) reviewed and considered complete)

1. First you need to register with galaxy.sanbi.za or usegalaxy.eu if you dont't have an account or login if you already have an account.

2. ☑ ☐ To get data click on the Shared Data icon --> Data Libraries then select SARS-CoV-2 Amplicon Sequencing i. Export the SARS-CoV-2 Amplicon Sequencing to the History as collection. ii. Make FASTQ datasets into list of pairs and enter the name of your new collection (your prefered name)

3. ☑ ☐ Trimming the FASTQ reads with fastp tool. Search for this tool in the search tools icon of Galaxy.

   • input is Paired collection of the given name above (step 2 ii)

4. ☑ ☐ Mapping of your reads with BWA MEM. Select this tool in the search tools icon.

   • Input is the the output of fastp above (step 3)
   • Reference genome select Use a genome from history and build index from the drop down arrow
   • Select paired collection from the Single or Paired-end reads drop down arrow
   • Select the first and second set of reads appropriately

5. ☑ ☐ Compute mapping statistics using samtools stats. Search for this tool in the search tools icon of Galaxy.

6. ☑ ☐ Trimming the aligned BAM reads with ivar trim. Search for this tool in the search tools icon of Galaxy.

   • Input is bam dataset
   • Select Built-in from the Source of primer information
   • Select SARS-CoV-2-ARTICv3 from the Primer scheme name
   • Select Yes for Include reads not ending in any primer binding sites?

7. ☑ ☐ Variant annotation with ivar variants

   • Enter Quality score (FASTQ quality) of 20
   • Enter Frequency threshold of 0.7, good for targeting common variants.

8. ☑ ☐ Convert the iVar tabular varaiants output to a .vcf file format with iVar Variants to VCF tool.

9. ☑ ☐ Rename the reference sequence with Text transformation with sed

   • Paste this /^[^#]/s/^[^\t]+\t/NC_045512.2\t/ sed code in the SED Program window.

10. ☑ ☐ Annotate the SARS-CoV-2 variants with the SnpEff eff tool. Make sure you use the SARS-CoV-2 version

    • Make sure you select your reference sequence as named above (step 8)

11. ☑ ☐ Call consensus from the aligned BAM file with ivar consensus.

    • input is the trimmed BAM reads.
    • Enter 20 for Minimum quality score threshold
    • Enter 0.7 for Minimum frequency threshold
    • Enter 20 for Minimum depth
    • Select No for Exclude regions with smaller depth than the minimum threshold
    • Select Yes for Use N instead of - for regions with less than minimum coverage

12. ☑ ☐ Rename the FASTA header with Text transformation with sed to be the name of the sample.

    • Paste this /^>/s/Consensus_(.)threshold./\1/ sed code in the SED Program window.

13. ☑ ☐ Phylogenetic lineage assignment with Pangolin.

    • Leave the Download the latest Pangolin from web option as default for the pangoLEARN source section
    • Rerun the Pangolin with UShER model selected as Yes. UShER model is a new model for lineage identification

14. ☑ ☐ Phylogenetic lineage designation with Nextclade.

Find the link to the tutorial here

Note: When you excute your run take note of the following colors:

color	Description
Grey	waiting job
Yellow	running
Red	error
Green	completed

See the schematic illustration of the above steps.

Members

• Peter van Heusden
• Evans Mudibo
• Jesse A Asimeng
• Bright Asante
• Brian Bwanya