2022 ASM NGS workshop

• Setup
• Part 1: SRA and Viral Resources
  • Introduction
    • Learning Objectives
    • Background Knowledge
    • Help Documentation
    • Other Resources
  • Background
    • SRA and Associated Repositories
    • Taxonomy
    • SARS-CoV-2 Variant Calling
    • NCBI Cloud Resources
  • Introductory Exercises
    • Exercise 1: Review metadata table contents and help docs
    • Exercise 2: Review taxonomy table and help docs
    • Exercise 3: Review STAT results table and help docs
    • Exercise 4: Review VCF results table and help docs
  • Project A: Find SARS2 data with paired Illumina and ONT samples, generated using ARTICv3
    • Step 1: Figuring out how to implement each filter individually
      • Query 1: Filter by Organism
      • Query 2: Filter by Platform
      • Query 3: Find additional fields that may have primer strategy information
    • Step 2: Putting it all together
      • Query 4
    • Step 3: Grouping by collection date
      • Query 5
  • Project B: Find SARS-CoV-2 data with low reference coverage
    • Part 1: Retrieving SRA data
      • Command 1: Letting the SRAToolkit do all the work
      • Command 2: Using SRAToolkit's SDL function to locate the data and then retrieve it yourself
      • Command 3: Then we download a file and extract the fastq from one of those locations.
    • Part 2: Retrieving a reference genome sequence
      • Command 4: To retrieve the reference genome as a fasta, we can use edirect.
    • Part 3: Writing bash scripts to calculate and plot reference genome coverage
      • Part 3a: A bash script to calculate reference coverage, using minimap2 and samtools.
        • Command 5: First create a file for the coverage calculation script
        • Command 6: Then copy and paste the following code into the file
      • Part 3b: Create a Coverage Plot script.
        • Command 7: Create a file for the coverage plot script.
        • Command 8: Then copy and paste the following code into the file
    • Part 4: Analyzing your SRA records
      • Command 9: Now analyze the two SRA records
  • Project C: Find variant calls that are common between paired Illumina and ONT SARS-CoV-2 records
    • Step 1: Different approaches to filtering by mutation
      • Query 1: Filtering by genomic position in nucleotide space
      • Query 2: Filtering by mutation types
      • Query 3: Filtering by associated protein
      • Query 4: Filtering by read support
    • Step 2: Combine what's been learned in Projects A, B and C thus far
      • Query 5: Putting it all together
    • Step 3: Describe our resulting dataset
      • Query 6: Finding average read support per group
• Part 2: NCBI Pathogen Detection
  • Background
  • Project 1
    • Introduction
    • Background
      • Isolates browser
      • MicroBIGG-E
      • BigQuery Cloud Console
    • Exercises
      • Exercise 1
      • Exercise 2
  • Project 2 Generate tree of KPC alleles to examine evolution of size variants
    • Background
    • Step 1 Download FASTA file of all blaKPC alleles from the Reference Gene Catalog
      • Step 1a Make a working directory
      • Step 1b Download the Reference Coding Sequence from the AMRFinderPlus database
    • Step 2 Filter for sequences less than 297 amino-acids in length
      • Step 2a Download the Reference Gene Catalog table
      • Step 2b Get a list of blaKPC genes < 297 amino-acids in length
      • Step 2c Filter and reformat the FASTA file
    • Step 3 Align the sequences with Muscle muscle
    • Step 4 Infer the tree using RAxML raxml
    • Step 5 Visualize the tree in iTOL
      • Step 5a Copy and paste into iTOL
      • Step 5b Download and add iTOL annotation files
      • Step 5c Upload the annotation file to color the taxon names
    • Discussion
  • Project 3 Selection analysis on 293-aa blaKPC genes
    • Background
    • Step 1 Get a list of contigs with sequences of interest
      • Step 1a Create a working directory for this project
      • Step 1b Use BigQuery to get a list of contigs with KPC genes
      • Step 1c How many contigs are we looking at?
    • Step 2 Download contig sequences
      • Step 2a Download a subset of the sequences
      • Step 2b Copy in tarball of all contigs
    • Step 3 Cut out coding sequences
      • Step 3a Reformat FASTA contig identifiers and combine to one file
      • Step 3b Create BED file of coordinates we want to cut out
      • Step 3c Cut out coding sequence
    • Step 4 Prepare sequences for selection analysis
    • Step 5 Use RAxML to infer a tree
    • Step 6 Run FUBAR test with HyPhy
    • Discussion