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Pipeline Overview

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Jill V. Hagey, PhD edited this page May 26, 2022 · 92 revisions

Pipeline Summary:

Pipeline Workflow

QC

  1. PhiX174 read removal and adapter removal using BBDuK
  2. Filtering, trimming, and base correction using fastp that includes:
    • quality trimming with a window size of 20 and quality of 30
    • quality pruning at 3' and 5' ends
    • removal of short reads
    • forced polyG tail trimming
  3. Contamination check of trimmed reads using Kraken2 and the MiniKraken2_v2_8GB.

Analysis of Trimmed Reads

  1. QC Metrics Generated (all data generated for paired and unpaired reads generated post-trimming):
  • Number of total reads/bases
  • Percent of reads/bases remaining (from raw sequences)
  • Number of Q20/Q30 bases
  • Percent Q20/Q30 bases

Analysis Using Trimmed Reads

  1. Gene detection and allele calling for antibiotic resistance srst2 in gene mode. DHQP has curated an AR gene database that is a combination of three AR gene databases with redundancies removed and gene names standardized.
  1. (optional) Sequence types determined based on MLST alleles and profile definitions using srst2 in MLST mode. The MLST schemes are downloaded from PubMLST with the getMLST.py script from srst2. Which MLST is download based on the organism ID that was determined by FastANI/Kraken2.
  • NOTES:
    • This step is optional and is only run if the MLST step (see below) fails.
    • The getMLST.py script from srst2 was edited to correct and error.

Assembly

  1. Assembly of trimmed reads using SPAdes
  2. Filter reads to remove any contigs less than 500bp in length.

QC of Assembled Reads >= 500bps

  1. Assess assembly quality using QUAST and custom scripts
  2. QC Metrics Generated:
  • Raw coverage (total raw bases / assembly length)
  • Trimmed coverage (total trimmed bases / assembly length)
  • Assembly ratio (assembly size / median genome size of species)

Analysis of Assembled Reads >= 500bps

  1. Assess assembly quality using QUAST
  2. Measure the average nucleotide identity (between genomes) using fastANI to determine species
  3. Type multiple loci to characterized isolates of microbial species using MLST
  4. AR genes, hypervirulence genes and plasmids are detected with GAMMA. DHQP has curated an AR gene database that is a combination of three AR gene databases with redundancies removed and gene names standardized.

Also, the additional databases are used:

  1. Rapid whole genome annotation using PROKKA
  2. (optional) If fastANI (step 7) fails to identify the taxa present Kraken2 is run on the assembled reads using the MiniKraken2_v2_8GB.

Analysis Using Annotated Genes

  1. Assess genome assembly for completeness using BUSCO

DISCLAIMER: The identification methods used and the data summarized are for public health surveillance or investigational purposes only and must NOT be communicated to the patient, their care provider, or placed in the patient’s medical record. These results should NOT be used for diagnosis, treatment, or assessment of individual patient health or management.

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