Detection of AMR genes in bacterial genomes

PURL: https://gxy.io/GTN:P00018
Comment: What is a Learning Pathway?
A graphic depicting a winding path from a start symbol to a trophy, with tutorials along the way
We recommend you follow the tutorials in the order presented on this page. They have been selected to fit together and build up your knowledge step by step. If a lesson has both slides and a tutorial, we recommend you start with the slides, then proceed with the tutorial.

This learning path aims to teach you the basic steps to detect and check Antimicrobial resistance (AMR) genes in bacterial genomes using Galaxy.

Module: Species and contamination checking

Quality control and taxonomic assignation is useful in AMR detection to verify the quality of the data but also to check contamination and confirm species

Time estimation: 2 hours

Learning Objectives
  • Run tools to evaluate sequencing data on quality and quantity
  • Evaluate the output of quality control tools
  • Improve the quality of sequencing data
  • Run a series of tool to identify species in bacterial isolate sequencing data
  • Visualize the species abundance
Lesson Slides Hands-on Recordings
Quality and contamination control in bacterial isolate using Illumina MiSeq Data
illumina bacteria microgalaxy

Module: Assembly

Assembly is a major step in the process of detecting AMR genes as it combines sequenced reads into contigs, longer sequences where it will be easier to identify genes and in particular AMR genes

Time estimation: 4 hours

Learning Objectives
  • Run tools to evaluate sequencing data on quality and quantity
  • Process the output of quality control tools
  • Improve the quality of sequencing data
  • Run a tool to assemble a bacterial genome using short reads
  • Run tools to assess the quality of an assembly
  • Understand the outputs of tools to assess the quality of an assembly
  • Run tools to evaluate sequencing data on quality and quantity
  • Process the output of quality control tools
  • Improve the quality of sequencing data
  • Run a tool to assemble a bacterial genome using short reads
  • Run tools to assess the quality of an assembly
  • Understand the outputs of tools to assess the quality of an assembly
Lesson Slides Hands-on Recordings
Genome Assembly of a bacterial genome (MRSA) sequenced using Illumina MiSeq Data
illumina assembly microgalaxy
Genome Assembly of MRSA from Oxford Nanopore MinION data (and optionally Illumina data)
nanopore assembly amr microgalaxy

Module: Genome annotation

The generated contigs can be annotated to detect genes, potential plasmids, etc. This will help the AMR gene detection process, especially the verification and visualization

Time estimation: 3 hours

Learning Objectives
  • Run a series of tool to annotate a draft bacterial genome for different types of genomic components
  • Evaluate the annotation
  • Process the outputs to formate them for visualization needs
  • Visualize a draft bacterial genome and its annotations
Lesson Slides Hands-on Recordings
Bacterial Genome Annotation
gmod illumina bacteria microgalaxy jbrowse1

Module: AMR gene detection

AMR gene content can be assessed from the contigs to detect known resistance mechanisms and potentially identify novel mechanisms.

Time estimation: 2 hours

Learning Objectives
  • Run a series of tool to assess the presence of antimicrobial resistance genes (ARG)
  • Get information about ARGs
  • Visualize the ARGs and plasmid genes in their genomic context
Lesson Slides Hands-on Recordings
Identification of AMR genes in an assembled bacterial genome
gmod illumina amr one-health jbrowse1 microgalaxy

Time estimation: 4 hours

Learning Objectives
  • Check quality reports generated by FastQC and NanoPlot for metagenomics Nanopore data
  • Preprocess the sequencing data to remove adapters, poor quality base content and host/contaminating reads
  • Perform taxonomy profiling indicating and visualizing up to species level in the samples
  • Identify pathogens based on the found virulence factor gene products via assembly, identify strains and indicate all antimicrobial resistance genes in samples
  • Identify pathogens via SNP calling and build the consensus gemone of the samples
  • Relate all samples' pathogenic genes for tracking pathogens via phylogenetic trees and heatmaps
Lesson Slides Hands-on Recordings
Pathogen detection from (direct Nanopore) sequencing data using Galaxy - Foodborne Edition
microgalaxy Nanopore data analysis Pathogens detection Phylogenetic tree Heatmap cyoa

Editorial Board

This material is reviewed by our Editorial Board:

orcid logoBérénice Batut avatar Bérénice Batutorcid logoClea Siguret avatar Clea Siguret

Funding

These individuals or organisations provided funding support for the development of this resource

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ABRomics