Date: No date given

This learning path aims to teach you the basics of Galaxy and analysis of metagenomics data.
You will learn how to use Galaxy for analysis, and will be guided through the common
steps of microbiome data analysis: quality control, taxonomic profiling, taxonomic binning, assembly, functional profiling, and also some applications

Keywords: microbiome

Learning objectives:

  • Apply Kraken and MetaPhlAn to assign taxonomic labels
  • Apply Krona and Pavian to visualize results of assignment and understand the output
  • Apply appropriate tools for analyzing the quality of metagenomic assembly
  • Apply appropriate tools for analyzing the quality of metagenomic data
  • Assess long reads FASTQ quality using Nanoplot and PycoQC
  • Assess short reads FASTQ quality using FASTQE 🧬😎 and FastQC
  • Binning of contigs into metagenome-assembled genomes (MAGs) using MetaBAT 2 software
  • Check quality reports generated by FastQC and NanoPlot for metagenomics Nanopore data
  • Choose the best approach to analyze metatranscriptomics data
  • Construct and apply simple assembly pipelines on short read data
  • Describe common problems in metagenomics binning
  • Describe what an assembly is
  • Describe what de-replication is
  • Describe what metagenomics binning is
  • Evaluate the Quality of the Assembly with Quast, Bowtie2, and CoverM-Genome
  • Evaluation of MAG quality and completeness using CheckM software
  • Explain how taxonomic assignment works
  • Explain how tools based on De Bruijn graph work
  • Explain the difference between co-assembly and individual assembly
  • Explain the difference between reads, contigs and scaffolds
  • Explain what taxonomic assignment is
  • Familiarize yourself with the basics of Galaxy
  • 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
  • Identify taxonomic classification tool that fits best depending on their data
  • Identify yeast species contained in a sequenced beer sample using DNA
  • Inspect metagenomics data
  • Learn how histories work
  • Learn how to create a workflow
  • Learn how to extract and run a workflow
  • Learn how to obtain data from external sources
  • Learn how to run tools
  • Learn how to share a history
  • Learn how to share your work
  • Learn how to upload a file
  • Learn how to use a tool
  • Learn how to view histories
  • Learn how to view results
  • Perform quality correction with Cutadapt (short reads)
  • Perform taxonomy profiling indicating and visualizing up to species level in the samples
  • Preprocess the sequencing data to remove adapters, poor quality base content and host/contaminating reads
  • Process single-end and paired-end data
  • Relate all samples' pathogenic genes for tracking pathogens via phylogenetic trees and heatmaps
  • Run metagenomics tools
  • Summarise quality metrics MultiQC
  • Understand the functional microbiome characterization using metatranscriptomic results
  • Understand where metatranscriptomics fits in 'multi-omic' analysis of microbiomes
  • Visualise a community structure
  • Visualize the microbiome community of a beer sample
  • What software tools are available for metagenomics binning

Event types:

  • Workshops and courses

Sponsors: Australian BioCommons, ELIXIR Europe, Erasmus Medical Center, The Pennsylvania State University, University of Freiburg, de.NBI

Scientific topics: Metagenomics, Sequence assembly, Microbial ecology, Taxonomy, Sequence analysis, Metatranscriptomics, Function analysis, Public health and epidemiology, Pathology


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