Keywords: beginner, data-science, galaxy-interface, microbiome, variant-analysis, visualisation

Learning objectives:

• Analyze and preprocess Nanopore reads
• Apply the ML techniques to analyse their own datasets
• Be able to write simple shell scripts for running multiple workflows concurrently or sequentially.
• Build complex and customized plots from data in a data frame.
• Check quality reports generated by FastQC and NanoPlot for metagenomics Nanopore data
• Create a number of Circos plots using the Galaxy tool
• Create clean, non-repetitive workflows
• Describe what faceting is and apply faceting in ggplot.
• Familiarise yourself with the various different track types
• Filter, annotate and report lists of variants
• 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 yeast species contained in a sequenced beer sample using DNA
• Inspect metagenomics data
• Interpret and visualize the results obtained from ML analyses on omics datasets
• Learn about SRA aligned read format and vcf files for Runs containing SARS-CoV-2 content
• Learn about the Rule Based Uploader
• Learn even more about the Rule Based Uploader
• Learn how to change a workflow using the workflow editor
• Learn how to extract a workflow from a Galaxy history
• Learn how to use Pangolin to assign annotated variants to lineages.
• Learn how to use Workflow Parameters to improve your Workflows
• Learn to use the planemo run subcommand to run workflows from the command line.
• Modify the aesthetics of an existing ggplot plot (including axis labels and color).
• Perform taxonomy profiling indicating and visualizing up to species level in the samples
• Perform variant linkage analyses for phenotypically selected recombinant progeny
• Plot an E. coli genome in Galaxy
• Preprocess the sequencing data to remove adapters, poor quality base content and host/contaminating reads
• Produce scatter plots, boxplots, and time series plots using ggplot.
• Relate all samples' pathogenic genes for tracking pathogens via phylogenetic trees and heatmaps
• Run metagenomics tools
• Set universal plot settings.
• Understand and master dataset collections
• Understand differences between ML algorithms categories and to which kind of problem they can be applied
• Understand different applications of ML in different -omics studies
• Understand how to search the metadata for these Runs to find your dataset of interest and then import that data in your preferred format
• Understand key aspects of workflows
• Understand the ML taxonomy and the commonly used machine learning algorithms for analysing -omics data
• Use Kraken2 to assign a taxonomic labels
• Use Nanopore data for studying soil metagenomics
• Use joint variant calling and extraction to facilitate variant comparison across samples
• Use some basic, widely used R packages for ML
• Use the scientific library matplolib to explore tabular datasets
• Visualize the microbiome community of a beer sample
• With tracks for the annotations, sequencing data, and variants.

Event types:

• Workshops and courses

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