Date: 10 - 14 March 2025

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This course introduces scientists to the data analysis platform Galaxy. The course is a beginner course; no programming skills are required.

Venue: University of Freiburg, Werthmannstrasse 4

City: Freiburg

Country: Germany

Postcode: 79104

Learning objectives:

  • Analyze the DESeq2 output to identify, annotate and visualize differentially expressed genes
  • Assess long reads FASTQ quality using Nanoplot and PycoQC
  • Assess short reads FASTQ quality using FASTQE 🧬😎 and FastQC
  • Assess the quality of a ChIP-seq experiment
  • Call enriched regions or peaks
  • Check a sequence quality report generated by FastQC for RNA-Seq data
  • Check quality reports generated by FastQC and NanoPlot for metagenomics Nanopore data
  • Construct and run a differential gene expression analysis
  • Describe the process to estimate the library strandness
  • Estimate the number of reads per genes
  • Evaluate the quality of mapping results
  • Explain the count normalization to perform before sample comparison
  • Explain the principle and specificity of mapping of RNA-Seq data to an eukaryotic reference genome
  • Extract coverage files
  • 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
  • Inspect the read quality
  • Jointly call variants and genotypes for a family trio from whole-exome sequencing data
  • Learn how histories work
  • Learn how to create a workflow
  • Learn how to obtain data from external sources
  • Learn how to run tools
  • Learn how to share your work
  • Map reads on a reference genome
  • Perform a gene ontology enrichment analysis
  • Perform and visualize an enrichment analysis for KEGG pathways
  • 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
  • Select and run a state of the art mapping tool for RNA-Seq data
  • Summarise quality metrics MultiQC
  • Trim low quality bases
  • Use variant annotation and the observed inheritance pattern of a phenotype to identify candidate causative variants and to prioritize them

Organizer: Daniela Schneider (https://training.galaxyproject.org/training-material/hall-of-fame/Sch-Da/), Teresa Müller (https://training.galaxyproject.org/training-material/hall-of-fame/teresa-m/)

Event types:

  • Workshops and courses

Sponsors: de.NBI

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


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