Workshop on high-throughput sequencing data analysis with Galaxy
Date: 10 - 14 March 2025
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
Activity log