Keywords: introduction, microbiome, sequence-analysis, single-cell, transcriptomics

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
• Apply Kraken and MetaPhlAn to assign taxonomic labels
• Apply Krakentools to calculate α and β diversity and understand the output
• Apply Krona and Pavian to visualize results of assignment and understand the output
• Assess long reads FASTQ quality using Nanoplot and PycoQC
• Assess short reads FASTQ quality using FASTQE 🧬😎 and FastQC
• Check a sequence quality report generated by Falco/MultiQC for RNA-Seq data
• Check quality reports generated by FastQC and NanoPlot for metagenomics Nanopore data
• Construct and run a cell type annotation for the clusters
• Construct and run a differential gene expression analysis
• Construct and run a dimensionality reduction using Principal Component Analysis
• Describe an AnnData object to store single-cell data
• Describe the process to estimate the library strandness
• Estimate the number of reads per genes
• Evaluate quality of single-cell data and apply steps to select and filter cells and genes based on QC
• Evaluate the quality of mapping results
• Execute data normalization and scaling
• Explain different metrics to calculate α and β diversity
• Explain how taxonomic assignment works
• Explain the count normalization to perform before sample comparison
• Explain the preprocessing steps for single-cell data
• Explain the principle and specificity of mapping of RNA-Seq data to an eukaryotic reference genome
• Explain what taxonomic assignment is
• Explain what taxonomic diversity is
• Familiarize yourself with the basics of Galaxy
• Identify highly variable genes
• Identify marker genes for the clusters
• 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
• 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
• Perform a gene ontology enrichment analysis
• Perform a graph-based clustering for cells
• 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

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, Microbial ecology, Taxonomy, Sequence analysis, Public health and epidemiology, Sequence assembly, Pathology