Keywords: epigenetics, introduction, microbiome, sequence-analysis, transcriptomics, variant-analysis

Venue: University of Freiburg, Werthmannstrasse 4

City: Freiburg

Country: Germany

Postcode: 79098

Learning objectives:

• Analyze the DESeq2 output to identify, annotate and visualize differentially expressed genes
• Analyze the relative abundance of microbial taxa in the samples and infer ecological dynamics.
• Assess long reads FASTQ quality using Nanoplot and PycoQC
• Assess short reads FASTQ quality using FASTQE 🧬😎 and FastQC
• Assess the effectiveness of using phyloseq for exploring and visualizing ASV data to gain ecological and evolutionary insights
• Assess the quality of MAGs and determine whether they meet standards for downstream analysis.
• Assess the quality of a ChIP-seq experiment
• Call enriched regions or peaks
• Check a sequence quality report generated by Falco/MultiQC for RNA-Seq data
• Compare the advantages of ASV-based methods over traditional OTU-based approaches in terms of accuracy and resolution
• Compare the quality of MAGs based on completeness, contamination, and other metrics.
• Construct and run a differential gene expression analysis
• Define essential terms such as MAGs (Metagenome-Assembled Genomes), binning, and functional annotation.
• Describe the process to estimate the library strandness
• Describe the purpose and process of assembling, binning, and refining MAGs.
• Estimate the number of reads per genes
• Evaluate the quality of mapping results
• Evaluate the reliability of taxonomic assignments and functional annotations based on reference databases.
• Execute the DADA2 pipeline to process raw 16S sequencing data and produce a high-resolution ASV table
• Explain the count normalization to perform before sample comparison
• Explain the importance of preprocessing metagenomic reads, including quality control and contamination removal.
• Explain the importance of quality filtering, error rate learning, and chimera removal in ensuring accurate microbial community analysis
• 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 the key steps in the DADA2 workflow for generating an ASV table from 16S rRNA gene sequencing data
• Identify the types of genomic features annotated by Bakta (e.g., CDS, rRNA, tRNA).
• Inspect the read quality
• Interpret the functional annotation results to identify metabolic pathways, virulence factors, and other biological roles.
• 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
• List the key steps involved in MAGs building from raw data.
• 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)
• Process single-end and paired-end data
• Select and run a state of the art mapping tool for RNA-Seq data
• Summarise quality metrics MultiQC
• Summarize how taxonomic assignment and functional annotation contribute to understanding microbial communities.
• 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://orcid.org/0000-0001-9536-5587)

Event types:

• Workshops and courses

Sponsors: de.NBI

Scientific topics: Microbial ecology, Taxonomy, Sequence analysis, Metabarcoding, Metagenomics, Sequence assembly