Keywords: introduction, proteomics, sequence-analysis, transcriptomics

Venue: University of Graz, Mozartgasse 14

City: Graz

Country: Austria

Postcode: 8010

Learning objectives:

• Analyze the DESeq2 output to identify, annotate and visualize differentially expressed genes
• Check a sequence quality report generated by Falco/MultiQC for RNA-Seq 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
• Familiarize yourself with the basics of Galaxy
• Learn how histories work
• Learn how to create a workflow
• Learn how to extract and run a workflow
• Learn how to obtain data from external sources
• Learn how to run tools
• Learn how to share a history
• Learn how to share your work
• Learn how to upload a file
• Learn how to use MaxQuant and MSstats for the analysis of label-free shotgun (DDA) data
• Learn how to use a tool
• Learn how to view histories
• Learn how to view results
• Perform a gene ontology enrichment analysis
• Perform and visualize an enrichment analysis for KEGG pathways
• Select and run a state of the art mapping tool for RNA-Seq data
• To comprehend the diversity of LC-MS metabolomic data analysis.
• To evaluate the potential of a workflow approach when dealing with LC-MS metabolomic data.
• To get familiar with the main steps constituting a metabolomic workflow for untargeted LC-MS analysis.

Organizer: Sebastian Preissl (https://training.galaxyproject.org/training-material/hall-of-fame/sebastian-preissl/)

Event types:

• Workshops and courses

Sponsors: German Network for Bioinformatics Infrastructure Service, Training, Cooperations & Cloud Computing, Ministry of Science, Research and Arts, University of Graz

Instructors: Pavankumar Videm, Amirhossein Naghsh Nilchi