Advance your research. Understand RNA-Seq analyses challenges and solve them yourself.
In a nutshell
- Learn the essential computing skills for NGS bioinformatics
- Understand NGS analysis algorithms (e.g. read alignment) and data formats
- Use bioinformatics tools for handling RNA-Seq data
- Create diagnostic graphics and statistics
- Compare different approaches for differential expression analysis
When?
October 25 – 28, 2022
Where?
Leipzig, Germany
The purpose of this workshop is to get a deeper understanding in Next-Generation Sequencing (NGS) with a special focus on bioinformatics issues. Advantages and disadvantages of current sequencing technologies and their implications on data analysis will be discovered. You will be trained on understanding NGS data formats and handling potential problems/errors therein. In the course we will use a real-life RNA-seq dataset from the current market leader illumina.
All workshop attendees will be enabled to perform important first tasks of NGS data analysis themselves. The course layout has been adapted to the needs of beginners in the field of NGS bioinformatics and allows scientists with no or little background in computer science to get a first hands-on experience in this new and fast evolving research topic.
Read our detailed course program.
This workshop has been redesigned and adapted to the needs of beginners in the field of NGS bioinformatics and comprises this three course modules:
- Linux for Bioinformatics:
This module will introduce the essential tools and file formats required for NGS data analysis. It helps to overcome the first hurdles when entering this (for NGS analyses) unavoidable operating system. - Introduction to NGS data analysis:
Different methods of NGS will be explained, the most important notations be given and first analyses be performed. This module covers essential knowledge for analysing RNA-Seq data. - RNA-seq Data Analyses: In this module different bioinformatics tools for RNA-seq alignment will be described and tested. We then apply and compare the various approaches for differential expression analysis using RNA-Seq.
