- Learn the essential computing skills for NGS bioinformatics
- Understand NGS analysis algorithms (e.g. read alignment) and data formats
- Use bioinformatics tools for handling NGS data
- Perform first downstream analyses for studying genetic variation
- Compare different approaches for differential expression analysis
The purpose of this intense one week summer course is to get a deep 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.
All students will be enabled to perform important first tasks of NGS data analysis themselves. The layout of the summer school has been adapted to the needs of absolute 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.
In the evenings there will be social events, like a conference dinner, or a guided city tour through Berlin. These are always great networking possibilities for the participants.
Read our detailed course program.
- 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 course covers essential knowledge for analysing data of many different NGS applications.
- DNA Variant Calling: In this module different bioinformatics tools for variant calling will be described. We then apply various methods for variant calling and filtering using DNA-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.