This course will provide a thorough introduction to the application of metabarcoding techniques in microbial ecology. The topics covered by the course range from bioinformatic processing of next-generation sequencing data to the most important approaches in multivariate statistics. Using a combination of theoretical lectures and hands-on exercises, the participants will learn the most important computational steps of a metabarcoding study from the processing of raw sequencing reads down to the final statistical evaluations. After completing the course, the participants should be able to understand the potential and limitations of metabarcoding techniques as well as to process their own datasets to answer the questions under investigation.
This course is designed for researchers and students with strong interests in applying novel high-throughput DNA sequencing technologies to answer questions in the area of community ecology and biodiversity. The course will mainly focus on the analysis of phylogenetic markers to study bacterial, archaeal and fungal assemblages in the environment, but the theoretical concepts and computational procedures can be equally applied to any taxonomic group or gene of interest.
The participants should have some basic background in biology and understand the central role of DNA for biodiversity studies. No programming or scripting expertise is required and some basic introduction to UNIX-based command line applications will be provided on the first day. However, some basic experience with using command line and/or R is clearly an advantage as not all the basics can be thoroughly covered in that short amount of time. All hands-on exercises will be run in UNIX-environments (Linux, Mac) on remote servers. Statistical analyses will be run in R using RStudio.
1) Understanding the concept, potential and limitation of microbial metabarcoding techniques.
2) Learning how to process raw sequencing reads to obtain meaningful information.
3) Obtaining experience on how to statistically evaluate and visualize your data.
4) Being able to make informed decisions on best practices for your own data.