The course aims to provide an introduction to the current state of RNA sequencing data analyses. Methods and applications will be presented by internationally renowned guest speakers in the mornings and hands-on training on the latest computational approaches will follow in the afternoons.
During the first day, we will introduce you to the linux environment and the statistical programming language R and BioConductor software packages for biological high-throughput data. The basics of R scripting will be practiced. During the following four days, we will cover the complete work-flow of RNA-seq analysis and also focus on special applications such as CLIP-seq.
Computers for hands-on exercises will be provided along with demo data sets.
This course was developed for PhD students with a background in biology and related fields. Preference will be given to PhD students who are applying or planning to apply high throughput sequencing technologies and bioinformatics methods in their research. Basic linux and scripting skills (e.g. in R) are beneficial, but not mandatory to attend the course.
The R/Bioconductor environment for statistical data analyses and graphics
Short read sequence alignment
Normalization and data reformatting
Selecting differentially regulated genes
Selecting alternative splicing events
Identification of protein-bound RNAs
Biological interpretation and visualization
After this course you should be able to:
understand the advantages and limitations of high-throughput RNA sequencing
assess the quality of your datasets
apply appropriate short read alignment algorithms
quantify differential expression from RNA-seq datasets
summarize results in tables and figures
know your way around in R/Bioconductor to analyse your own dataset