The course participants will use open-source software. R-coding skills and deep knowledge of statistics are not necessary for attendance because most analyses will be performed with docker4seq package, which was developed to facilitate the use of computing demanding applications in the field of NGS data analysis. Docker4seq package uses docker containers that embed demanding computing tasks (e.g. short reads mapping) into isolated containers. This approach provides multiple advantages:
• user does not need to install all the software on its local server;
• results generated by different containers can be organized in pipelines;
• reproducible research is guarantee by the possibility of sharing the docker images used for the analysis.
Computers for hands-on exercises will be provided along with demo data sets.
This course was developed for biologists as the intended audience. Basic knowledge of massively parallel sequencing (MPS) is desirable.
- Tools for RNA-seq data analysis
- Experimental design
- Quality control
- Normalisation and data reformatting
- Basic statistics
- Selecting differentially regulated genes/microRNAs
- Selecting alternative splicing events
- Multiple testing
- Biological interpretation
- Understand the importance of experimental design in order to ask sensible biological questions
- Assess the quality of your data
- Perform normalisation and reformatting procedures
- Complete basic statistical tests on Next Generation Sequencing (NGS) data
- Understand some of the problems encountered when analysing data