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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, particularly for those already with basic knowledge of massively parallel sequencing (MPS). However, R-coding skills and deep knowledge of statistics are not necessary for attendance.
• Tools for RNA-seq data analysis
• Experimental design
• Quality control
• Normalisation, batch effect correction and data reformatting
• Basic Statistics
• Selecting differentially regulated genes/microRNAs/non-coding RNAs
• Biological interpretation
• Transcriptome analysis in absence of genomic annotation information.
• Detecting alternative splicing events
• Single cell RNAseq data analysis
The students will learn the importance of experimental design in order to ask sensible biological questions, as well as the ability to assess the quality of data. Perform normalisation and reformatting procedures. Also covered will be complete basic statistical tests on next generation sequencing (NGS) data, various problems encountered when analysing data and the basics of R scripting.