Whole Transcriptome Data Analysis

Overview
Speakers
Programme

Course Overview

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.

Audience

This course was developed for biologists as the intended audience. Basic knowledge of massively parallel sequencing (MPS) is desirable.

Modules

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

Learning Outcomes

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